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What are the different types of Data Analysis?

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types of Data Analysis

Data analysis is a procedure of inspecting, transforming, cleansing, and modeling the data with the intent of finding out useful information, making different conclusions, and supporting decision-making. The data analysis plays an important part in helping you broaden your business. It is because when the data is used meticulously, it leads to a better understanding of what the previous performance of your business was and how you can make an even better decision for future activities. There are different types of data analysis and many ways to utilize data at all levels of a company’s operations. So it is best to know all the options and then act accordingly.

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Types of data analysis:

Four different types of data analysis are widely used across all industries. Even though there are separated into different categories, yet they are all linked together and are the building blocks of each other.

  • Descriptive analysis.
  • Diagnostic analysis.
  • Predictive analysis.
  • Prescriptive analysis.

Descriptive analysis:

Descriptive analysis is the first type of data analysis and is at the foundation of all data insight. You can consider this data type to be the simplest and most commonly used in today’s business. Descriptive analysis answers the “what happened” by summarizing past data. It usually does it in the form of dashboards. The descriptive analysis comes with different uses but the biggest one is it being used in business to track Key Performance Indicators (KPIs). The KPIs describe the performance of any business based on some specified benchmarks.

The business applications of descriptive analysis include the following:

  • KPS dashboards.
  • Monthly revenue reports.
  • Sales lead overview.

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Diagnostic analysis:

Now that you have asked the main question which is “what happened”, the next step is to be implemented where you need to dive deeper and ask why it happened. This is the part where diagnostic analysis comes into action. The diagnostic analysis takes all the insights found from descriptive analytics and then drills it down to find the actual causes of these outcomes. The organizations seem to use this type of analytics often as it helps you create more connections between data and lets you identify patterns of a particular behavior.

The business applications of diagnostic analysis may include:

  • A freight company who has to investigate the cause of slow shipments in a certain area or region.
  • A SaaS company has to drill down so as to determine which marketing activities increased trials.

Predictive analysis:

In this type of data analysis, you are to find the answer to the question “what is likely to happen”. In predictive analysis, the business uses previous data to make different predictions about future outcomes. This type of data analysis is one step higher than the descriptive and diagnostic analysis. Predictive analysis uses data that we summarize and then make logical predictions of the outcomes of the event. The Data analysis made relies on statistical modeling and requires added technology and manpower to forecast. But keep that in mind the forecasting is only an estimate and that the accuracy of predictions relies only on quality and detailed data.

The business applications of predictive analysis include are mentioned below:

  • Sales forecasting.
  • Risk assessment.
  • Predictive analytics in customer success teams.

Prescriptive analysis:

The final type of data analysis is the prescriptive analysis that is considered to be the most sought after, still, a few organizations know and are truly equipped to perform it. It is the frontier of data analysis that combines the insight from all of the previous analysis and determine the right course of action to take and solve the current problem or make a decision. It utilizes data practices and the state of the art technology and is a huge organizational commitment. That is why companies have to be sure that they are not only ready but also willing to put forth the effort and resources.

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Conclusion:

Now you can see for yourself that each of these data analysis is connected to each other and also rely on each other to some extent. All these types of data analysis serve a different purpose and provide varying insights. So organizations should have the technical ability to use these types and unlock more insight into the organization.