What Are the Six Phases of Data Analytics?

Data analytics is the process of looking at and analyzing data to draw conclusions and make wise judgments. In order to gather, process, and analyze data from diverse sources, including databases, spreadsheets, and internet platforms, a range of approaches and technologies are used. To make data-driven decisions and optimize operations, data analytics is employed in a range of industries, including business, finance, healthcare, and government.

The six phases of data analytics are:

  • Ask
  • Prepare
  • Process
  • Analyze
  • Share
  • Act
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Every data analysis is done to solve a problem. But you cannot choose a proper model without understanding what the problem is. Therefore, the first phase in data analysis begins with asking the right set of questions to recognize the problem. Without understanding the problem, you cannot come to the correct resolutions. Also, finding the problem might be one of the difficult tasks.

Here are some guidelines to identify the problem by asking the right questions:

  • Define a Problem Statement. This will be the first step in this phase which will give you a lead. The problem statement is not necessarily accurate at first. You may learn more about the problem as you go and you can refine it when you have a clear picture. But make sure the statement is clear and concise.
  • You can also hold discussions with your team members to get a concise problem statement. Brainstorm the cause and effects to come to a conclusion about your problem.
  • Try to find the root cause of the problem so that you can fundamentally resolve it with the help of data analytics.


After defining the problem, naturally, we go for resolving it. But before resolving the problem, you must prepare the necessary tools and create a strategy. Only then you will be able to easily find a solution for the problem and become successful in achieving the right resolution. Once you prepare yourself before solving the problem, you can be more efficient in completing the task. 

Here are some guidelines for preparing for the problem defined:

  • Find out the Metrics you are going to measure. These metrics are subjective to the organization you are doing the analysis. Try to find out the KPIs so that you can understand the different processes present in the company. 
  • After finding out the metrics, choose which of the factors to consider. This will help you to focus on important areas. 
  • Gather information like the location of the data and how it will be stored, moved, and shared. Also, you need to take security measures to protect data...
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Since data is gathered from various sources, it will not be without inaccuracies, corruption, and mistakes. It will be incomplete data in the dataset and you have to process it. This processing step makes the analysis efficient and avoids inaccuracies in the analysis. 

Here are some of the ways in which you can process the gathered data before it is used for creating an analysis model:

  • Find out the inaccuracies and mistakes in the data using the proper tools. This is the first step in processing the data. If there are any incomplete data, try to reach the source and collect the complete information.
  • After finding out the errors, remove them with efficient tools. Sometimes there might be duplicates in the data which must be removed to avoid inconsistencies.
  • Also, check whether the data is biased. If it is biased, it will not be addressing the actual problem.
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Now you have reached the important phase of data analysis. This is where you start analyzing the data you obtained and get results for your problem. You have to master this area over time and with experience, you will be able to start thinking critically. There are various methods and tools to analyze the data you have. 

Here are some of the guidelines for data analysis:

  • Choose the right type of tools to perform the analysis. You can run different models to find the right tool which makes it more efficient.
  • Try to do different calculations so that you can get additional metrics.
  • Create visual charts to make it easier to comprehend and analyze.


Getting different views and opinions is an important aspect to improve the analysis. We may have taken a biased decision which can be altered when you get different opinions from others. Here are some of the benefits of sharing phase:

  • Feedback allows you to take better decisions that you might not have considered. 
  • You can get a ton of additional information and suggestions on the analysis which will help improve its multiple folds. 
  • You can get results from various angles which can help you form a better decision.
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Once you have performed the analysis, you must perform some actions with it to resolve the problem you defined in the first step. You can recommend the organizations to perform some actions and give them advices on decisions to be made. The company will now be able to perform data-driven decisions which are where data analysis comes to the rescue.


These are the stages of data analytics that each and every data analyst ought to go through in order to carry out an appropriate analysis. Having the database at your disposal enables you to be more productive and give clients with accurate results.

Each organization can give importance to each of these Vs in a different manner. But the fundamentals of the Vs are common to all and it is important to take note of these to maintain the quality of data used. These are the importance of 5Vs in big data analytics.