Businesses today need every edge and advantage they can get. Thanks to obstacles like rapidly changing markets, economic uncertainty, shifting political landscapes, finicky consumer attitudes, and even global pandemics, businesses today are working with slimmer margins for error.

Companies that want to stay in business and thrive can improve their odds of success by making smart choices while answering the question: “What is data analysis?” And how does an individual or organization make these choices? They collect as much useful, actionable information as possible and then use it to make better-informed decisions!

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This strategy is common sense, and it applies to personal life as well as business. No one makes important decisions without first finding out what’s at stake, the pros and cons, and the possible outcomes. Similarly, no company that wants to succeed should make decisions based on bad data. Organizations need information; they need data. This is where data analysis or data analytics enters the picture.

The job of understanding data is currently one of the growing industries in today's day and age, where data is considered as the 'new oil' in the market. Now, before getting into the details about the data analysis methods, let us first answer the question, what is data analysis?

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What Is Data Analysis?

Although many groups, organizations, and experts have different ways of approaching data analysis, most of them can be distilled into a one-size-fits-all definition. Data analysis is the process of cleaning, changing, and processing raw data and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs.

A simple example of data analysis can be seen whenever we make a decision in our daily lives by evaluating what has happened in the past or what will happen if we make that decision. Basically, this is the process of analyzing the past or future and making a decision based on that analysis.

It’s not uncommon to hear the term “big data” brought up in discussions about data analysis. Data analysis plays a crucial role in processing big data into useful information. Neophyte data analysts who want to dig deeper by revisiting big data fundamentals should go back to the basic question, “What is data?”

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Why is Data Analysis Important?

Here is a list of reasons why data analysis is crucial to doing business today.

  • Better Customer Targeting: You don’t want to waste your business’s precious time, resources, and money putting together advertising campaigns targeted at demographic groups that have little to no interest in the goods and services you offer. Data analysis helps you see where you should be focusing your advertising and marketing efforts.
  • You Will Know Your Target Customers Better: Data analysis tracks how well your products and campaigns are performing within your target demographic. Through data analysis, your business can get a better idea of your target audience’s spending habits, disposable income, and most likely areas of interest. This data helps businesses set prices, determine the length of ad campaigns, and even help project the number of goods needed.
  • Reduce Operational Costs: Data analysis shows you which areas in your business need more resources and money, and which areas are not producing and thus should be scaled back or eliminated outright.
  • Better Problem-Solving Methods: Informed decisions are more likely to be successful decisions. Data provides businesses with information. You can see where this progression is leading. Data analysis helps businesses make the right choices and avoid costly pitfalls.
  • You Get More Accurate Data: If you want to make informed decisions, you need data, but there’s more to it. The data in question must be accurate. Data analysis helps businesses acquire relevant, accurate information, suitable for developing future marketing strategies, business plans, and realigning the company’s vision or mission.

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What Is the Data Analysis Process?

Answering the question “what is data analysis” is only the first step. Now we will look at how it’s performed. The process of data analysis, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights. The process of data analysis consists of:

  • Data Requirement Gathering

Ask yourself why you’re doing this analysis, what type of data you want to use, and what data you plan to analyze.

Guided by your identified requirements, it’s time to collect the data from your sources. Sources include case studies, surveys, interviews, questionnaires, direct observation, and focus groups. Make sure to organize the collected data for analysis.

Not all of the data you collect will be useful, so it’s time to clean it up. This process is where you remove white spaces, duplicate records, and basic errors. Data cleaning is mandatory before sending the information on for analysis.

  • Data Analysis

Here is where you use data analysis software and other tools to help you interpret and understand the data and arrive at conclusions. Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI.

  • Data Interpretation

Now that you have your results, you need to interpret them and come up with the best courses of action based on your findings.

  • Data Visualization

Data visualization is a fancy way of saying, “graphically show your information in a way that people can read and understand it.” You can use charts, graphs, maps, bullet points, or a host of other methods. Visualization helps you derive valuable insights by helping you compare datasets and observe relationships.

Types of Data Analysis

A half-dozen popular types of data analysis are available today, commonly employed in the worlds of technology and business. They are: 

  • Descriptive Analysis

Descriptive analysis involves summarizing and describing the main features of a dataset. It focuses on organizing and presenting the data in a meaningful way, often using measures such as mean, median, mode, and standard deviation. It provides an overview of the data and helps identify patterns or trends.

  • Inferential Analysis

Inferential analysis aims to make inferences or predictions about a larger population based on sample data. It involves applying statistical techniques such as hypothesis testing, confidence intervals, and regression analysis. It helps generalize findings from a sample to a larger population.

  • Exploratory Data Analysis (EDA)

EDA focuses on exploring and understanding the data without preconceived hypotheses. It involves visualizations, summary statistics, and data profiling techniques to uncover patterns, relationships, and interesting features. It helps generate hypotheses for further analysis.

  • Diagnostic Analysis

Diagnostic analysis aims to understand the cause-and-effect relationships within the data. It investigates the factors or variables that contribute to specific outcomes or behaviors. Techniques such as regression analysis, ANOVA (Analysis of Variance), or correlation analysis are commonly used in diagnostic analysis.

  • Predictive Analysis

Predictive analysis involves using historical data to make predictions or forecasts about future outcomes. It utilizes statistical modeling techniques, machine learning algorithms, and time series analysis to identify patterns and build predictive models. It is often used for forecasting sales, predicting customer behavior, or estimating risk.

  • Prescriptive Analysis

Prescriptive analysis goes beyond predictive analysis by recommending actions or decisions based on the predictions. It combines historical data, optimization algorithms, and business rules to provide actionable insights and optimize outcomes. It helps in decision-making and resource allocation.

Next, we will get into the depths to understand about the data analysis methods.

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Data Analysis Methods

Some professionals use the terms “data analysis methods” and “data analysis techniques” interchangeably. To further complicate matters, sometimes people throw in the previously discussed “data analysis types” into the fray as well! Our hope here is to establish a distinction between what kinds of data analysis exist, and the various ways it’s used.

Although there are many data analysis methods available, they all fall into one of two primary types: qualitative analysis and quantitative analysis.

  • Qualitative Data Analysis

The qualitative data analysis method derives data via words, symbols, pictures, and observations. This method doesn’t use statistics. The most common qualitative methods include:

  1. Content Analysis, for analyzing behavioral and verbal data.
  2. Narrative Analysis, for working with data culled from interviews, diaries, surveys.
  3. Grounded Theory, for developing causal explanations of a given event by studying and extrapolating from one or more past cases.
  • Quantitative Data Analysis

Also known as statistical data analysis methods collect raw data and process it into numerical data. Quantitative analysis methods include:

  1. Hypothesis Testing, for assessing the truth of a given hypothesis or theory for a data set or demographic.
  2. Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers by the number of items on the list.
  3. Sample Size Determination uses a small sample taken from a larger group of people and analyzed. The results gained are considered representative of the entire body. 

We can further expand our discussion of data analysis by showing various techniques, broken down by different concepts and tools. 

How to Analyze Data? Top Data Analysis Techniques to Apply

To analyze data effectively, you can apply various data analysis techniques. Here are some top techniques to consider:

  • Define Your Objectives

Clearly define the objectives of your data analysis. Understand the questions you want to answer or the insights you want to gain from the data. This will guide your analysis process.

  • Data Cleaning

Start by cleaning the data to ensure its quality and reliability. Remove duplicates, handle missing values, and correct any errors or inconsistencies. Data cleaning is crucial for accurate analysis.

  • Descriptive Statistics

Calculate descriptive statistics to understand the main characteristics of the data. Compute measures such as mean, median, mode, standard deviation, and percentiles. These statistics provide insights into the data's central tendency, spread, and distribution.

  • Data Visualization

Create visual representations of the data using charts, graphs, or plots. Visualization helps spot patterns, trends, or outliers that may not be immediately apparent in the raw data. Use appropriate visualizations based on the type of data and the insights you want to convey.

  • Exploratory Data Analysis (EDA)

Perform EDA techniques to explore the data deeply. Use data profiling, summary statistics, and visual exploration to identify patterns, relationships, or interesting features within the data. EDA helps generate hypotheses and guides further analysis.

  • Inferential Statistics

Apply inferential statistics to conclude the larger population based on sample data. Use techniques like hypothesis testing, confidence intervals, and regression analysis to test relationships, make predictions, or assess the significance of findings.

  • Machine Learning Algorithms

Utilize machine learning algorithms to analyze data and make predictions or classifications. Choose appropriate algorithms based on the nature of your data and the problem you're trying to solve. Train models using historical data and evaluate their performance on new data.

  • Clustering and Segmentation

Employ clustering techniques to identify groups or segments within your data. Clustering helps in understanding patterns or similarities between data points. It can be useful for customer segmentation, market analysis, or anomaly detection.

  • Time Series Analysis

If your data is collected over time, apply time series analysis techniques. Study trends, seasonality, and patterns in the data to forecast future values or identify underlying patterns or cycles.

  • Text Mining and NLP

If working with textual data, employ text mining and natural language processing techniques. Analyze sentiment, extract topics, classify text, or conduct entity recognition to derive insights from unstructured text data.

Remember, the choice of techniques depends on your specific data, objectives, and the insights you seek. It's essential to have a systematic and iterative approach, using multiple techniques to gain a comprehensive understanding of your data.

What Is the Importance of Data Analysis in Research?

A huge part of a researcher’s job is to sift through data. That is literally the definition of “research.” However, today’s Information Age routinely produces a tidal wave of data, enough to overwhelm even the most dedicated researcher. From a birds eye view, data analysis:

1. Plays a key role in distilling this information into a more accurate and relevant form, making it easier for researchers to do to their job.

2. Provides researchers with a vast selection of different tools, such as descriptive statistics, inferential analysis, and quantitative analysis.

3. Offers researchers better data and better ways to analyze and study said data.

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Top Data Analysis Tools

So, here's a list of the top seven data analysis tools in terms of popularity, learning, and performance.

  • Tableau Public
  • R Programming
  • Python
  • Apache Spark
  • SAS
  • Excel
  • RapidMiner

Choose the Right Program

Looking to build a career in the exciting field of data analytics? Our Data Analytics courses are designed to provide you with the skills and knowledge you need to excel in this rapidly growing industry. Our expert instructors will guide you through hands-on projects, real-world scenarios, and case studies, giving you the practical experience you need to succeed. With our courses, you'll learn to analyze data, create insightful reports, and make data-driven decisions that can help drive business success.

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
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How to Become a Data Analyst

Now that we have answered the question “what is data analysis”, if you want to pursue a career in data analytics, you should start by first researching what it takes to become a data analyst. You can even check out the PG Program in Data Analytics in partnership with Purdue University. This program provides a hands-on approach with case studies and industry-aligned projects to bring the relevant concepts live. You will get broad exposure to key technologies and skills currently used in data analytics.

According to Forbes, the data analytics profession is exploding. The United States Bureau of Labor Statistics forecasts impressively robust growth for data science jobs skills and predicts that the data science field will grow about 28 percent through 2026. So, if you want a career that pays handsomely and will always be in demand, then check out Simplilearn and get started on your new, brighter future!


1. What is meant by data analysis?

Data analysis refers to the process of inspecting, cleaning, transforming, and interpreting data to discover valuable insights, draw conclusions, and support decision-making. It involves using various techniques and tools to analyze large sets of data and extract meaningful patterns, trends, correlations, and relationships within the data. Data analysis is essential across various industries and disciplines, as it helps uncover valuable information that can be used to optimize processes, solve problems, and make informed decisions.

2. What is the purpose of data analysis?

The purpose of data analysis is to gain meaningful insights from raw data to support decision-making, identify patterns, and extract valuable information. Some of the key objectives of data analysis include: Identifying trends and patterns, Making data-driven decisions, Finding correlations and relationships, Detecting anomalies, Improving performance, and Predictive modeling.

3. What are the types of data analytics?

Diagnostic Analysis, Predictive Analysis, Prescriptive Analysis, Text Analysis, and Statistical Analysis are the most commonly used data analytics types. Statistical analysis can be further broken down into Descriptive Analytics and Inferential Analysis.

4. What are the analytical tools used in data analytics?

The top 10 data analytical tools are Sequentum Enterprise, Datapine, Looker, KNIME, Lexalytics, SAS Forecasting, RapidMiner, OpenRefine, Talend, and NodeXL. The tools aid different data analysis processes, from data gathering to data sorting and analysis. 

5. What is the career growth in data analytics?

Starting off as a Data Analysis, you can quickly move into Senior Analyst, then Analytics Manager, Director of Analytics, or even Chief Data Officer (CDO).

6. Why Is Data Analytics Important?

Data Analysis is essential as it helps businesses understand their customers better, improves sales, improves customer targeting, reduces costs, and allows for the creation of better problem-solving strategies. 

7. Who Is Using Data Analytics?

Data Analytics has now been adopted almost across every industry. Regardless of company size or industry popularity, data analytics plays a huge part in helping businesses understand their customer’s needs and then use it to better tweak their products or services. Data Analytics is prominently used across industries such as Healthcare, Travel, Hospitality, and even FMCG products.

8. Is SQL good for data analysis?

Yes, SQL (Structured Query Language) is an excellent tool for data analysis, especially when dealing with structured data in relational databases. SQL is specifically designed for managing and manipulating structured data, making it a powerful language for data analysis tasks like filtering, sorting, aggregating, and joining datasets. It allows users to retrieve specific subsets of data from large databases efficiently.

While SQL excels at handling structured data, it may not be the best choice for all types of data analysis. For tasks involving more complex data manipulation, statistical analysis, or machine learning, other tools like Python or R may be more suitable.

9. What is data analysis in Excel?

Data analysis in Excel refers to the process of using Microsoft Excel's built-in features and functions to perform data analysis tasks. Excel provides a range of tools for basic data analysis, making it accessible to a wide range of users, including those without advanced programming or statistical knowledge.Some common data analysis tasks in Excel include: Filtering and sorting data, PivotTables and PivotCharts, Formulas and Functions, and Charts and Graphs.

While Excel is useful for basic data analysis, it may become limited when dealing with larger datasets or more complex analyses. In such cases, more advanced tools like Python, R, or dedicated data analysis software might be more suitable.

10. What is data analysis in Python?

Data analysis in Python refers to using Python programming language and its associated libraries to perform various data manipulation, exploration, and analysis tasks. Python has become popular for data analysis due to its simplicity, versatility, and the availability of numerous powerful libraries tailored for data science tasks.

Some commonly used Python libraries for data analysis include: Pandas, NumPy, Matplotlib and Seaborn, SciPy, Scikit-learn

Python's flexibility and the extensive range of data analysis libraries make it a powerful tool for handling various data analysis challenges, from exploratory data analysis to sophisticated machine learning tasks.

About the Author

Karin KelleyKarin Kelley

Karin has spent more than a decade writing about emerging enterprise and cloud technologies. A passionate and lifelong researcher, learner, and writer, Karin is also a big fan of the outdoors, music, literature, and environmental and social sustainability.

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