Big Data Analytics – Descriptive Analytics

Learn about the Descriptive Analytics, what is the procedure for using descriptive analytics.
Submitted by IncludeHelp, on December 30, 2021

Descriptive Analytics

Descriptive analytics is the analysis of historical data to determine what happened, what changed, and what patterns can be identified. Descriptive analytics involves the breaking down of big data into smaller chunks of usable information so that companies can understand what happened with a specific operation, process, or set of transactions.

Descriptive analytics can provide insight into current customer behaviors and operational trends to support decisions about resource allocations, process improvements, and overall performance management. Most industry observers believe it represents most of the analytics used by companies today.

Key Facts of Descriptive Analytics

The following points are showing the key facts of Descriptive analytics -

  • Descriptive analytics is the most fundamental and widely used sort of analytics in businesses. It summarizes and draws attention to patterns in both current and historical data sources.
  • Descriptive analytics is used to generate reports, key performance indicators (KPIs), and business metrics that allow businesses to track and analyze performance and other patterns.
  • Descriptive analytics assists firms in understanding what has occurred so far in their operations. When descriptive analytics and diagnostic, predictive, and prescriptive analytics are used together, firms can better explain why something happened while also predicting probable future outcomes and actionable insights.
  • Descriptive analytics presents crucial information in a simple and easy-to-understand manner. In the future, descriptive analytics will continue to be required. On the other hand, more work is being directed towards other domains of analytics, such as predictive analytics and prescriptive analytics.

What is the procedure for using descriptive analytics?

Data aggregation and data mining are two strategies that are used in descriptive analytics to obtain historical information about a given set of variables. Before any analysis can begin, data is gathered and sorted according to data aggregation in order to make the datasets more manageable for analysts.

Data mining is the next phase in the analysis process, and it entails searching through large amounts of data in order to find patterns and meaning. The patterns that have been identified are next evaluated in order to determine the exact methods in which learners engaged with the learning content and within the learning environment.


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