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“My business doesn’t need
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– Anonymous, 1994

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Data transformation with Data Analytics

Data analytics refers to the various procedures of deriving valuable insights from data. It involves various methods such as extraction of data and then it’s categorization to manage it in a better way. These valuable insights can help you manage data by building patterns, relations, and connections in the derived data. Over time, most of the organizations have started to change itself into a data-driven organization so that the derived data can be categorized and analyzed so that to make the maximum use of it.

Types of analytics 

  1. Prescriptive Analytics- This kind of analytics talks about an analysis based on the rules to define a certain analytical path for the organization.
  2. Predictive Analytics – This kind of analytics ensures that the method for the further mode of action is predicted.
  3. Diagnostic Analytics- This method of analytics works around determining the reason behind the cause. It also revolves around working on a dashboard.
  4. Descriptive Analytics- The descriptive analytics works around the incoming data and the deployment of analytics for mining. Hence after getting the required data, you can come up with a description based upon the data.

How Data Analytics has made working easy?

The need for Data Analytics springs from the data which is created at breakneck speeds on the internet. Over time, these digital lives will make big data even bigger. The bigger the data, the bigger the problems of handling it. Therefore, the analysis of data by using traditional methods of working with data cannot be implemented. Hence, there is a need for analytics tools to make sense of all the data. It helps in the organization, transformation, and creation of different data models based upon the requirements of identifying the patterns in the data and making the necessary conclusions.

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