In the context of research, it is for the purpose of...

1. Investigating issues and problems.

2. Solve problems or to suggest for problem solving.

3. Decision making.

In the context of businesses, it is for...

1. Growing businesses. 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.

2. If your business is growing, then you have to look forward to make the business to grow more.

All you need to do is to analyze your business data and business processes.

A process of inspecting, cleaning, transforming & modeling data with the goal of discovering useful information, suggesting the conclusion, and supporting decision making.

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.

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

Statistical Analysis

Statistical Analysis shows "What happen?" by using past data in the form of dashboards. It includes collection, analysis and modeling, interpretation, and presentation 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 population. In this type of analysis, you can find different conclusions from the same data by selecting different samples.

Diagnostic Analysis

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

Predictive Analysis shows "what is likely to happen" by using previous data. The simplest 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

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.

Step 1: Data Requirement Gathering

• Why do you need to do this data analysis? Find the purpose or aim of doing the Analysis.

• What to analyze and how to measure it?

• What should be your findings?

• Which type of data analysis you intend to do?

Step 2: Data Collection

• Collect your data based on the requirement.

• Gather your data from reliable sources in an ethical way.

• Use proper data collection methods/tools.

• Organize data 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.

Step 3: Data Cleaning

• The data must be cleaned and error free.

• Handle the missing values by using the proper missing value imputation procedure.

• Clean data from any "noises" and "distortions", such as duplicate records, white spaces, and errors.

• When you do a parametric analysis approach, check for normality.

• Bad outliers should be discarded.

Step 4: Data Analysis

• Check whether you have the exact information you need, or you might need to collect more data.

• Use the proper technique of analysis.

• Ensure the correct procedure.

• Use the right data analysis tools and software, which will help you to understand, interpret, and derive conclusions.

Step 5: Data Interpretation

• Choose the way to express or communicate your data analysis either you can use simply in words or maybe a table or chart.

• Understand and review the meaning of the data to guide you to relevant conclusion.

• Use the results of your data analysis process to decide your best course of action.

Step 6: Data Visualization

• Visualize your data in a proper way. It often appears in the form of charts and graphs.

• Data shown graphically 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.