what is data analysis in the research

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Data Analysis in Research: Types & Methods

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Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Data collection

data analysis

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• Table Of Contents

data analysis , the process of systematically collecting, cleaning, transforming, describing, modeling, and interpreting data , generally employing statistical techniques. Data analysis is an important part of both scientific research and business, where demand has grown in recent years for data-driven decision making . Data analysis techniques are used to gain useful insights from datasets, which can then be used to make operational decisions or guide future research . With the rise of “Big Data,” the storage of vast quantities of data in large databases and data warehouses, there is increasing need to apply data analysis techniques to generate insights about volumes of data too large to be manipulated by instruments of low information-processing capacity.

Datasets are collections of information. Generally, data and datasets are themselves collected to help answer questions, make decisions, or otherwise inform reasoning. The rise of information technology has led to the generation of vast amounts of data of many kinds, such as text, pictures, videos, personal information, account data, and metadata, the last of which provide information about other data. It is common for apps and websites to collect data about how their products are used or about the people using their platforms. Consequently, there is vastly more data being collected today than at any other time in human history. A single business may track billions of interactions with millions of consumers at hundreds of locations with thousands of employees and any number of products. Analyzing that volume of data is generally only possible using specialized computational and statistical techniques.

The desire for businesses to make the best use of their data has led to the development of the field of business intelligence , which covers a variety of tools and techniques that allow businesses to perform data analysis on the information they collect.

For data to be analyzed, it must first be collected and stored. Raw data must be processed into a format that can be used for analysis and be cleaned so that errors and inconsistencies are minimized. Data can be stored in many ways, but one of the most useful is in a database . A database is a collection of interrelated data organized so that certain records (collections of data related to a single entity) can be retrieved on the basis of various criteria . The most familiar kind of database is the relational database , which stores data in tables with rows that represent records (tuples) and columns that represent fields (attributes). A query is a command that retrieves a subset of the information in the database according to certain criteria. A query may retrieve only records that meet certain criteria, or it may join fields from records across multiple tables by use of a common field.

Frequently, data from many sources is collected into large archives of data called data warehouses. The process of moving data from its original sources (such as databases) to a centralized location (generally a data warehouse) is called ETL (which stands for extract , transform , and load ).

• The extraction step occurs when you identify and copy or export the desired data from its source, such as by running a database query to retrieve the desired records.
• The transformation step is the process of cleaning the data so that they fit the analytical need for the data and the schema of the data warehouse. This may involve changing formats for certain fields, removing duplicate records, or renaming fields, among other processes.
• Finally, the clean data are loaded into the data warehouse, where they may join vast amounts of historical data and data from other sources.

After data are effectively collected and cleaned, they can be analyzed with a variety of techniques. Analysis often begins with descriptive and exploratory data analysis. Descriptive data analysis uses statistics to organize and summarize data, making it easier to understand the broad qualities of the dataset. Exploratory data analysis looks for insights into the data that may arise from descriptions of distribution, central tendency, or variability for a single data field. Further relationships between data may become apparent by examining two fields together. Visualizations may be employed during analysis, such as histograms (graphs in which the length of a bar indicates a quantity) or stem-and-leaf plots (which divide data into buckets, or “stems,” with individual data points serving as “leaves” on the stem).

Data analysis frequently goes beyond descriptive analysis to predictive analysis, making predictions about the future using predictive modeling techniques. Predictive modeling uses machine learning , regression analysis methods (which mathematically calculate the relationship between an independent variable and a dependent variable), and classification techniques to identify trends and relationships among variables. Predictive analysis may involve data mining , which is the process of discovering interesting or useful patterns in large volumes of information. Data mining often involves cluster analysis , which tries to find natural groupings within data, and anomaly detection , which detects instances in data that are unusual and stand out from other patterns. It may also look for rules within datasets, strong relationships among variables in the data.

Data Analysis

• Introduction to Data Analysis
• Quantitative Analysis Tools
• Qualitative Analysis Tools
• Mixed Methods Analysis
• Geospatial Analysis
• Further Reading

What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

• Photographs 
• Hand-written notes from field observation
• Machine learning training data sets
• Ethnographic interview transcripts
• Sheet music
• Scripts for plays and musicals 
• Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

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What is Data Analysis? An Introductory Guide

The data analysis process, key data analysis skills, start your journey into data analysis with the official enterpise big data analyst certification, data analysis examples in the enterprise, frequently asked questions (faqs).

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to derive meaningful insights and make informed decisions. It involves examining raw data to identify patterns, trends, and relationships that can be used to understand various aspects of a business, organization, or phenomenon. This process often employs statistical methods, machine learning algorithms, and data visualization techniques to extract valuable information from data sets.

At its core, data analysis aims to answer questions, solve problems, and support decision-making processes. It helps uncover hidden patterns or correlations within data that may not be immediately apparent, leading to actionable insights that can drive business strategies and improve performance. Whether it’s analyzing sales figures to identify market trends, evaluating customer feedback to enhance products or services, or studying medical data to improve patient outcomes, data analysis plays a crucial role in numerous domains.

Effective data analysis requires not only technical skills but also domain knowledge and critical thinking. Analysts must understand the context in which the data is generated, choose appropriate analytical tools and methods, and interpret results accurately to draw meaningful conclusions. Moreover, data analysis is an iterative process that may involve refining hypotheses, collecting additional data, and revisiting analytical techniques to ensure the validity and reliability of findings.

Why spend time to learn data analysis?

Learning about data analysis is beneficial for your career because it equips you with the skills to make data-driven decisions, which are highly valued in today’s data-centric business environment. Employers increasingly seek professionals who can gather, analyze, and interpret data to drive innovation, optimize processes, and achieve strategic objectives.

The data analysis process is a systematic approach to extracting valuable insights and making informed decisions from raw data. It begins with defining the problem or question at hand, followed by collecting and cleaning the relevant data. Exploratory data analysis (EDA) helps in understanding the data’s characteristics and uncovering patterns, while data modeling and analysis apply statistical or machine learning techniques to derive meaningful conclusions. In most organizations, data analysis is structured in a number of steps:

• Define the Problem or Question: The first step is to clearly define the problem or question you want to address through data analysis. This could involve understanding business objectives, identifying research questions, or defining hypotheses to be tested.
• Data Collection: Once the problem is defined, gather relevant data from various sources. This could include structured data from databases, spreadsheets, or surveys, as well as unstructured data like text documents or social media posts.
• Data Cleaning and Preprocessing: Clean and preprocess the data to ensure its quality and reliability. This step involves handling missing values, removing duplicates, standardizing formats, and transforming data if needed (e.g., scaling numerical data, encoding categorical variables).
• Exploratory Data Analysis (EDA): Explore the data through descriptive statistics, visualizations (e.g., histograms, scatter plots, heatmaps), and data profiling techniques. EDA helps in understanding the distribution of variables, detecting outliers, and identifying patterns or trends.
• Data Modeling and Analysis: Apply appropriate statistical or machine learning models to analyze the data and answer the research questions or address the problem. This step may involve hypothesis testing, regression analysis, clustering, classification, or other analytical techniques depending on the nature of the data and objectives.
• Interpretation of Results: Interpret the findings from the data analysis in the context of the problem or question. Determine the significance of results, draw conclusions, and communicate insights effectively.
• Decision Making and Action: Use the insights gained from data analysis to make informed decisions, develop strategies, or take actions that drive positive outcomes. Monitor the impact of these decisions and iterate the analysis process as needed.
• Communication and Reporting: Present the findings and insights derived from data analysis in a clear and understandable manner to stakeholders, using visualizations, dashboards, reports, or presentations. Effective communication ensures that the analysis results are actionable and contribute to informed decision-making.

These steps form a cyclical process, where feedback from decision-making may lead to revisiting earlier stages, refining the analysis, and continuously improving outcomes.

Key data analysis skills encompass a blend of technical expertise, critical thinking, and domain knowledge. Some of the essential skills for effective data analysis include:

Statistical Knowledge: Understanding statistical concepts and methods such as hypothesis testing, regression analysis, probability distributions, and statistical inference is fundamental for data analysis.

Data Manipulation and Cleaning: Proficiency in tools like Python, R, SQL, or Excel for data manipulation, cleaning, and transformation tasks, including handling missing values, removing duplicates, and standardizing data formats.

Data Visualization: Creating clear and insightful visualizations using tools like Matplotlib, Seaborn, Tableau, or Power BI to communicate trends, patterns, and relationships within data to non-technical stakeholders.

Machine Learning: Familiarity with machine learning algorithms such as decision trees, random forests, logistic regression, clustering, and neural networks for predictive modeling, classification, clustering, and anomaly detection tasks.

Programming Skills: Competence in programming languages such as Python, R, or SQL for data analysis, scripting, automation, and building data pipelines, along with version control using Git.

Critical Thinking: Ability to think critically, ask relevant questions, formulate hypotheses, and design robust analytical approaches to solve complex problems and extract actionable insights from data.

Domain Knowledge: Understanding the context and domain-specific nuances of the data being analyzed, whether it’s finance, healthcare, marketing, or any other industry, is crucial for meaningful interpretation and decision-making.

Data Ethics and Privacy: Awareness of data ethics principles , privacy regulations (e.g., GDPR, CCPA), and best practices for handling sensitive data responsibly and ensuring data security and confidentiality.

Communication and Storytelling: Effectively communicating analysis results through clear reports, presentations, and data-driven storytelling to convey insights, recommendations, and implications to diverse audiences, including non-technical stakeholders.

These skills are crucial in data analysis because they empower analysts to effectively extract, interpret, and communicate insights from complex datasets across various domains. Statistical knowledge forms the foundation for making data-driven decisions and drawing reliable conclusions. Proficiency in data manipulation and cleaning ensures data accuracy and consistency, essential for meaningful analysis. Here

The Enterprise Big Data Analyst certification is aimed at Data Analyst and provides in-depth theory and practical guidance to deduce value out of Big Data sets. The curriculum segments between different kinds of Big Data problems and its corresponding solutions. This course will teach participants how to autonomously find valuable insights in large data sets in order to realize business benefits.

Data analysis plays an important role in driving informed decision-making and strategic planning within enterprises across various industries. By harnessing the power of data, organizations can gain valuable insights into market trends, customer behaviors, operational efficiency, and performance metrics. Data analysis enables businesses to identify opportunities for growth, optimize processes, mitigate risks, and enhance overall competitiveness in the market. Examples of data analysis in the enterprise span a wide range of applications, including sales and marketing optimization, customer segmentation, financial forecasting, supply chain management, fraud detection, and healthcare analytics.

• Sales and Marketing Optimization: Enterprises use data analysis to analyze sales trends, customer preferences, and marketing campaign effectiveness. By leveraging techniques like customer segmentation and predictive modeling, businesses can tailor marketing strategies, optimize pricing strategies, and identify cross-selling or upselling opportunities.
• Customer Segmentation: Data analysis helps enterprises segment customers based on demographics, purchasing behavior, and preferences. This segmentation allows for targeted marketing efforts, personalized customer experiences, and improved customer retention and loyalty.
• Financial Forecasting: Data analysis is used in financial forecasting to analyze historical data, identify trends, and predict future financial performance. This helps businesses make informed decisions regarding budgeting, investment strategies, and risk management.
• Supply Chain Management: Enterprises use data analysis to optimize supply chain operations, improve inventory management, reduce lead times, and enhance overall efficiency. Analyzing supply chain data helps identify bottlenecks, forecast demand, and streamline logistics processes.
• Fraud Detection: Data analysis is employed to detect and prevent fraud in financial transactions, insurance claims, and online activities. By analyzing patterns and anomalies in data, enterprises can identify suspicious activities, mitigate risks, and protect against fraudulent behavior.
• Healthcare Analytics: In the healthcare sector, data analysis is used for patient care optimization, disease prediction, treatment effectiveness evaluation, and resource allocation. Analyzing healthcare data helps improve patient outcomes, reduce healthcare costs, and support evidence-based decision-making.

These examples illustrate how data analysis is a vital tool for enterprises to gain actionable insights, improve decision-making processes, and achieve strategic objectives across diverse areas of business operations.

Below are some of the most frequently asked questions about data analysis and their answers:

What role does domain knowledge play in data analysis?

Domain knowledge is crucial as it provides context, understanding of data nuances, insights into relevant variables and metrics, and helps in interpreting results accurately within specific industries or domains.

How do you ensure the quality and accuracy of data for analysis?

Ensuring data quality and accuracy involves data validation, cleaning techniques like handling missing values and outliers, standardizing data formats, performing data integrity checks, and validating results through cross-validation or data audits.

What tools and techniques are commonly used in data analysis?

Commonly used tools and techniques in data analysis include programming languages like Python and R, statistical methods such as regression analysis and hypothesis testing, machine learning algorithms for predictive modeling, data visualization tools like Tableau and Matplotlib, and database querying languages like SQL.

What are the steps involved in the data analysis process?

The data analysis process typically includes defining the problem, collecting data, cleaning and preprocessing the data, conducting exploratory data analysis, applying statistical or machine learning models for analysis, interpreting results, making decisions based on insights, and communicating findings to stakeholders.

What is data analysis, and why is it important?

Data analysis involves examining, cleaning, transforming, and modeling data to derive meaningful insights and make informed decisions. It is crucial because it helps organizations uncover trends, patterns, and relationships within data, leading to improved decision-making, enhanced business strategies, and competitive advantage.

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Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

What is Data Analysis? (Types, Methods, and Tools)

• Couchbase Product Marketing December 17, 2023

Data analysis is the process of cleaning, transforming, and interpreting data to uncover insights, patterns, and trends. It plays a crucial role in decision making, problem solving, and driving innovation across various domains. 

In addition to further exploring the role data analysis plays this blog post will discuss common data analysis techniques, delve into the distinction between quantitative and qualitative data, explore popular data analysis tools, and discuss the steps involved in the data analysis process. 

By the end, you should have a deeper understanding of data analysis and its applications, empowering you to harness the power of data to make informed decisions and gain actionable insights.

Why is Data Analysis Important?

Data analysis is important across various domains and industries. It helps with:

• Decision Making : Data analysis provides valuable insights that support informed decision making, enabling organizations to make data-driven choices for better outcomes.
• Problem Solving : Data analysis helps identify and solve problems by uncovering root causes, detecting anomalies, and optimizing processes for increased efficiency.
• Performance Evaluation : Data analysis allows organizations to evaluate performance, track progress, and measure success by analyzing key performance indicators (KPIs) and other relevant metrics.
• Gathering Insights : Data analysis uncovers valuable insights that drive innovation, enabling businesses to develop new products, services, and strategies aligned with customer needs and market demand.
• Risk Management : Data analysis helps mitigate risks by identifying risk factors and enabling proactive measures to minimize potential negative impacts.

By leveraging data analysis, organizations can gain a competitive advantage, improve operational efficiency, and make smarter decisions that positively impact the bottom line.

Quantitative vs. Qualitative Data

In data analysis, you’ll commonly encounter two types of data: quantitative and qualitative. Understanding the differences between these two types of data is essential for selecting appropriate analysis methods and drawing meaningful insights. Here’s an overview of quantitative and qualitative data:

Quantitative Data

Quantitative data is numerical and represents quantities or measurements. It’s typically collected through surveys, experiments, and direct measurements. This type of data is characterized by its ability to be counted, measured, and subjected to mathematical calculations. Examples of quantitative data include age, height, sales figures, test scores, and the number of website users.

Quantitative data has the following characteristics:

• Numerical : Quantitative data is expressed in numerical values that can be analyzed and manipulated mathematically.
• Objective : Quantitative data is objective and can be measured and verified independently of individual interpretations.
• Statistical Analysis : Quantitative data lends itself well to statistical analysis. It allows for applying various statistical techniques, such as descriptive statistics, correlation analysis, regression analysis, and hypothesis testing.
• Generalizability : Quantitative data often aims to generalize findings to a larger population. It allows for making predictions, estimating probabilities, and drawing statistical inferences.

Qualitative Data

Qualitative data, on the other hand, is non-numerical and is collected through interviews, observations, and open-ended survey questions. It focuses on capturing rich, descriptive, and subjective information to gain insights into people’s opinions, attitudes, experiences, and behaviors. Examples of qualitative data include interview transcripts, field notes, survey responses, and customer feedback.

Qualitative data has the following characteristics:

• Descriptive : Qualitative data provides detailed descriptions, narratives, or interpretations of phenomena, often capturing context, emotions, and nuances.
• Subjective : Qualitative data is subjective and influenced by the individuals’ perspectives, experiences, and interpretations.
• Interpretive Analysis : Qualitative data requires interpretive techniques, such as thematic analysis, content analysis, and discourse analysis, to uncover themes, patterns, and underlying meanings.
• Contextual Understanding : Qualitative data emphasizes understanding the social, cultural, and contextual factors that shape individuals’ experiences and behaviors.
• Rich Insights : Qualitative data enables researchers to gain in-depth insights into complex phenomena and explore research questions in greater depth.

In summary, quantitative data represents numerical quantities and lends itself well to statistical analysis, while qualitative data provides rich, descriptive insights into subjective experiences and requires interpretive analysis techniques. Understanding the differences between quantitative and qualitative data is crucial for selecting appropriate analysis methods and drawing meaningful conclusions in research and data analysis.

Types of Data Analysis

Different types of data analysis techniques serve different purposes. In this section, we’ll explore four types of data analysis: descriptive, diagnostic, predictive, and prescriptive, and go over how you can use them.

Descriptive Analysis

Descriptive analysis involves summarizing and describing the main characteristics of a dataset. It focuses on gaining a comprehensive understanding of the data through measures such as central tendency (mean, median, mode), dispersion (variance, standard deviation), and graphical representations (histograms, bar charts). For example, in a retail business, descriptive analysis may involve analyzing sales data to identify average monthly sales, popular products, or sales distribution across different regions.

Diagnostic Analysis

Diagnostic analysis aims to understand the causes or factors influencing specific outcomes or events. It involves investigating relationships between variables and identifying patterns or anomalies in the data. Diagnostic analysis often uses regression analysis, correlation analysis, and hypothesis testing to uncover the underlying reasons behind observed phenomena. For example, in healthcare, diagnostic analysis could help determine factors contributing to patient readmissions and identify potential improvements in the care process.

Predictive Analysis

Predictive analysis focuses on making predictions or forecasts about future outcomes based on historical data. It utilizes statistical models, machine learning algorithms, and time series analysis to identify patterns and trends in the data. By applying predictive analysis, businesses can anticipate customer behavior, market trends, or demand for products and services. For example, an e-commerce company might use predictive analysis to forecast customer churn and take proactive measures to retain customers.

Prescriptive Analysis

Prescriptive analysis takes predictive analysis a step further by providing recommendations or optimal solutions based on the predicted outcomes. It combines historical and real-time data with optimization techniques, simulation models, and decision-making algorithms to suggest the best course of action. Prescriptive analysis helps organizations make data-driven decisions and optimize their strategies. For example, a logistics company can use prescriptive analysis to determine the most efficient delivery routes, considering factors like traffic conditions, fuel costs, and customer preferences.

In summary, data analysis plays a vital role in extracting insights and enabling informed decision making. Descriptive analysis helps understand the data, diagnostic analysis uncovers the underlying causes, predictive analysis forecasts future outcomes, and prescriptive analysis provides recommendations for optimal actions. These different data analysis techniques are valuable tools for businesses and organizations across various industries.

Data Analysis Methods

In addition to the data analysis types discussed earlier, you can use various methods to analyze data effectively. These methods provide a structured approach to extract insights, detect patterns, and derive meaningful conclusions from the available data. Here are some commonly used data analysis methods:

Statistical Analysis 

Statistical analysis involves applying statistical techniques to data to uncover patterns, relationships, and trends. It includes methods such as hypothesis testing, regression analysis, analysis of variance (ANOVA), and chi-square tests. Statistical analysis helps organizations understand the significance of relationships between variables and make inferences about the population based on sample data. For example, a market research company could conduct a survey to analyze the relationship between customer satisfaction and product price. They can use regression analysis to determine whether there is a significant correlation between these variables.

Data Mining

Data mining refers to the process of discovering patterns and relationships in large datasets using techniques such as clustering, classification, association analysis, and anomaly detection. It involves exploring data to identify hidden patterns and gain valuable insights. For example, a telecommunications company could analyze customer call records to identify calling patterns and segment customers into groups based on their calling behavior. 

Text Mining

Text mining involves analyzing unstructured data , such as customer reviews, social media posts, or emails, to extract valuable information and insights. It utilizes techniques like natural language processing (NLP), sentiment analysis, and topic modeling to analyze and understand textual data. For example, consider how a hotel chain might analyze customer reviews from various online platforms to identify common themes and sentiment patterns to improve customer satisfaction.

Time Series Analysis

Time series analysis focuses on analyzing data collected over time to identify trends, seasonality, and patterns. It involves techniques such as forecasting, decomposition, and autocorrelation analysis to make predictions and understand the underlying patterns in the data.

For example, an energy company could analyze historical electricity consumption data to forecast future demand and optimize energy generation and distribution.

Data Visualization

Data visualization is the graphical representation of data to communicate patterns, trends, and insights visually. It uses charts, graphs, maps, and other visual elements to present data in a visually appealing and easily understandable format. For example, a sales team might use a line chart to visualize monthly sales trends and identify seasonal patterns in their sales data.

These are just a few examples of the data analysis methods you can use. Your choice should depend on the nature of the data, the research question or problem, and the desired outcome.

How to Analyze Data

Analyzing data involves following a systematic approach to extract insights and derive meaningful conclusions. Here are some steps to guide you through the process of analyzing data effectively:

Define the Objective : Clearly define the purpose and objective of your data analysis. Identify the specific question or problem you want to address through analysis.

Prepare and Explore the Data : Gather the relevant data and ensure its quality. Clean and preprocess the data by handling missing values, duplicates, and formatting issues. Explore the data using descriptive statistics and visualizations to identify patterns, outliers, and relationships.

Apply Analysis Techniques : Choose the appropriate analysis techniques based on your data and research question. Apply statistical methods, machine learning algorithms, and other analytical tools to derive insights and answer your research question.

Interpret the Results : Analyze the output of your analysis and interpret the findings in the context of your objective. Identify significant patterns, trends, and relationships in the data. Consider the implications and practical relevance of the results.

Communicate and Take Action : Communicate your findings effectively to stakeholders or intended audiences. Present the results clearly and concisely, using visualizations and reports. Use the insights from the analysis to inform decision making.

Remember, data analysis is an iterative process, and you may need to revisit and refine your analysis as you progress. These steps provide a general framework to guide you through the data analysis process and help you derive meaningful insights from your data.

Data Analysis Tools

Data analysis tools are software applications and platforms designed to facilitate the process of analyzing and interpreting data . These tools provide a range of functionalities to handle data manipulation, visualization, statistical analysis, and machine learning. Here are some commonly used data analysis tools:

Spreadsheet Software

Tools like Microsoft Excel, Google Sheets, and Apple Numbers are used for basic data analysis tasks. They offer features for data entry, manipulation, basic statistical functions, and simple visualizations.

Business Intelligence (BI) Platforms

BI platforms like Microsoft Power BI, Tableau, and Looker integrate data from multiple sources, providing comprehensive views of business performance through interactive dashboards, reports, and ad hoc queries.

Programming Languages and Libraries

Programming languages like R and Python, along with their associated libraries (e.g., NumPy, SciPy, scikit-learn), offer extensive capabilities for data analysis. They provide flexibility, customizability, and access to a wide range of statistical and machine-learning algorithms.

Cloud-Based Analytics Platforms

Cloud-based platforms like Google Cloud Platform (BigQuery, Data Studio), Microsoft Azure (Azure Analytics, Power BI), and Amazon Web Services (AWS Analytics, QuickSight) provide scalable and collaborative environments for data storage, processing, and analysis. They have a wide range of analytical capabilities for handling large datasets.

Data Mining and Machine Learning Tools

Tools like RapidMiner, KNIME, and Weka automate the process of data preprocessing, feature selection, model training, and evaluation. They’re designed to extract insights and build predictive models from complex datasets.

Text Analytics Tools

Text analytics tools, such as Natural Language Processing (NLP) libraries in Python (NLTK, spaCy) or platforms like RapidMiner Text Mining Extension, enable the analysis of unstructured text data . They help extract information, sentiment, and themes from sources like customer reviews or social media.

Choosing the right data analysis tool depends on analysis complexity, dataset size, required functionalities, and user expertise. You might need to use a combination of tools to leverage their combined strengths and address specific analysis needs. 

By understanding the power of data analysis, you can leverage it to make informed decisions, identify opportunities for improvement, and drive innovation within your organization. Whether you’re working with quantitative data for statistical analysis or qualitative data for in-depth insights, it’s important to select the right analysis techniques and tools for your objectives.

To continue learning about data analysis, review the following resources:

• What is Big Data Analytics?
• Operational Analytics
• JSON Analytics + Real-Time Insights
• Database vs. Data Warehouse: Differences, Use Cases, Examples
• Couchbase Capella Columnar Product Blog
• Posted in: Analytics , Application Design , Best Practices and Tutorials
• Tagged in: data analytics , data visualization , time series

Posted by Couchbase Product Marketing

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What Is Data Analysis in Research? Why It Matters & What Data Analysts Do

Data analysis in research is the process of uncovering insights from data sets. Data analysts can use their knowledge of statistical techniques, research theories and methods, and research practices to analyze data. They take data and uncover what it’s trying to tell us, whether that’s through charts, graphs, or other visual representations. To analyze data effectively you need a strong background in mathematics and statistics, excellent communication skills, and the ability to identify relevant information.

Read on for more information about data analysis roles in research and what it takes to become one.

In this article – What is data analysis in research?

What is data analysis in research?

Why data analysis matters, what is data science, data analysis for quantitative research, data analysis for qualitative research, what are data analysis techniques in research, what do data analysts do, in related articles.

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Data analysis is looking at existing data and attempting to draw conclusions from it. It is the process of asking “what does this data show us?” There are many different types of data analysis and a range of methods and tools for analyzing data. You may hear some of these terms as you explore data analysis roles in research – data exploration, data visualization, and data modelling. Data exploration involves exploring and reviewing the data, asking questions like “Does the data exist?” and “Is it valid?”.

Data visualization is the process of creating charts, graphs, and other visual representations of data. The goal of visualization is to help us see and understand data more quickly and easily. Visualizations are powerful and can help us uncover insights from the data that we may have missed without the visual aid. Data modelling involves taking the data and creating a model out of it. Data modelling organises and visualises data to help us understand it better and make sense of it. This will often include creating an equation for the data or creating a statistical model.

Data analysis is important for all research areas, from quantitative surveys to qualitative projects. While researchers often conduct a data analysis at the end of the project, they should be analyzing data alongside their data collection. This allows researchers to monitor their progress and adjust their approach when needed.

The analysis is also important for verifying the quality of the data. What you discover through your analysis can also help you decide whether or not to continue with your project. If you find that your data isn’t consistent with your research questions, you might decide to end your research before collecting enough data to generalize your results.

Data science is the intersection between computer science and statistics. It’s been defined as the “conceptual basis for systematic operations on data”. This means that data scientists use their knowledge of statistics and research methods to find insights in data. They use data to find solutions to complex problems, from medical research to business intelligence. Data science involves collecting and exploring data, creating models and algorithms from that data, and using those models to make predictions and find other insights.

Data scientists might focus on the visual representation of data, exploring the data, or creating models and algorithms from the data. Many people in data science roles also work with artificial intelligence and machine learning. They feed the algorithms with data and the algorithms find patterns and make predictions. Data scientists often work with data engineers. These engineers build the systems that the data scientists use to collect and analyze data.

Data analysis techniques can be divided into two categories:

• Quantitative approach
• Qualitative approach

Note that, when discussing this subject, the term “data analysis” often refers to statistical techniques.

Qualitative research uses unquantifiable data like unstructured interviews, observations, and case studies. Quantitative research usually relies on generalizable data and statistical modelling, while qualitative research is more focused on finding the “why” behind the data. This means that qualitative data analysis is useful in exploring and making sense of the unstructured data that researchers collect.

Data analysts will take their data and explore it, asking questions like “what’s going on here?” and “what patterns can we see?” They will use data visualization to help readers understand the data and identify patterns. They might create maps, timelines, or other representations of the data. They will use their understanding of the data to create conclusions that help readers understand the data better.

Quantitative research relies on data that can be measured, like survey responses or test results. Quantitative data analysis is useful in drawing conclusions from this data. To do this, data analysts will explore the data, looking at the validity of the data and making sure that it’s reliable. They will then visualize the data, making charts and graphs to make the data more accessible to readers. Finally, they will create an equation or use statistical modelling to understand the data.

A common type of research where you’ll see these three steps is market research. Market researchers will collect data from surveys, focus groups, and other methods. They will then analyze that data and make conclusions from it, like how much consumers are willing to spend on a product or what factors make one product more desirable than another.

Quantitative methods

These are useful in quantitatively analyzing data. These methods use a quantitative approach to analyzing data and their application includes in science and engineering, as well as in traditional business. This method is also useful for qualitative research.

Statistical methods are used to analyze data in a statistical manner. Data analysis is not limited only to statistics or probability. Still, it can also be applied in other areas, such as engineering, business, economics, marketing, and all parts of any field that seeks knowledge about something or someone.

If you are an entrepreneur or an investor who wants to develop your business or your company’s value proposition into a reality, you will need data analysis techniques. But if you want to understand how your company works, what you have done right so far, and what might happen next in terms of growth or profitability—you don’t need those kinds of experiences. Data analysis is most applicable when it comes to understanding information from external sources like research papers that aren’t necessarily objective.

A brief intro to statistics

Statistics is a field of study that analyzes data to determine the number of people, firms, and companies in a population and their relative positions on a particular economic level. The application of statistics can be to any group or entity that has any kind of data or information (even if it’s only numbers), so you can use statistics to make an educated guess about your company, your customers, your competitors, your competitors’ customers, your peers, and so on. You can also use statistics to help you develop a business strategy.

Data analysis methods can help you understand how different groups are performing in a given area—and how they might perform differently from one another in the future—but they can also be used as an indicator for areas where there is better or worse performance than expected.

In addition to being able to see what trends are occurring within an industry or population within that industry or population—and why some companies may be doing better than others—you will also be able to see what changes have been made over time within that industry or population by comparing it with others and analyzing those differences over time.

Data mining

Data mining is the use of mathematical techniques to analyze data with the goal of finding patterns and trends. A great example of this would be analyzing the sales patterns for a certain product line. In this case, a data mining technique would involve using statistical techniques to find patterns in the data and then analyzing them using mathematical techniques to identify relationships between variables and factors.

Note that these are different from each other and much more advanced than traditional statistics or probability.

As a data analyst, you’ll be responsible for analyzing data from different sources. You’ll work with multiple stakeholders and your job will vary depending on what projects you’re working on. You’ll likely work closely with data scientists and researchers on a daily basis, as you’re all analyzing the same data.

Communication is key, so being able to work with others is important. You’ll also likely work with researchers or principal investigators (PIs) to collect and organize data. Your data will be from various sources, from structured to unstructured data like interviews and observations. You’ll take that data and make sense of it, organizing it and visualizing it so readers can understand it better. You’ll use this data to create models and algorithms that make predictions and find other insights. This can include creating equations or mathematical models from the data or taking data and creating a statistical model.

Data analysis is an important part of all types of research. Quantitative researchers analyze the data they collect through surveys and experiments, while qualitative researchers collect unstructured data like interviews and observations. Data analysts take all of this data and turn it into something that other researchers and readers can understand and make use of.

With proper data analysis, researchers can make better decisions, understand their data better, and get a better picture of what’s going on in the world around them. Data analysis is a valuable skill, and many companies hire data analysts and data scientists to help them understand their customers and make better decisions.

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What Is Data Analysis? Methods, Process & Tools

Up to 55% of data collected by companies goes unused for analysis .

That’s a large chunk of insights companies are missing out on.

So, what can you do to make sure your data doesn't get lost among the noise, and how can you properly analyze your data? What even is data analysis?

In this guide, you’ll learn all this and more.

Let’s dive in.

What Is Data Analysis?

• Why Is Data Analysis Important?
• Data Analysis Techniques

Data Analysis Process

Data analysis tools, data analysis tips.

Data analysis is the process of cleaning, analyzing, and visualizing data, with the goal of discovering valuable insights and driving smarter business decisions.

The methods you use to analyze data will depend on whether you’re analyzing quantitative or qualitative data .

Either way, you’ll need data analysis tools to help you extract useful information from business data, and help make the data analysis process easier.

You’ll often hear the term data analytics in business, which is the science or discipline that encompasses the whole process of data management, from data collection and storage to data analysis andvisualization.

Data analysis, while part of the data management process, focuses on the process of turning raw data into useful statistics, information, and explanations.

Why Is Data Analysis important in 2022?

Data is everywhere: in spreadsheets, your sales pipeline, social media platforms, customer satisfaction surveys , customer support tickets, and more. In our modern information age it’s created at blinding speeds and, when data is analyzed correctly, can be a company’s most valuable asset. 

Businesses need to know what their customers need, so that they can increase customer retention and attract new customers. But to know exactly what customers need and what their pain points are, businesses need to deep-dive into their customer data.

In short, through data analysis businesses can reveal insights that tell you where you need to focus your efforts to help your company grow.  

It can help businesses improve specific aspects about their products and services, as well as their overall brand image and customer experience .

Product teams, for example, often analyze customer feedback to understand how customers interact with their product, what they’re frustrated with, and which new features they’d like to see. Then, they translate this insight into UX improvements, new features, and enhanced functionalities.

Through data analysis, you can also detect the weaknesses and strengths of your competition, uncovering opportunities for improvement.

6 Types of Data Analysis: Techniques and Methods

There are a number of useful data analysis techniques you can use to discover insights in all types of data, and emerging data analysis trends that can help you stay ahead of your competitors.

Types of data analysis:

• Text Analysis
• Descriptive Analysis
• Inferential Analysis
• Diagnostic Analysis
• Predictive Analysis
• Prescriptive Analysis

Text Analysis: What is happening?

Text analysis , also text analytics or data mining, uses machine learning with natural language processing (NLP) to organize unstructured text data so that it can be properly analyzed for valuable insights. Text analysis is a form of qualitative analysis that is concerned with more than just statistics and numerical values.

By transforming human language into machine-readable data, text analysis tools can sort text by topic, extract keywords, and read for emotion and intent. It tells us “What is happening” as specific, often subjective data. It offers more in-depth and targeted views into why something may be happening, or why something happened.

You can use text analysis to detect topics in customer feedback, for example, and understand which aspects of your brand are important to your customers. 

Try out this survey analyzer that sorts open-ended survey responses into different topics:

Test with your own text

Sentiment analysis is another approach to text analysis, used to analyze data and sort it as Positive, Negative, or Neutral to gain in-depth knowledge about how customers feel towards each aspect . 

Try out this sentiment analyzer , below, to get an idea of how text analysis works.

Descriptive Analysis: What happened?

Descriptive data analysis provides the “What happened?” when analyzing quantitative data. It is the most basic and most common form of data analysis concerned with describing, summarizing, and identifying patterns through calculations of existing data, like mean, median, mode, percentage, frequency, and range. 

Descriptive analysis is usually the baseline from which other data analysis begins. It is, no doubt, very useful for producing things like revenue reports and KPI dashboards. However, as it is only concerned with statistical analysis and absolute numbers, it can’t provide the reason or motivation for why and how those numbers developed.

Inferential Analysis: What happened?

Inferential analysis generalizes or hypothesizes about “What happened?” by comparing statistics from groups within an entire population: the population of a country, existing customer base, patients in a medical study, etc. The most common methods for conducting inferential statistics are hypothesis tests and estimation theories.

Inferential analysis is used widely in market research, to compare two variables in an attempt to reach a conclusion: money spent by female customers vs. male or among different age groups, for example. Or it can be used to survey a sample set of the population in an attempt to extrapolate information about the entire population. In this case it is necessary to properly calculate for a representative sample of the population.

Diagnostic Analysis: Why did it happen?

Diagnostic analysis, also known as root cause analysis, aims to answer “Why did 'X' happen?” . It uses insights from statistical analysis to attempt to understand the cause or reason behind statistics, by identifying patterns or deviations within the data to answer for why .

Diagnostic analysis can be helpful to understand customer behavior, to find out which marketing campaigns actually increase sales, for example. Or let’s say you notice a sudden decrease in customer complaints: Why did this happen?  

Perhaps you fired a certain employee or hired new ones. Maybe you have a new online interface or added a particular product feature. Diagnostic analysis can help calculate the correlation between these possible causes and existing data points. 

Predictive Analysis: What is likely to happen?

Predictive analysis uses known data to postulate about future events. It is concerned with “What is likely to happen.” Used in sales analysis , it often combines demographic data and purchase data with other data points to predict the actions of customers. 

For example, as the demographics of a certain area change, this will affect the ability of certain businesses to exist there. Or as the salary of a certain customer increases, theoretically, they will be able to buy more of your products.

There is often a lot of extrapolative guesswork involved in predictive analysis, but the more data points you have on a given demographic or individual customer, the more accurate the prediction is likely to be. 

Prescriptive Analysis: What action to take

Prescriptive analysis is the most advanced form of analysis, as it combines all of your data and analytics, then outputs a model prescription: What action to take. Prescriptive analysis works to analyze multiple scenarios, predict the outcome of each, and decide which is the best course of action based on the findings.

Artificial intelligence is an example of prescriptive analysis that’s at the cutting edge of data analysis. AI allows for prescriptive analysis that can ingest and break down massive amounts of data and effectively teach itself how to use the information and make its own informed decisions.

AI used to require huge computing power, making it difficult for businesses to implement. However, with the rise of more advanced data analysis tools , there are many exciting options available.

To speed up your data analysis process, you should consider integrating data analysis tools .

There are many data analysis tools you can get started with, depending on your technical skills, budget, and type of data you want to analyze. Most tools can easily be integrated via APIs and one-click integrations. 

If using an API, you might need a developer’s help to set it up. Once connected, your data can run freely through your data analysis tools.

Here’s a quick rundown of the top data analysis tools that can help you perform everything from text analysis to data visualization.

• MonkeyLearn – No-code machine learning platform that provides a full suite of text analysis tools and a robust API . Easily build custom machine learning models in a point and click interface.
• KNIME: – Open-source platform for building advanced machine learning solutions, and visualizing data.
• RapidMiner – For data analytics teams that want to tackle challenging tasks and handle large amounts of data.
• Microsoft Excel – Filter, organize, and visualize quantitative data. The perfect tool for performing simple data analysis. Explore common functions and formulas for data analysis in Excel .
• Tableau – A powerful analytics and data visualization platform. Connect all your data and create interactive dashboards that update in real-time. 
• R – A free software environment for statistical computing and graphics. Learning R is relatively easy, even if you don’t have a programming background.
• Python – The preferred programming language for machine learning. Use it to build data analysis solutions for various use cases.

You’ll need to implement a data analysis process to get the most out of your data. While it can be complex to perform data analysis, depending on the type of data you’re analyzing, there are some hard and fast rules that you can follow.

Below, we’ve outlined the steps you’ll need to follow to analyze your data :

• Data Decision
• Data Collection
• Data Cleaning
• Data Analysis
• Data Interpretation
• Data Visualization

1. Data Decision

First, you’ll need to set  clear objectives. What do you want to gain from your data analysis.

This will help you determine the type of data that you’ll need to collect and analyze, and which data analysis technique you need to apply.

2. Data Collection

Data is everywhere, and you’ll want to bring it together in one place ready for analysis.

Whether you’re collecting quantitative or qualitative data, Excel is a great platform for storing your data, or you could connect data sources directly to your analysis tools via APIs and integrations.

3. Data Cleaning

It’s likely that unstructured data will need to be cleaned before analyzing it to gain more accurate results.

Get rid of the noise, like special characters, punctuation marks, stopwords (and, too, she, they), HTML tags, duplicates, etc. Discover some more in-depth tips on how to clean your data .

4. Data Analysis

Once your data has been cleaned it will be ready for analysis. As you choose topics to focus on and parameters for measuring your data, you might notice that you don’t have enough relevant data. That might mean you have to go back to the data collection phase.

It’s important to remember that data analysis is not a linear process. You’ll have to go back and forth and reiterate. During the actual analysis, you’ll benefit from using data analysis tools that will make it easier to understand, interpret, and draw clear conclusions from your data.

5. Data Interpretation

Remember the goals you set at the beginning?

Now you can interpret the results of your data to help you reach your goals. Structure the results in a way that’s clear and makes sense to all teams. And make decisions based on what you’ve learned.

6. Data Visualization

Dashboards are a great way to aggregate your data, and make it easy to spot trends and patterns. Some data analysis tools, like MonkeyLearn , have in-built dashboards or you can connect to your existing BI tools.

Check out MonkeyLearn’s data dashboard, below, and try out the public data visualization dashboard , where you can slice and dice your data by topic, keyword, sentiment, and more.

Remember data analysis is a reiterative process. 

It can be painstaking and tedious at times, especially if you are manually analyzing huge amounts of data. 

However, once you’ve defined your goals and collected enough relevant data, you should be well on your way to discovering those valuable insights.

So, without further ado, here are some final tips before you set off on your data analysis journey:

• Collect as much data as possible – the more relevant data you have, the more accurate your insights will be data. 
• Systematically reach out to your customers – up-to-date insights will help your business grow and, besides, your customers' needs are constantly changing – which means your data is too. To stay relevant, keep on top of what your customers are requesting or complaining about.  
• Keep data analysis in-house – your ‘data analyst’ should know your business and understand your strategic goals. Remember that the insights you might uncover from performing data analysis could lead to valuable business decisions. The more familiar someone is with your data and goals, the more likely they are to find value in your data. 
• Remember, data is everywhere – Don’t forget to analyze data from external sources too. From third-party payment processing services to public online reviews.

Get Started with Data Analysis

There is almost no end to the possibilities of data analysis when you know how to do it right. Whether quantitative or qualitative, there are a number of analytical solutions and pathways to get real insights from your data.

Performing text analysis on your unstructured text data can offer huge advantages and potential advancements for your company, whether it comes from surveys, social media, customer service tickets – the list goes on and on. There is a wealth of information to be gathered from text data you may not have even considered.

MonkeyLearn offers dozens of easy-to-use text analysis tools that can be up and running in just a few minutes to help you get the most from your data. Schedule a demo to see how it works.

Inés Roldós

January 9th, 2021

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What is data analysis? Examples and how to get started

Even with years of professional experience working with data, the term "data analysis" still sets off a panic button in my soul. And yes, when it comes to serious data analysis for your business, you'll eventually want data scientists on your side. But if you're just getting started, no panic attacks are required.

Table of contents:

Quick review: What is data analysis?

Data analysis is the process of examining, filtering, adapting, and modeling data to help solve problems. Data analysis helps determine what is and isn't working, so you can make the changes needed to achieve your business goals. 

Keep in mind that data analysis includes analyzing both quantitative data (e.g., profits and sales) and qualitative data (e.g., surveys and case studies) to paint the whole picture. Here are two simple examples (of a nuanced topic) to show you what I mean.

An example of quantitative data analysis is an online jewelry store owner using inventory data to forecast and improve reordering accuracy. The owner looks at their sales from the past six months and sees that, on average, they sold 210 gold pieces and 105 silver pieces per month, but they only had 100 gold pieces and 100 silver pieces in stock. By collecting and analyzing inventory data on these SKUs, they're forecasting to improve reordering accuracy. The next time they order inventory, they order twice as many gold pieces as silver to meet customer demand.

An example of qualitative data analysis is a fitness studio owner collecting customer feedback to improve class offerings. The studio owner sends out an open-ended survey asking customers what types of exercises they enjoy the most. The owner then performs qualitative content analysis to identify the most frequently suggested exercises and incorporates these into future workout classes.

Why is data analysis important?

Here's why it's worth implementing data analysis for your business:

Understand your target audience: You might think you know how to best target your audience, but are your assumptions backed by data? Data analysis can help answer questions like, "What demographics define my target audience?" or "What is my audience motivated by?"

Inform decisions: You don't need to toss and turn over a decision when the data points clearly to the answer. For instance, a restaurant could analyze which dishes on the menu are selling the most, helping them decide which ones to keep and which ones to change.

Adjust budgets: Similarly, data analysis can highlight areas in your business that are performing well and are worth investing more in, as well as areas that aren't generating enough revenue and should be cut. For example, a B2B software company might discover their product for enterprises is thriving while their small business solution lags behind. This discovery could prompt them to allocate more budget toward the enterprise product, resulting in better resource utilization.

Identify and solve problems: Let's say a cell phone manufacturer notices data showing a lot of customers returning a certain model. When they investigate, they find that model also happens to have the highest number of crashes. Once they identify and solve the technical issue, they can reduce the number of returns.

Types of data analysis (with examples)

There are five main types of data analysis—with increasingly scary-sounding names. Each one serves a different purpose, so take a look to see which makes the most sense for your situation. It's ok if you can't pronounce the one you choose. 

Text analysis: What is happening?

Here are a few methods used to perform text analysis, to give you a sense of how it's different from a human reading through the text: 

Word frequency identifies the most frequently used words. For example, a restaurant monitors social media mentions and measures the frequency of positive and negative keywords like "delicious" or "expensive" to determine how customers feel about their experience. 

Language detection indicates the language of text. For example, a global software company may use language detection on support tickets to connect customers with the appropriate agent. 

Keyword extraction automatically identifies the most used terms. For example, instead of sifting through thousands of reviews, a popular brand uses a keyword extractor to summarize the words or phrases that are most relevant. 

Statistical analysis: What happened?

Statistical analysis pulls past data to identify meaningful trends. Two primary categories of statistical analysis exist: descriptive and inferential.

Descriptive analysis

Here are a few methods used to perform descriptive analysis: 

Measures of frequency identify how frequently an event occurs. For example, a popular coffee chain sends out a survey asking customers what their favorite holiday drink is and uses measures of frequency to determine how often a particular drink is selected. 

Measures of central tendency use mean, median, and mode to identify results. For example, a dating app company might use measures of central tendency to determine the average age of its users.

Measures of dispersion measure how data is distributed across a range. For example, HR may use measures of dispersion to determine what salary to offer in a given field. 

Inferential analysis

Inferential analysis uses a sample of data to draw conclusions about a much larger population. This type of analysis is used when the population you're interested in analyzing is very large. 

Here are a few methods used when performing inferential analysis: 

Hypothesis testing identifies which variables impact a particular topic. For example, a business uses hypothesis testing to determine if increased sales were the result of a specific marketing campaign. 

Regression analysis shows the effect of independent variables on a dependent variable. For example, a rental car company may use regression analysis to determine the relationship between wait times and number of bad reviews. 

Diagnostic analysis: Why did it happen?

Diagnostic analysis, also referred to as root cause analysis, uncovers the causes of certain events or results. 

Here are a few methods used to perform diagnostic analysis: 

Time-series analysis analyzes data collected over a period of time. A retail store may use time-series analysis to determine that sales increase between October and December every year. 

Correlation analysis determines the strength of the relationship between variables. For example, a local ice cream shop may determine that as the temperature in the area rises, so do ice cream sales. 

Predictive analysis: What is likely to happen?

Predictive analysis aims to anticipate future developments and events. By analyzing past data, companies can predict future scenarios and make strategic decisions.  

Here are a few methods used to perform predictive analysis: 

Decision trees map out possible courses of action and outcomes. For example, a business may use a decision tree when deciding whether to downsize or expand. 

Prescriptive analysis: What action should we take?

The highest level of analysis, prescriptive analysis, aims to find the best action plan. Typically, AI tools model different outcomes to predict the best approach. While these tools serve to provide insight, they don't replace human consideration, so always use your human brain before going with the conclusion of your prescriptive analysis. Otherwise, your GPS might drive you into a lake.

Here are a few methods used to perform prescriptive analysis: 

Algorithms are used in technology to perform specific tasks. For example, banks use prescriptive algorithms to monitor customers' spending and recommend that they deactivate their credit card if fraud is suspected. 

Data analysis process: How to get started

The actual analysis is just one step in a much bigger process of using data to move your business forward. Here's a quick look at all the steps you need to take to make sure you're making informed decisions. 

Data decision

As with almost any project, the first step is to determine what problem you're trying to solve through data analysis. 

Make sure you get specific here. For example, a food delivery service may want to understand why customers are canceling their subscriptions. But to enable the most effective data analysis, they should pose a more targeted question, such as "How can we reduce customer churn without raising costs?" 

Data collection

Next, collect the required data from both internal and external sources. 

Internal data comes from within your business (think CRM software, internal reports, and archives), and helps you understand your business and processes.

External data originates from outside of the company (surveys, questionnaires, public data) and helps you understand your industry and your customers. 

Data cleaning

Data can be seriously misleading if it's not clean. So before you analyze, make sure you review the data you collected.  Depending on the type of data you have, cleanup will look different, but it might include: 

Removing unnecessary information 

Addressing structural errors like misspellings

Deleting duplicates

Trimming whitespace

Human checking for accuracy 

Data analysis

Now that you've compiled and cleaned the data, use one or more of the above types of data analysis to find relationships, patterns, and trends. 

Data analysis tools can speed up the data analysis process and remove the risk of inevitable human error. Here are some examples.

Spreadsheets sort, filter, analyze, and visualize data. 

Structured query language (SQL) tools manage and extract data in relational databases. 

Data interpretation

After you analyze the data, you'll need to go back to the original question you posed and draw conclusions from your findings. Here are some common pitfalls to avoid:

Correlation vs. causation: Just because two variables are associated doesn't mean they're necessarily related or dependent on one another. 

Confirmation bias: This occurs when you interpret data in a way that confirms your own preconceived notions. To avoid this, have multiple people interpret the data. 

Small sample size: If your sample size is too small or doesn't represent the demographics of your customers, you may get misleading results. If you run into this, consider widening your sample size to give you a more accurate representation. 

Data visualization

Automate your data collection, frequently asked questions.

Need a quick summary or still have a few nagging data analysis questions? I'm here for you.

What are the five types of data analysis?

The five types of data analysis are text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Each type offers a unique lens for understanding data: text analysis provides insights into text-based content, statistical analysis focuses on numerical trends, diagnostic analysis looks into problem causes, predictive analysis deals with what may happen in the future, and prescriptive analysis gives actionable recommendations.

What is the data analysis process?

The data analysis process involves data decision, collection, cleaning, analysis, interpretation, and visualization. Every stage comes together to transform raw data into meaningful insights. Decision determines what data to collect, collection gathers the relevant information, cleaning ensures accuracy, analysis uncovers patterns, interpretation assigns meaning, and visualization presents the insights.

What is the main purpose of data analysis?

In business, the main purpose of data analysis is to uncover patterns, trends, and anomalies, and then use that information to make decisions, solve problems, and reach your business goals.

Related reading: 

This article was originally published in October 2022 and has since been updated with contributions from Cecilia Gillen. The most recent update was in September 2023.

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Shea Stevens

Shea is a content writer currently living in Charlotte, North Carolina. After graduating with a degree in Marketing from East Carolina University, she joined the digital marketing industry focusing on content and social media. In her free time, you can find Shea visiting her local farmers market, attending a country music concert, or planning her next adventure.

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What is Data Analysis? Research, Types & Example

What is Data Analysis?

Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.

A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis.

In this Data Science Tutorial, you will learn:

Why Data Analysis?

To grow your business even to grow in your life, sometimes all you need to do is Analysis!

If your business is not growing, then you have to look back and acknowledge your mistakes and make a plan again without repeating those mistakes. And even if your business is growing, then you have to look forward to making the business to grow more. All you need to do is analyze your business data and business processes.

Data Analysis Tools

Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Here is a complete list of tools used for data analysis in research.

Types of Data Analysis: Techniques and Methods

There are several types of Data Analysis techniques that exist based on business and technology. However, the major Data Analysis methods are:

Text Analysis

Statistical analysis, diagnostic analysis, predictive analysis, prescriptive analysis.

Text Analysis is also referred to as Data Mining. It is one of the methods of data analysis to discover a pattern in large data sets using databases or data mining tools . It used to transform raw data into business information. Business Intelligence tools are present in the market which is used to take strategic business decisions. Overall it offers a way to extract and examine data and deriving patterns and finally interpretation of the data.

Statistical Analysis shows “What happen?” by using past data in the form of dashboards. Statistical Analysis includes collection, Analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data. There are two categories of this type of Analysis – Descriptive Analysis and Inferential Analysis.

Descriptive Analysis

analyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.

Inferential Analysis

analyses sample from complete data. In this type of Analysis, you can find different conclusions from the same data by selecting different samples.

Diagnostic Analysis shows “Why did it happen?” by finding the cause from the insight found in Statistical Analysis. This Analysis is useful to identify behavior patterns of data. If a new problem arrives in your business process, then you can look into this Analysis to find similar patterns of that problem. And it may have chances to use similar prescriptions for the new problems.

Predictive Analysis shows “what is likely to happen” by using previous data. The simplest data analysis example is like if last year I bought two dresses based on my savings and if this year my salary is increasing double then I can buy four dresses. But of course it’s not easy like this because you have to think about other circumstances like chances of prices of clothes is increased this year or maybe instead of dresses you want to buy a new bike, or you need to buy a house!

So here, this Analysis makes predictions about future outcomes based on current or past data. Forecasting is just an estimate. Its accuracy is based on how much detailed information you have and how much you dig in it.

Prescriptive Analysis combines the insight from all previous Analysis to determine which action to take in a current problem or decision. Most data-driven companies are utilizing Prescriptive Analysis because predictive and descriptive Analysis are not enough to improve data performance. Based on current situations and problems, they analyze the data and make decisions.

Data Analysis Process

The Data Analysis Process is nothing but gathering information by using a proper application or tool which allows you to explore the data and find a pattern in it. Based on that information and data, you can make decisions, or you can get ultimate conclusions.

Data Analysis consists of the following phases:

Data Requirement Gathering

Data collection, data cleaning, data analysis, data interpretation, data visualization.

First of all, you have to think about why do you want to do this data analysis? All you need to find out the purpose or aim of doing the Analysis of data. You have to decide which type of data analysis you wanted to do! In this phase, you have to decide what to analyze and how to measure it, you have to understand why you are investigating and what measures you have to use to do this Analysis.

After requirement gathering, you will get a clear idea about what things you have to measure and what should be your findings. Now it’s time to collect your data based on requirements. Once you collect your data, remember that the collected data must be processed or organized for Analysis. As you collected data from various sources, you must have to keep a log with a collection date and source of the data.

Now whatever data is collected may not be useful or irrelevant to your aim of Analysis, hence it should be cleaned. The data which is collected may contain duplicate records, white spaces or errors. The data should be cleaned and error free. This phase must be done before Analysis because based on data cleaning, your output of Analysis will be closer to your expected outcome.

Once the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate data, you may find you have the exact information you need, or you might need to collect more data. During this phase, you can use data analysis tools and software which will help you to understand, interpret, and derive conclusions based on the requirements.

After analyzing your data, it’s finally time to interpret your results. You can choose the way to express or communicate your data analysis either you can use simply in words or maybe a table or chart. Then use the results of your data analysis process to decide your best course of action.

Data visualization is very common in your day to day life; they often appear in the form of charts and graphs. In other words, data shown graphically so that it will be easier for the human brain to understand and process it. Data visualization often used to discover unknown facts and trends. By observing relationships and comparing datasets, you can find a way to find out meaningful information.

• Data analysis means a process of cleaning, transforming and modeling data to discover useful information for business decision-making
• Types of Data Analysis are Text, Statistical, Diagnostic, Predictive, Prescriptive Analysis
• Data Analysis consists of Data Requirement Gathering, Data Collection, Data Cleaning, Data Analysis, Data Interpretation, Data Visualization
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Table of Contents

What is data analysis, what is the data analysis process, why is data analysis important, data analysis methods with examples, applications of data analysis, top data analysis techniques to analyze data, what is the importance of data analysis in research, future trends in data analysis, choose the right program, what is data analysis: a comprehensive guide.

Analysis involves breaking down a whole into its parts for detailed study. Data analysis is the practice of transforming raw data into actionable insights for informed decision-making. It involves collecting and examining data to answer questions, validate hypotheses, or refute theories.

In the contemporary business landscape, gaining a competitive edge is imperative, given the challenges such as rapidly evolving markets, economic unpredictability, fluctuating political environments, capricious consumer sentiments, and even global health crises. These challenges have reduced the room for error in business operations. For companies striving not only to survive but also to thrive in this demanding environment, the key lies in embracing the concept of data analysis . This involves strategically accumulating valuable, actionable information, which is leveraged to enhance decision-making processes.

If you're interested in forging a career in data analysis and wish to discover the top data analysis courses in 2024, we invite you to explore our informative video. It will provide insights into the opportunities to develop your expertise in this crucial field.

Data analysis inspects, cleans, transforms, and models data to extract insights and support decision-making. As a data analyst , your role involves dissecting vast datasets, unearthing hidden patterns, and translating numbers into actionable information.

The data analysis process is a structured sequence of steps that lead from raw data to actionable insights. Here are the answers to what is data analysis:

• Data Collection: Gather relevant data from various sources, ensuring data quality and integrity.
• Data Cleaning: Identify and rectify errors, missing values, and inconsistencies in the dataset. Clean data is crucial for accurate analysis.
• Exploratory Data Analysis (EDA): Conduct preliminary analysis to understand the data's characteristics, distributions, and relationships. Visualization techniques are often used here.
• Data Transformation: Prepare the data for analysis by encoding categorical variables, scaling features, and handling outliers, if necessary.
• Model Building: Depending on the objectives, apply appropriate data analysis methods, such as regression, clustering, or deep learning.
• Model Evaluation: Depending on the problem type, assess the models' performance using metrics like Mean Absolute Error, Root Mean Squared Error , or others.
• Interpretation and Visualization: Translate the model's results into actionable insights. Visualizations, tables, and summary statistics help in conveying findings effectively.
• Deployment: Implement the insights into real-world solutions or strategies, ensuring that the data-driven recommendations are implemented.

Data analysis plays a pivotal role in today's data-driven world. It helps organizations harness the power of data, enabling them to make decisions, optimize processes, and gain a competitive edge. By turning raw data into meaningful insights, data analysis empowers businesses to identify opportunities, mitigate risks, and enhance their overall performance.

1. Informed Decision-Making

Data analysis is the compass that guides decision-makers through a sea of information. It enables organizations to base their choices on concrete evidence rather than intuition or guesswork. In business, this means making decisions more likely to lead to success, whether choosing the right marketing strategy, optimizing supply chains, or launching new products. By analyzing data, decision-makers can assess various options' potential risks and rewards, leading to better choices.

2. Improved Understanding

Data analysis provides a deeper understanding of processes, behaviors, and trends. It allows organizations to gain insights into customer preferences, market dynamics, and operational efficiency .

3. Competitive Advantage

Organizations can identify opportunities and threats by analyzing market trends, consumer behavior , and competitor performance. They can pivot their strategies to respond effectively, staying one step ahead of the competition. This ability to adapt and innovate based on data insights can lead to a significant competitive advantage.

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4. Risk Mitigation

Data analysis is a valuable tool for risk assessment and management. Organizations can assess potential issues and take preventive measures by analyzing historical data. For instance, data analysis detects fraudulent activities in the finance industry by identifying unusual transaction patterns. This not only helps minimize financial losses but also safeguards the reputation and trust of customers.

5. Efficient Resource Allocation

Data analysis helps organizations optimize resource allocation. Whether it's allocating budgets, human resources, or manufacturing capacities, data-driven insights can ensure that resources are utilized efficiently. For example, data analysis can help hospitals allocate staff and resources to the areas with the highest patient demand, ensuring that patient care remains efficient and effective.

6. Continuous Improvement

Data analysis is a catalyst for continuous improvement. It allows organizations to monitor performance metrics, track progress, and identify areas for enhancement. This iterative process of analyzing data, implementing changes, and analyzing again leads to ongoing refinement and excellence in processes and products.

Descriptive Analysis

Descriptive analysis involves summarizing and organizing data to describe the current situation. It uses measures like mean, median, mode, and standard deviation to describe the main features of a data set.

Example: A company analyzes sales data to determine the monthly average sales over the past year. They calculate the mean sales figures and use charts to visualize the sales trends.

Diagnostic Analysis

Diagnostic analysis goes beyond descriptive statistics to understand why something happened. It looks at data to find the causes of events.

Example: After noticing a drop in sales, a retailer uses diagnostic analysis to investigate the reasons. They examine marketing efforts, economic conditions, and competitor actions to identify the cause.

Predictive Analysis

Predictive analysis uses historical data and statistical techniques to forecast future outcomes. It often involves machine learning algorithms.

Example: An insurance company uses predictive analysis to assess the risk of claims by analyzing historical data on customer demographics, driving history, and claim history.

Prescriptive Analysis

Prescriptive analysis recommends actions based on data analysis. It combines insights from descriptive, diagnostic, and predictive analyses to suggest decision options.

Example: An online retailer uses prescriptive analysis to optimize its inventory management . The system recommends the best products to stock based on demand forecasts and supplier lead times.

Quantitative Analysis

Quantitative analysis involves using mathematical and statistical techniques to analyze numerical data.

Example: A financial analyst uses quantitative analysis to evaluate a stock's performance by calculating various financial ratios and performing statistical tests.

Qualitative Research

Qualitative research focuses on understanding concepts, thoughts, or experiences through non-numerical data like interviews, observations, and texts.

Example: A researcher interviews customers to understand their feelings and experiences with a new product, analyzing the interview transcripts to identify common themes.

Time Series Analysis

Time series analysis involves analyzing data points collected or recorded at specific time intervals to identify trends , cycles, and seasonal variations.

Example: A climatologist studies temperature changes over several decades using time series analysis to identify patterns in climate change.

Regression Analysis

Regression analysis assesses the relationship between a dependent variable and one or more independent variables.

Example: An economist uses regression analysis to examine the impact of interest, inflation, and employment rates on economic growth.

Cluster Analysis

Cluster analysis groups data points into clusters based on their similarities.

Example: A marketing team uses cluster analysis to segment customers into distinct groups based on purchasing behavior, demographics, and interests for targeted marketing campaigns.

Sentiment Analysis

Sentiment analysis identifies and categorizes opinions expressed in the text to determine the sentiment behind it (positive, negative, or neutral).

Example: A social media manager uses sentiment analysis to gauge public reaction to a new product launch by analyzing tweets and comments.

Factor Analysis

Factor analysis reduces data dimensions by identifying underlying factors that explain the patterns observed in the data.

Example: A psychologist uses factor analysis to identify underlying personality traits from a large set of behavioral variables.

Statistics involves the collection, analysis, interpretation, and presentation of data.

Example: A researcher uses statistics to analyze survey data, calculate the average responses, and test hypotheses about population behavior.

Content Analysis

Content analysis systematically examines text, images, or media to quantify and analyze the presence of certain words, themes, or concepts.

Example: A political scientist uses content analysis to study election speeches and identify common themes and rhetoric from candidates.

Monte Carlo Simulation

Monte Carlo simulation uses random sampling and statistical modeling to estimate mathematical functions and mimic the operation of complex systems.

Example: A financial analyst uses Monte Carlo simulation to assess a portfolio's risk by simulating various market scenarios and their impact on asset prices.

Cohort Analysis

Cohort analysis studies groups of people who share a common characteristic or experience within a defined time period to understand their behavior over time.

Example: An e-commerce company conducts cohort analysis to track the purchasing behavior of customers who signed up in the same month to identify retention rates and revenue trends.

Grounded Theory

Grounded theory involves generating theories based on systematically gathered and analyzed data through the research process.

Example: A sociologist uses grounded theory to develop a theory about social interactions in online communities by analyzing participant observations and interviews.

Text Analysis

Text analysis involves extracting meaningful information from text through techniques like natural language processing (NLP).

Example: A customer service team uses text analysis to automatically categorize and prioritize customer support emails based on the content of the messages.

Data Mining

Data mining involves exploring large datasets to discover patterns, associations, or trends that can provide actionable insights.

Example: A retail company uses data mining to identify purchasing patterns and recommend products to customers based on their previous purchases.

Decision-Making

Decision-making involves choosing the best course of action from available options based on data analysis and evaluation.

Example: A manager uses data-driven decision-making to allocate resources efficiently by analyzing performance metrics and cost-benefit analyses.

Neural Network

A neural network is a computational model inspired by the human brain used in machine learning to recognize patterns and make predictions.

Example: A tech company uses neural networks to develop a facial recognition system that accurately identifies individuals from images.

Data Cleansing

Data cleansing involves identifying and correcting inaccuracies and inconsistencies in data to improve its quality.

Example: A data analyst cleans a customer database by removing duplicates, correcting typos, and filling in missing values.

Narrative Analysis

Narrative analysis examines stories or accounts to understand how people make sense of events and experiences.

Example: A researcher uses narrative analysis to study patients' stories about their experiences with healthcare to identify common themes and insights into patient care.

Data Collection

Data collection is the process of gathering information from various sources to be used in analysis.

Example: A market researcher collects data through surveys, interviews, and observations to study consumer preferences.

Data Interpretation

Data interpretation involves making sense of data by analyzing and drawing conclusions from it.

Example: After analyzing sales data, a manager interprets the results to understand the effectiveness of a recent marketing campaign and plans future strategies based on these insights.

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Data analysis is a versatile and indispensable tool that finds applications across various industries and domains. Its ability to extract actionable insights from data has made it a fundamental component of decision-making and problem-solving. Let's explore some of the key applications of data analysis:

1. Business and Marketing

• Market Research: Data analysis helps businesses understand market trends, consumer preferences, and competitive landscapes. It aids in identifying opportunities for product development, pricing strategies, and market expansion.
• Sales Forecasting: Data analysis models can predict future sales based on historical data, seasonality, and external factors. This helps businesses optimize inventory management and resource allocation.

2. Healthcare and Life Sciences

• Disease Diagnosis: Data analysis is vital in medical diagnostics, from interpreting medical images (e.g., MRI, X-rays) to analyzing patient records. Machine learning models can assist in early disease detection.
• Drug Discovery: Pharmaceutical companies use data analysis to identify potential drug candidates, predict their efficacy, and optimize clinical trials.
• Genomics and Personalized Medicine: Genomic data analysis enables personalized treatment plans by identifying genetic markers that influence disease susceptibility and response to therapies.
• Risk Management: Financial institutions use data analysis to assess credit risk, detect fraudulent activities, and model market risks.
• Algorithmic Trading: Data analysis is integral to developing trading algorithms that analyze market data and execute trades automatically based on predefined strategies.
• Fraud Detection: Credit card companies and banks employ data analysis to identify unusual transaction patterns and detect fraudulent activities in real-time.

4. Manufacturing and Supply Chain

• Quality Control: Data analysis monitors and controls product quality on manufacturing lines. It helps detect defects and ensure consistency in production processes.
• Inventory Optimization: By analyzing demand patterns and supply chain data, businesses can optimize inventory levels, reduce carrying costs, and ensure timely deliveries.

5. Social Sciences and Academia

• Social Research: Researchers in social sciences analyze survey data, interviews, and textual data to study human behavior, attitudes, and trends. It helps in policy development and understanding societal issues.
• Academic Research: Data analysis is crucial to scientific physics, biology, and environmental science research. It assists in interpreting experimental results and drawing conclusions.

6. Internet and Technology

• Search Engines: Google uses complex data analysis algorithms to retrieve and rank search results based on user behavior and relevance.
• Recommendation Systems: Services like Netflix and Amazon leverage data analysis to recommend content and products to users based on their past preferences and behaviors.

7. Environmental Science

• Climate Modeling: Data analysis is essential in climate science. It analyzes temperature, precipitation, and other environmental data. It helps in understanding climate patterns and predicting future trends.
• Environmental Monitoring: Remote sensing data analysis monitors ecological changes, including deforestation, water quality, and air pollution.

1. Descriptive Statistics

Descriptive statistics provide a snapshot of a dataset's central tendencies and variability. These techniques help summarize and understand the data's basic characteristics.

2. Inferential Statistics

Inferential statistics involve making predictions or inferences based on a sample of data. Techniques include hypothesis testing, confidence intervals, and regression analysis. These methods are crucial for drawing conclusions from data and assessing the significance of findings.

3. Regression Analysis

It explores the relationship between one or more independent variables and a dependent variable. It is widely used for prediction and understanding causal links. Linear, logistic, and multiple regression are common in various fields.

4. Clustering Analysis

It is an unsupervised learning method that groups similar data points. K-means clustering and hierarchical clustering are examples. This technique is used for customer segmentation, anomaly detection, and pattern recognition.

5. Classification Analysis

Classification analysis assigns data points to predefined categories or classes. It's often used in applications like spam email detection, image recognition, and sentiment analysis. Popular algorithms include decision trees, support vector machines, and neural networks.

6. Time Series Analysis

Time series analysis deals with data collected over time, making it suitable for forecasting and trend analysis. Techniques like moving averages, autoregressive integrated moving averages (ARIMA), and exponential smoothing are applied in fields like finance, economics, and weather forecasting.

7. Text Analysis (Natural Language Processing - NLP)

Text analysis techniques, part of NLP , enable extracting insights from textual data. These methods include sentiment analysis, topic modeling, and named entity recognition. Text analysis is widely used for analyzing customer reviews, social media content, and news articles.

8. Principal Component Analysis

It is a dimensionality reduction technique that simplifies complex datasets while retaining important information. It transforms correlated variables into a set of linearly uncorrelated variables, making it easier to analyze and visualize high-dimensional data.

9. Anomaly Detection

Anomaly detection identifies unusual patterns or outliers in data. It's critical in fraud detection, network security, and quality control. Techniques like statistical methods, clustering-based approaches, and machine learning algorithms are employed for anomaly detection.

10. Data Mining

Data mining involves the automated discovery of patterns, associations, and relationships within large datasets. Techniques like association rule mining, frequent pattern analysis, and decision tree mining extract valuable knowledge from data.

11. Machine Learning and Deep Learning

ML and deep learning algorithms are applied for predictive modeling, classification, and regression tasks. Techniques like random forests, support vector machines, and convolutional neural networks (CNNs) have revolutionized various industries, including healthcare, finance, and image recognition.

12. Geographic Information Systems (GIS) Analysis

GIS analysis combines geographical data with spatial analysis techniques to solve location-based problems. It's widely used in urban planning, environmental management, and disaster response.

• Uncovering Patterns and Trends: Data analysis allows researchers to identify patterns, trends, and relationships within the data. By examining these patterns, researchers can better understand the phenomena under investigation. For example, in epidemiological research, data analysis can reveal the trends and patterns of disease outbreaks, helping public health officials take proactive measures.
• Testing Hypotheses: Research often involves formulating hypotheses and testing them. Data analysis provides the means to evaluate hypotheses rigorously. Through statistical tests and inferential analysis, researchers can determine whether the observed patterns in the data are statistically significant or simply due to chance.
• Making Informed Conclusions: Data analysis helps researchers draw meaningful and evidence-based conclusions from their research findings. It provides a quantitative basis for making claims and recommendations. In academic research, these conclusions form the basis for scholarly publications and contribute to the body of knowledge in a particular field.
• Enhancing Data Quality: Data analysis includes data cleaning and validation processes that improve the quality and reliability of the dataset. Identifying and addressing errors, missing values, and outliers ensures that the research results accurately reflect the phenomena being studied.
• Supporting Decision-Making: In applied research, data analysis assists decision-makers in various sectors, such as business, government, and healthcare. Policy decisions, marketing strategies, and resource allocations are often based on research findings.
• Identifying Outliers and Anomalies: Outliers and anomalies in data can hold valuable information or indicate errors. Data analysis techniques can help identify these exceptional cases, whether medical diagnoses, financial fraud detection, or product quality control.
• Revealing Insights: Research data often contain hidden insights that are not immediately apparent. Data analysis techniques, such as clustering or text analysis, can uncover these insights. For example, social media data sentiment analysis can reveal public sentiment and trends on various topics in social sciences.
• Forecasting and Prediction: Data analysis allows for the development of predictive models. Researchers can use historical data to build models forecasting future trends or outcomes. This is valuable in fields like finance for stock price predictions, meteorology for weather forecasting, and epidemiology for disease spread projections.
• Optimizing Resources: Research often involves resource allocation. Data analysis helps researchers and organizations optimize resource use by identifying areas where improvements can be made, or costs can be reduced.
• Continuous Improvement: Data analysis supports the iterative nature of research. Researchers can analyze data, draw conclusions, and refine their hypotheses or research designs based on their findings. This cycle of analysis and refinement leads to continuous improvement in research methods and understanding.

Data analysis is an ever-evolving field driven by technological advancements. The future of data analysis promises exciting developments that will reshape how data is collected, processed, and utilized. Here are some of the key trends of data analysis:

1. Artificial Intelligence and Machine Learning Integration

Artificial intelligence (AI) and machine learning (ML) are expected to play a central role in data analysis. These technologies can automate complex data processing tasks, identify patterns at scale, and make highly accurate predictions. AI-driven analytics tools will become more accessible, enabling organizations to harness the power of ML without requiring extensive expertise.

2. Augmented Analytics

Augmented analytics combines AI and natural language processing (NLP) to assist data analysts in finding insights. These tools can automatically generate narratives, suggest visualizations, and highlight important trends within data. They enhance the speed and efficiency of data analysis, making it more accessible to a broader audience.

3. Data Privacy and Ethical Considerations

As data collection becomes more pervasive, privacy concerns and ethical considerations will gain prominence. Future data analysis trends will prioritize responsible data handling, transparency, and compliance with regulations like GDPR . Differential privacy techniques and data anonymization will be crucial in balancing data utility with privacy protection.

4. Real-time and Streaming Data Analysis

The demand for real-time insights will drive the adoption of real-time and streaming data analysis. Organizations will leverage technologies like Apache Kafka and Apache Flink to process and analyze data as it is generated. This trend is essential for fraud detection, IoT analytics, and monitoring systems.

5. Quantum Computing

It can potentially revolutionize data analysis by solving complex problems exponentially faster than classical computers. Although quantum computing is in its infancy, its impact on optimization, cryptography , and simulations will be significant once practical quantum computers become available.

6. Edge Analytics

With the proliferation of edge devices in the Internet of Things (IoT), data analysis is moving closer to the data source. Edge analytics allows for real-time processing and decision-making at the network's edge, reducing latency and bandwidth requirements.

7. Explainable AI (XAI)

Interpretable and explainable AI models will become crucial, especially in applications where trust and transparency are paramount. XAI techniques aim to make AI decisions more understandable and accountable, which is critical in healthcare and finance.

8. Data Democratization

The future of data analysis will see more democratization of data access and analysis tools. Non-technical users will have easier access to data and analytics through intuitive interfaces and self-service BI tools , reducing the reliance on data specialists.

9. Advanced Data Visualization

Data visualization tools will continue to evolve, offering more interactivity, 3D visualization, and augmented reality (AR) capabilities. Advanced visualizations will help users explore data in new and immersive ways.

10. Ethnographic Data Analysis

Ethnographic data analysis will gain importance as organizations seek to understand human behavior, cultural dynamics, and social trends. This qualitative data analysis approach and quantitative methods will provide a holistic understanding of complex issues.

11. Data Analytics Ethics and Bias Mitigation

Ethical considerations in data analysis will remain a key trend. Efforts to identify and mitigate bias in algorithms and models will become standard practice, ensuring fair and equitable outcomes.

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Having addressed the question of what is data analysis, if you're considering a career in data analytics, it's advisable to begin by researching the prerequisites for becoming a data analyst. You may also want to explore the Post Graduate Program in Data Analytics offered in collaboration with Purdue University. This program offers a practical learning experience through real-world case studies and projects aligned with industry needs. It provides comprehensive exposure to the essential technologies and skills currently employed in the field of data analytics.

Program Name Data Analyst Post Graduate Program In Data Analytics Data Analytics Bootcamp Geo All Geos All Geos US University Simplilearn Purdue Caltech Course Duration 11 Months 8 Months 6 Months Coding Experience Required No Basic No Skills You Will Learn 10+ skills including Python, MySQL, Tableau, NumPy and more Data Analytics, Statistical Analysis using Excel, Data Analysis Python and R, and more Data Visualization with Tableau, Linear and Logistic Regression, Data Manipulation and more Additional Benefits Applied Learning via Capstone and 20+ industry-relevant Data Analytics projects Purdue Alumni Association Membership Free IIMJobs Pro-Membership of 6 months Access to Integrated Practical Labs Caltech CTME Circle Membership Cost $$ $$$$ $$$$ Explore Program Explore Program Explore Program

1. What is the difference between data analysis and data science? 

Data analysis primarily involves extracting meaningful insights from existing data using statistical techniques and visualization tools. Whereas, data science encompasses a broader spectrum, incorporating data analysis as a subset while involving machine learning, deep learning, and predictive modeling to build data-driven solutions and algorithms.

2. What are the common mistakes to avoid in data analysis?

Common mistakes to avoid in data analysis include neglecting data quality issues, failing to define clear objectives, overcomplicating visualizations, not considering algorithmic biases, and disregarding the importance of proper data preprocessing and cleaning. Additionally, avoiding making unwarranted assumptions and misinterpreting correlation as causation in your analysis is crucial.

Data Science & Business Analytics Courses Duration and Fees

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Research: Using AI at Work Makes Us Lonelier and Less Healthy

• David De Cremer
• Joel Koopman

Employees who use AI as a core part of their jobs report feeling more isolated, drinking more, and sleeping less than employees who don’t.

The promise of AI is alluring — optimized productivity, lightning-fast data analysis, and freedom from mundane tasks — and both companies and workers alike are fascinated (and more than a little dumbfounded) by how these tools allow them to do more and better work faster than ever before. Yet in fervor to keep pace with competitors and reap the efficiency gains associated with deploying AI, many organizations have lost sight of their most important asset: the humans whose jobs are being fragmented into tasks that are increasingly becoming automated. Across four studies, employees who use it as a core part of their jobs reported feeling lonelier, drinking more, and suffering from insomnia more than employees who don’t.

Imagine this: Jia, a marketing analyst, arrives at work, logs into her computer, and is greeted by an AI assistant that has already sorted through her emails, prioritized her tasks for the day, and generated first drafts of reports that used to take hours to write. Jia (like everyone who has spent time working with these tools) marvels at how much time she can save by using AI. Inspired by the efficiency-enhancing effects of AI, Jia feels that she can be so much more productive than before. As a result, she gets focused on completing as many tasks as possible in conjunction with her AI assistant.

• David De Cremer is a professor of management and technology at Northeastern University and the Dunton Family Dean of its D’Amore-McKim School of Business. His website is daviddecremer.com .
• JK Joel Koopman is the TJ Barlow Professor of Business Administration at the Mays Business School of Texas A&M University. His research interests include prosocial behavior, organizational justice, motivational processes, and research methodology. He has won multiple awards from Academy of Management’s HR Division (Early Career Achievement Award and David P. Lepak Service Award) along with the 2022 SIOP Distinguished Early Career Contributions award, and currently serves on the Leadership Committee for the HR Division of the Academy of Management .

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• Artificial intelligence in healthcare

DrAfter123/DigitalVision Vectors

10 high-value use cases for predictive analytics in healthcare

Predictive analytics can support population health management, financial success, and better outcomes across the value-based care continuum..

• Editorial Staff

As healthcare organizations pursue improved care delivery and increased operational efficiency, digital transformation remains a key strategy to help achieve these goals. Many health systems’ digital transformation journey involves identifying the value of their data and capitalizing on that value through big data analytics.

Of the four types of healthcare data analytics , predictive analytics currently has some of the highest potential for value generation. This type of analytics goes beyond showing stakeholders what happened and why, allowing users to gain insight into what’s likely to happen based on historical data trends.

Being able to forecast potential future patterns has game-changing potential as healthcare organizations aim to move from reactive to proactive, but those looking to leverage predictive analytics must first define relevant use cases.

In this primer, HealthITAnalytics will outline 10 predictive analytics use cases, in alphabetical order, that health systems can pursue as part of a successful predictive analytics strategy .

1. CARE COORDINATION

Improved care coordination can bolster patient outcomes and satisfaction, and predictive analytics is one way healthcare organizations can enhance these efforts. Predictive analytics is beneficial in hospital settings, where care coordination staff are trying to prevent outcomes like patient deterioration and readmission while optimizing patient flow.

Some healthcare organizations are already beginning to see success after deploying advanced analytics to reduce hospital readmissions .

In June, a research team from New York University (NYU) Grossman School of Medicine successfully built a large language model (LLM) known as NYUTron to predict multiple outcomes, including readmissions and length of stay.

The tool, detailed in a Nature study , can accurately forecast 30-day all-cause readmission, in-hospital mortality, comorbidity index, length of stay, and insurance denials using unaltered electronic health record (EHR) data. At the time of the study’s publication, NYUTron could predict 80 percent of all-cause readmissions, a five percent improvement over existing models.

According to a December 2023 NEJM Catalyst study , predictive models deployed at Corewell Health have seen similar success, keeping 200 patients from being readmitted and resulting in a $5 million cost savings.

In a 2022 interview with HealthITAnalytics , leadership from Children’s of Alabama discussed how real-time risk prediction allows the health system to tackle patient deterioration and pursue intensive care unit (ICU) liberation.

Alongside its applications for inpatient care management, predictive analytics is particularly useful for other preventive care uses, such as disease detection.

2. EARLY DISEASE DETECTION

Effective disease management is vital to improving patient outcomes, but capturing and analyzing the necessary data only became plausible with the advent of predictive analytics.

Using predictive analytics for disease management requires healthcare organizations to pool extensive patient data — including EHRs, genomics, social determinants of health (SDOH), and other information — to identify relevant trends. These insights can then be used as a starting point to guide early disease detection and diagnosis efforts, anticipate disease progression, flag high-risk patients, and optimize treatment plans and resource allocation.

The promise of big data and predictive analytics is valuable in infectious disease monitoring.

In a February 2024 PLOS One study , researchers from the University of Virginia detailed the development of an online big data dashboard to track enteric infectious disease burden in low- and middle-income countries.

The dashboard is part of the Planetary Child Health & Enterics Observatory (Plan-EO) initiative, which aims to provide an evidence base to help geographically target child health interventions.

The dashboard will pull data from various sources to map transmission hotspots and predict outbreaks of diarrheal diseases, which public health stakeholders can use to better understand disease burden and guide decision-making.

The impacts of infectious disease are often inequitable, which may lead some to question the role that predictive analytics plays in concerns about health equity. Like any advanced data analytics approach, these tools must be used responsibly to avoid perpetuating health disparities, but when used responsibly, predictive tools can positively impact equity efforts.

3. HEALTH EQUITY

Care disparities, bias and health inequity are rampant in the United States healthcare system. Researchers and clinicians are on the front lines of efforts to ensure that patients receive equitable care, but doing so requires healthcare stakeholders to gain a deep, nuanced understanding of how factors like SDOH impact patients .

Predictive analytics can help draw a wealth of information from the large, complex data needed to guide these efforts.

The health of those in marginalized communities is disproportionately impacted by housing, care access, social isolation and loneliness , food insecurity, and other issues. Effectively capturing data on these phenomena and designing interventions to address them is challenging, but predictive analytics has already bolstered these efforts.

Recently, researchers from Cleveland Clinic and MetroHealth were awarded over $3 million from the National Institutes of Health (NIH) to develop a digital twin-based, neighborhood-focused model to reduce disparities.

The Digital Twin Neighborhoods project uses de-identified EHR data to design digital replicas of real communities served by both organizations. Experts on the project indicated that by pulling geographic, biological, and SDOH information, researchers can better understand place-based health disparities.

Models developed using these data can simulate life course outcomes in a community. Tools that accurately predict the outcomes observed within a population’s EHRs can inform health equity interventions.

In 2021, United Healthcare launched a predictive analytics-based advocacy program to help address SDOH and improve care for its members. The system uses machine learning to identify individuals who may need social services support.

These insights are incorporated into an agent dashboard that member advocates can use, alongside more traditional tools like questionnaires, to gather more information from the patient about their situation. If necessary, the advocate connects the individual with support mechanisms.

Efforts like these also demonstrate the utility of predictive analytics tools in patient and member engagement.

4. PATIENT ENGAGEMENT

Patient engagement plays a vital role in enhancing healthcare delivery. The advent of big data analytics in healthcare provides many opportunities for stakeholders to actively involve patients in their care.

Predictive analytics has shown promise in allowing health systems to proactively address barriers to patient engagement, such as appointment no-shows and medication adherence.

In a 2021 interview with HealthITAnalytics , Community Health Network leadership detailed how the health system bolsters its engagement efforts by using predictive analytics to reduce appointment no-shows and conduct post-discharge outreach.

A key aspect of this strategy is meeting patients where they are to effectively individualize their care journeys and improve their outcomes.

Appointment no-shows present a significant hurdle to achieving these aims, leading Community Health Network to implement automated, text message-based appointment reminders, with plans to deploy a two-way communication system to streamline the appointment scheduling process further.

The health system took a similar approach to post-discharge outreach, successfully deploying an automated solution during the COVID-19 pandemic.

To further enhance these systems, Community Health Network turned to predictive analytics.  By integrating a predictive algorithm into existing workflows, the health system could personalize outreach for appointment no-shows. Patients at low risk for no-shows may receive only one text message, but those at higher risk receive additional support, including outreach to determine whether unmet needs that the health system can help address are preventing them from making it to appointments.

Data analytics can also support medication adherence strategies by identifying non-adherence or predicting poor adherence.

One 2020 study published in Psychiatry Research showed that machine learning models can “accurately predict rates of medication adherence of [greater than or equal to 80 percent] across a clinical trial, adherence over the subsequent week, and adherence the subsequent day” among a large cohort of participants with a variety of conditions.

Research published in the March 2020 issue of BMJ Open Diabetes Research & Care found that a machine learning model tasked with identifying type 2 diabetes patients at high risk of medication nonadherence was accurate and sensitive, achieving good performance.

Outside the clinical sphere, predictive analytics is also useful for helping organizations like payers meet their strategic goals.

5. PAYER FORECASTING

Payers are an integral part of the US healthcare system. As payer organizations work with providers to guide members' care journeys, they generate a wealth of data that provides insights into healthcare utilization, costs, and outcomes.

Predictive analytics can help transform these data and inform efforts to improve payer forecasting . With historical data, payers can use predictive modeling to identify care management trends, forecast membership shifts, project enrollment churn, and pinpoint changes in service demand, among other uses.

In June 2023, leaders from Elevance Health discussed how the payer’s emphasis on predictive analytics is key to improving member outcomes.

Elevance utilizes a predictive algorithm to personalize member experience by addressing diabetes management and fall risk. The predictive model pulls clinical indicators like demographics, comorbidities, and A1C levels to forecast future A1C patterns and identify individuals with uncontrolled or poorly controlled diabetes.

From there, the payer can help members manage their condition through at-home lab A1C test kits and increased member and care team engagement.

The second predictive tool incorporates data points — including past diagnoses, procedures, and medications, the presence of musculoskeletal-related conditions and connective tissue disorders, analgesic or opioid drug usage, and frailty indicators — to flag women over the age of 65 at higher risk of fracture from a fall.

Elevance then conducts outreach to these individuals to recommend bone density scans and other interventions to improve outcomes.

These efforts are one example of how predictive analytics can improve the health of specific populations, but these tools can also be applied to population health more broadly.

6. POPULATION HEALTH

While much of healthcare is concerned with improving individual patients’ well-being, advancing the health of populations is extremely valuable for boosting health outcomes on a large scale. To that end, many healthcare organizations are pursuing data-driven population health management .

Predictive analytics tools can enhance these initiatives by guiding large-scale efforts in chronic disease management and population-wide care coordination.

In one 2021 American Journal of Preventive Medicine study , a research team from New York University’s School of Global Public Health and Tandon School showed that machine learning-driven models incorporating SDOH data can accurately predict cardiovascular disease burden. Further, insights from these tools can guide treatment recommendations.

The early identification of chronic disease risk is also helpful in informing preventive care interventions and flagging gaps in care .

Being closely related to population health , public health can also benefit from applying predictive analytics.

Researchers from the Center for Neighborhood Knowledge at UCLA Luskin, writing in the International Journal of Environmental Health in 2021, detailed how a predictive model successfully helped them identify which neighborhoods in Los Angeles County were at the greatest risk for COVID-19 infections.

The tool mapped the county on a neighborhood-by-neighborhood basis to evaluate residents’ vulnerability to infection using four indicators: barriers to accessing health care, socioeconomic challenges, built-environment characteristics, and preexisting medical conditions.

The model allowed stakeholders to harness existing local data to guide public health decision-making, prioritize vulnerable populations for vaccination, and prevent new COVID-19 infections.

Alongside large-scale initiatives like these, predictive modeling can also support the advancement of precision medicine.

7. PRECISION MEDICINE

The emergence of genomics and big data analytics has opened new doors in the realm of tailored health interventions. Precision and personalized medicine rely on individual patients’ data points to guide their care and improve their well-being.

From cancer to genetic conditions, predictive analytics is a crucial aspect of precision medicine.

In 2021, a meta-analysis presented at the American Society for Radiation Oncology (ASTRO) Annual Meeting showed that a genetic biomarker test could accurately predict treatment response in men with high-risk prostate cancer.

The test analyzes gene activity in prostate tumors to generate a score to represent the aggressiveness of a patient’s cancer. These insights can be used to personalize treatment plans that balance survival risk with quality of life.

Researchers from Arizona State University (ASU) revealed in a 2024 Cell Systems paper that they developed a machine learning model to predict how a patient’s immune system will respond to foreign pathogens.

The tool uses information on individualized molecular interactions to characterize how major histocompatibility complex-1 (MHC-1) proteins — key players in the body’s ability to recognize foreign cells — impact immune response.

MHC-1s exist on the cell surface and bind foreign peptides to present to the immune system for recognition and attack. These proteins also come in thousands of varieties across the human genome, making it difficult to forecast how various MHC-1s interact with a given pathogen.

The ASU research addressed this by analyzing just under 6,000 MHC-1 alleles, shedding light on how these molecules interact with peptides and revealing that individuals with a diverse range of MHC-1s were more likely to survive cancer treatment.

Using the model, providers could potentially forecast pathological outcomes for patients, bolstering treatment planning and clinical decision-making.

In addition to these successes at the microscopic level, predictive analytics is also useful on the macro level in healthcare.

8. RESOURCE ALLOCATION AND SUPPLY CHAIN

Optimization of the supply chain and resource allocation ensures that providers and patients receive the equipment, medications, and other tools that they need to support positive outcomes. Data analytics plays a massive role in this, as supply chain management and resource use rely heavily on accurately recording and tracking resources as they move from the assembly line into the clinical setting.

Predictive analytics takes this one step further by helping stakeholders anticipate and address supply chain issues before they arise while optimizing resource use.

Seattle Children's Hospital is using predictive modeling in the form of digital twins to help the health system streamline hospital operations , particularly resource allocation.

By using digital twin simulation to “clone” the hospital, stakeholders can model how certain events, strategies, or policies might impact operational efficiency. This capability was critical in the wake of COVID-19, as it allowed the health system to identify how rapidly its personal protective equipment (PPE) supplies would diminish, forecast bed capacity, and generate insights around labor resources.

Predictive analytics can also be used by distinct parts of the supply chain to help prevent shortages.

The 2022 infant formula shortage is one example of how supply chain disruptions can significantly impact health.

One potential way for parents to deal with the formula shortage was to turn to human breast milk banks, which distribute donated milk to vulnerable babies and their families. However, accomplishing this vital work requires milk banks to effectively screen donors, accept donations, process and test them to ensure they’re safe, and dispense them.

In an interview with HealthITAnalytics , stakeholders from Mothers' Milk Bank at WakeMed Health & Hospitals described how data analytics can help optimize aspects of this process.

A crucial part of ensuring that milk is available to those who need it is tracking milk waste. Milk can be wasted for various reasons, but the presence of bacteria is one of the primary causes. To address this, the milk bank began analyzing donor records to determine what factors may make a batch of milk more likely to test positive for bacillus .

The milk bank can then use the insights generated from the analysis to predict which donors may be at high risk for having bacillus in their milk, allowing milk from these individuals to be tested separately. This removes any bacillus -positive samples before the milk is pooled for processing.

Predictive analytics is also helpful in assessing and managing risks in clinical settings.

9. RISK STRATIFICATION

Patient risk scores have the potential to improve care management initiatives, as they allow providers to formulate improved prevention strategies to eliminate or reduce adverse outcomes. Risk scores are used to help understand what characteristics may make a patient more susceptible to various conditions.

From there, the scores can inform risk stratification efforts, which enables health systems to categorize patients based on whether they are low-, medium- or high-risk. These data can show how one or more factors increase a patient's risk.

Risk stratification is one of the most valuable use cases for predictive analytics because of its ability to prevent adverse outcomes.

In February 2024, leaders from Parkland Health & Hospital System (PHHS) and Parkland Center for Clinical Innovation (PCCI) in Dallas, Texas, detailed one of these high-value use cases.

Parkland’s Universal Suicide Screening Program is an initiative designed to flag patients at risk of suicide who may have flown under the health system’s radar through proactive screening of all Parkland patients aged 10 or older, regardless of the reason for the clinical encounter.

During the encounter, nursing staff ask the patient a set of standardized, validated questions to assess their suicide risk. This information is then incorporated into the EHR for risk stratification.

These data are useful for stakeholders looking to better understand patients’ stories, including factors like healthcare utilization before suicide. Coupling these insights with state mortality could help predict and prevent suicide in the future.

Risk stratification is also crucial for improving outcomes for some of the youngest, most vulnerable patients: newborns.

Parkland also runs an initiative that uses SDOH data to identify at-risk pregnant patients and enable early interventions to help reduce preterm births .

The program’s risk prediction model and text message-based patient education program have been invaluable in understanding the nuances of preterm birth risk for Parkland patients. Major risk factors like cervical length and history of spontaneous preterm delivery may not be easy to determine for some patients. Further, many preterm births appear to be associated with additional risk factors outside of these – like prenatal visit attendance.

Using these additional factors to forecast risk, Parkland has developed clinical- and population-level interventions that have resulted in a 20 percent reduction in preterm births.

These use cases, among other things, demonstrate the key role predictive analytics can play in advancing value-based care.

10. VALUE-BASED CARE SUCCESS

Value-based care incentivizes healthcare providers to improve care quality and delivery by linking reimbursement to patient outcomes. To achieve value-based care success, providers rely on a host of tools: health information exchange (HIE), data analytics, artificial intelligence (AI) and machine learning (ML), population health management solutions, and price transparency technologies.

Predictive analytics can be utilized alongside these tools to drive long-term success for healthcare organizations pursuing value-based care.

Accountable care organizations (ACOs) are significant players in the value-based care space, and predictive modeling has already helped some achieve their goals in this area.

Buena Vida y Salud ACO partnered with the Health Data Analytics Institute (HDAI) in 2023 to explore how predictive analytics could help the organization keep patients healthy at home.

At the outset of the collaboration, the ACO’s leadership team was presented with multiple potential use cases in which data analysis could help with unplanned admissions, worsening heart failure, pneumonia development, and more.

However, providers were overwhelmed when given risk-stratified patient lists for multiple use cases. Upon working with its providers, the ACO found that allowing clinicians to choose the use cases or patient cohorts they wanted to focus on was much more successful.

The approach has helped the ACO engage its providers and enhance care management efforts through predictive modeling and digital twins. These tools provide fine-grain insights into the drivers of outcomes like pneumonia-related hospitalization, which guide the development of care management interventions.

These 10 use cases are just the beginning of predictive analytics' potential to transform healthcare. As data analytics technologies like AI, ML and digital twins continue to advance, the value of predictive analytics is likely to increase exponentially.

What Are the Benefits of Predictive Analytics in Healthcare?

• How Can Predictive Analytics Help ACOs Boost Value-Based Care Delivery?
• Putting the Pieces Together for a Successful Predictive Analytics Strategy

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Forest Inventory and Analysis

For nearly 100 years, the FIA program has been recognized as a world leader in conducting national-scale forest inventories. FIA information is widely used to address local and regional issues related to trends in forest extent, health and productivity; land cover and land use change; and the changing demographics of private forest landowners.

• Nationwide Forest Inventory
• National Resource Use Monitoring
• National Woodland Owner Survey
• Urban Forest Inventory and Analysis
• Forest Definitions
• Tree Volume, Biomass, and Carbon Models
• Technology Transfer
• USDA Climate Hubs
• Laboratories
• Centers and Groups
• Experimental Forests and Ranges
• Urban Field Stations

The Forest Inventory and Analysis (FIA) program of the USDA Forest Service Research and Development Branch collects, processes, analyzes, and reports on data necessary for assessing the extent and condition of forest resources in the United States.

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What does the Forest Inventory and Analysis Program do? 

Forest Inventory and Analysis (FIA) is a congressionally mandated program that delivers current, consistent, and credible information about the status of forests and forest resources within the United States by continually collecting and analyzing data about these forests and the values they provide. FIA works to: 

• Collect annualized data relating to forest resources, health and ownership.
• Collect and analyze a consistent core set of ecological data on all forests to view trends over time. 
• Utilize new and emerging technologies to acquire data through remote sensing and field activities. 

FIA completes data collection and analysis work within four main inventories: 

• The Nationwide Forest Inventory (NFI) : a network of permanent plots, located in non-urban areas that are forested (or capable of being forested). NFI plots are remeasured every 5-10 years depending on location. Information on the site, land use, trees, and both standing and dead trees is collected on all plots. Additionally, information about down woody material, soils and understory vegetation is collected on a subset of plots.
• The National Resource Use Monitoring (NRUM) : a survey which collects information on manufacturers that use harvested wood products for reporting on size of facilities, products that are manufactured, manufacturing capacity, and other data points. 
• The National Woodland Owners Survey (NWOS) : a survey that collects information on private forest landowners to understand why they own the land, what they use it for, and how they are planning on managing their land over time.
• Urban Inventory : an inventory program that monitors the Nation’s urban forests, examines social dimensions of urban forests and green spaces, and estimates the industrial and nonindustrial uses of urban wood. 

FIA also works with experts from universities and trusted partners to expand research capacity, analytical capabilities, and continually develop and enhance our inventory and monitoring techniques within these inventories. 

Program Deliverables 

Information and trends are important indicators of the conservation and sustainable management of United States forests, and these trends provide policymakers, partners, and other users a variety of data that inform their land-management decisions over time.  Our users can rely on the credibility of our information to make critical land management, policy, and investment decisions. Data outputs include, but are not limited to: 

• Developing summaries and reports detailing forest health and productivity every five years. 
• Providing current and historical data across political and administrative boundaries and land ownerships, including urban forests.
• Developing data sets and analytical products that include a wide array of forest ecosystem parameters addressing the extent, productivity, health, ownership, and utilization of United States forests. 

FIA seeks to address emerging user needs by conducting development research in additional to its operational surveys. Current research focuses on seven strategic areas that were identified in previous legislation and the 2015 FIA Strategic Plan . Three of these research portfolios have successfully been operationalized and are each described above (NRUM, NWOS, and Urban). The other four research portfolios include: 

• developing estimation and accounting compilation systems and tools, 
• advancing carbon pool science (including harvested wood products)
• leveraging FIA remeasurements and auxiliary information for change estimation and attribution across spatial and temporal scales, and 
• information carbon management, mitigation, and adaptation activities.
• Digital Engagement: The mission of the digital engagement portfolio is to transform FIA's analysis, reporting, and delivery of information. This work is made possible through collaboration with agency and external partners to develop and publish relevant and authoritative data that supports user needs. To see examples of FIA's digital engagement work in action, visit the FIA Geospatial Showcase or learn about the BIGMAP project . 
• leverage FIA's unique dataset to lead national LULC monitoring,
• create a forum for FIA LULC experts to coordinate research within and outside of the program; and
• communicate new LULC research to FIA's customers. Visit the FIA Land Resources Explorer to view information on land use, land cover, and change in an interactive map. 
• a nationwide, experimental series of annual, county level forest area and biomass estimates by 2025, and
• area and biomass change by 2027. 

National Program Coordination

The FIA Program is implemented across four units located at USDA Forest Service Research Stations: The Northern Research Station, the Pacific Northwest Research Station, the Rocky Mountain Research Station, and the Southern Research Station. National teams of FIA specialists work together to ensure consistency and efficiency in data collection, management, and analysis. They review and implement modifications, additions, or deletions to any component of the National FIA Program. These teams cross four functional areas, known as Bands: 

• Data Acquisition Band: Focuses on standardization of FIA’s core field data collection across the US. This includes testing new protocols, developing documentation and training programs, and conducting quality assurance. For more information on data acquisition contact the Data Acquisition Band Lead,  Maryfaith Snyder. 
• Information Management Band: Focuses on data management systems for FIA data, including systems for data collection, data editing and validation, internal and public databases, and web applications that allow all users to access and analyze FIA data. For more information, contact the Information Management Band Lead,  Chad Keyser . 
• Analysis Band: Focuses on reporting and providing statistically defensible methods for summarizing FIA data, including identification of new variables (either field measured or computed) needed by FIA customers. For more information, contact the Analysis Band Lead,  Randy Morin . 
• Techniques Research Band: Focuses on improving the efficiency, timeliness, and quality of the FIA program through research that assesses and integrates new technologies and methodologies into current FIA workflows. The band addresses problem areas outlined in the FIA strategic plan or identified by Congress, FIA Program Managers for each unit, or National FIA leadership. For more information on the Techniques Research Band contact the Techniques Research Band Lead,  Hans Andersen .

Background Information and History

The FIA program concept is over 120 years old. The Organic Act of 1897, which established the National Forest System, included provisions for inventory and management of those lands. In 1928, the Forestry Research Act (McSweeney-McNary) directed the Secretary of Agriculture to make and keep current a comprehensive inventory and analysis of the Nation’s forest resources. The Resources Planning Act of 1974 (RPA, PL 93-378) amended the earlier research act. The Forest and Rangeland Renewable Resources Research Act of 1978 (PL 95-307) replaced earlier Forestry Research legislation but repeated the amendment contained in the RPA and further instructed the Secretary of Agriculture to:

 “...obtain, analyze, develop, demonstrate, and disseminate scientific information about protecting, managing, and utilizing forest and rangeland renewable resources in rural, suburban, and urban areas” 

The National Forest Management Act of 1976 (PL 94-588) directed the USDA Forest Service to:

 “ensure research on and (based upon continuous monitoring and assessment in the field) evaluation of the effects of each management system…” 

More recently, in 1999 (Farm Bill, Public Law 105-185) and again in 2014 (Farm Bill, Public Law 113-79), Congress directed the Forest Service to reevaluate its statewide inventory mission and to transition to survey each State annually rather than periodically, with the exception of Interior Alaska and U.S. associated islands of the Caribbean and Pacific Ocean. Additionally, FIA was directed to implement urban forest inventories, improve sub-state estimation precision, and improve the timber product output program among other provisions. In collaboration with partners, FIA developed strategic plans to fully transition into an annualized inventory and comply with other requirements. 

User Notifications and Bulletins

• FIADB v1.9.1 release is now available. EVALIDator and DATIM Live have been updated as well to accommodate the database changes. Updates include changes to cubic-foot volume, biomass, and carbon estimates. More information about those changes can be found here: Tree Volume, Biomass and Carbon Models.  
• June 21, 2022: Estimates and statistics based on Alaska borough and census areas may be misleading. FIA's Alaska inventory is ongoing and many survey units have yet to be sampled. The FIA inventory Alaska survey unit boundaries do not follow Alaska’s borough and census area boundaries or ecoregions. The survey units were outlined broadly encompassing major watershed boundaries. Because much of Alaska is not organized into a recognized borough (county equivalent), the FIA program utilizes the Census Bureau Census Area boundaries as the county equivalents in those areas. However, Alaskan borough and municipality boundaries have changed multiple times since the inception of the FIA annualized inventory (~2004). The Census Bureau has also frequently changed Alaskan Census Area boundaries over that same timeframe. Therefore, any FIA reported estimates reflect only the Alaska survey units involved. Any estimates and summary statistics calculated based on borough or Census Area spatial extents may be misleading due to the variability of these features. For more information, please see the supporting documentation here .

Upcoming Events

Forest inventory and analysis science symposium, november 19-21, 2024.

• The symposium offers an opportunity for scientific and technical exchange, drawing together a world-class group of partners, practitioners, and scientists with regional, national, and international inventory and monitoring missions. Find more information about the symposium HERE .

Work with Us

FIA work is coordinated and accomplished out of four regional units that cover the nation, including U.S. territories. Staff and contractors complete our work from different locations across the country. For more information on contracts for field work, contact the person associated with the location you are interested in. 

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Analysis of data suggests homosexual behavior in other animals is far more common than previously thought

by Bob Yirka , Phys.org

A team of anthropologists and biologists from Canada, Poland, and the U.S., working with researchers at the American Museum of Natural History, in New York, has found via meta-analysis of data from prior research efforts that homosexual behavior is far more common in other animals than previously thought. The paper is published in PLOS ONE .

For many years, the biology community has accepted the notion that homosexuality is less common in animals than in humans, despite a lack of research on the topic. In this new effort, the researchers sought to find out if such assumptions are true.

The work involved study of 65 studies into the behavior of multiple species of animals, mostly mammals, such as elephants, squirrels, monkeys, rats and racoons.

The researchers found that 76% of the studies mentioned observations of homosexual behavior, though they also noted that only 46% had collected data surrounding such behavior—and only 18.5% of those who had mentioned such behavior in their papers had focused their efforts on it to the extent of publishing work with homosexuality as it core topic.

They noted that homosexual behavior observed in other species included mounting, intromission and oral contact—and that researchers who identified as LGBTQ+ were no more or less likely to study the topic than other researchers.

The researchers point to a hesitancy in the biological community to study homosexuality in other species , and thus, little research has been conducted. They further suggest that some of the reluctance has been due to the belief that such behavior is too rare to warrant further study.

The research team suggests that homosexuality is far more common in the animal kingdom than has been reported—they further suggest more work is required regarding homosexual behaviors in other animals to dispel the myth of rarity.

Journal information: PLoS ONE

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Public Trust in Government: 1958-2024

Public trust in the federal government, which has been low for decades, has increased modestly since 2023 . As of April 2024, 22% of Americans say they trust the government in Washington to do what is right “just about always” (2%) or “most of the time” (21%). Last year, 16% said they trusted the government just about always or most of the time, which was among the lowest measures in nearly seven decades of polling.

When the National Election Study began asking about trust in government in 1958, about three-quarters of Americans trusted the federal government to do the right thing almost always or most of the time.

Trust in government began eroding during the 1960s, amid the escalation of the Vietnam War, and the decline continued in the 1970s with the Watergate scandal and worsening economic struggles.

Confidence in government recovered in the mid-1980s before falling again in the mid-’90s. But as the economy grew in the late 1990s, so too did trust in government. Public trust reached a three-decade high shortly after the 9/11 terrorist attacks but declined quickly after. Since 2007, the shares saying they can trust the government always or most of the time have not been higher than 30%.

Today, 35% of Democrats and Democratic-leaning independents say they trust the federal government just about always or most of the time, compared with 11% of Republicans and Republican leaners.

Democrats report slightly more trust in the federal government today than a year ago. Republicans’ views have been relatively unchanged over this period.

Since the 1970s, trust in government has been consistently higher among members of the party that controls the White House than among the opposition party.

Republicans have often been more reactive than Democrats to changes in political leadership, with Republicans expressing much lower levels of trust during Democratic presidencies. Democrats’ attitudes have tended to be somewhat more consistent, regardless of which party controls the White House.

However, Republican and Democratic shifts in attitudes from the end of Donald Trump’s presidency to the start of Joe Biden’s were roughly the same magnitude.

Among Asian, Hispanic and Black adults, 36%, 30% and 27% respectively say they trust the federal government “most of the time” or “just about always” – higher levels of trust than among White adults (19%).

During the last Democratic administration, Black and Hispanic adults similarly expressed more trust in government than White adults. Throughout most recent Republican administrations, White Americans were substantially more likely than Black Americans to express trust in the federal government to do the right thing.

Note: For full question wording, refer to the topline . White, Black and Asian American adults include those who report being one race and are not Hispanic. Hispanics are of any race. Estimates for Asian adults are representative of English speakers only.

Sources: Pew Research Center, National Election Studies, Gallup, ABC/Washington Post, CBS/New York Times, and CNN Polls. Data from 2020 and later comes from Pew Research Center’s online American Trends Panel; prior data is from telephone surveys. Details about changes in survey mode can be found in this 2020 report . Read more about the Center’s polling methodology . For analysis by party and race/ethnicity, selected datasets were obtained from searches of the iPOLL Databank provided by the Roper Center for Public Opinion Research .

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