DATA ANALYTICS

Forecast about your business future with Data Analytics

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By admin
2 Min Read

Data Analytics is the process of analysing raw data to find conclusion about the information. In general, data analytics comprises of diverse types of data analysis. It includes techniques which can reveal metrics that would have lost in the mass of information. It is used to optimize the several processes to enhance the overall efficiency of a business.

The techniques and processes of data analytics have been automated into mechanical processes and algorithms which convert the raw data for human consumption.

Types of Data Analytics

In the complete data analysis process, each type has a different goal and holds a different place in this broad field of data analytics. Let’s discuss about the different types of data analytics:

  1. Descriptive Analytics: This kind of data analytics summarizes large datasets to describe outcomes to stake holders. To track performance in specific industries, several metrics are required such as insights into past performance, return on investment(ROI). This process includes the collection of relevant data, processing, data analysis and data visualization.

  2. Diagnostic Analytics: This kind of data analytics digs deep into find the cause and supplements basic descriptive analytics. It works on the research works and questions about why things happened. This kind of investigation helps in discovering changes in both positive and negative manner. It generally happens in three steps:

  • The primary step identifies the unexpected changes in a metric or particular market.
  • Collection of data related to its changes.
  • Statistical techniques are implemented to find relationships to explain these changes.

These kind of data analytics offer the right insights which are required by businesses to make efficient and effective decisions. When these analytics are utilized in the right way, they offer better understanding of companies’ requirements.

For the successful operation of businesses, these data analytics steps must be used in order to enhance efficiency and improve performance by discovering data patterns.

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