Types of Analytics
Today we are going to discuss Types of Analytics. Analytics is a category tool for visualizing and navigating data and statistics. The following are illustrative examples of analytics. The big data revolution has given birth to different kinds, types and stages of data analytics. The key motive of using Big Data is by gaining accurate information which delivers knowledge, that gives business to gain a competitive edge and help to smarter decisions for better business outcomes. To Learn Analytics skills learn form Eayshiksha SAS, SAP, Power BI and Advance Excel courses Following are the Types of Analytics.
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Descriptive analytics
It is the most basic form of analytics and 90% of org use this analytics. This analytics is based on real-time and historical data. The main objective of this analytics is to find the success and failure rate in the past. This analytics is based on social analytics like no. of followers, likes, post, which just require school math summarized certain groups based on some events and simple count. The metrics are used for avg response time, average no. of replies etc.
Diagnostic Analytics
This analytics is to find the cause behind why something happened? And fall second in the list of Types of Analytics. IT use Business Intelligence for this Its a machine learning technique again. We can take an example to understand by using ML in healthcare we can diagnose the suspectable rate of cancer-based on medical screening.
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Predictive Analytics
Predictive analytics is based on past data patterns. They use some Tools that predict future from patterns in past data. This helps in setting realistic goals for the business. To get the real insight the organizations collect contextual data and relate it with customer behaviour datasets and web server data. Predictive analytics provides better recommendations and more future-looking answers to questions that cannot be answered by BI.
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Prescriptive Analytics
Prescriptive analytics is the next step in Predictive analytics. These are based on possible outcomes and results actions are likely to maximise key business matrices. It is a combination of data and business rules. The data can be both internal and external. Business rules are preferences, best practices, boundaries and other constraints. These analytics use Mathematical models include natural language processing, machine learning, statistics, operations research, etc.
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