What Are Descriptive Analytics?

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The first type of data analytics are descriptive analytics. Although it is simpler to execute they should not be understated. They provide us with valuable insight by answering the question “ What happened?”. The main goal of this type of analyses is to explain variances and look for trends in our data. This allows for managers to evaluate the business performance of a company and take corrective actions when required.
The application of historical analyses is an example of a technique that can be used broadly and take different shapes and forms. Frequently. It helps us uncover some of the key drivers of business performance and is often considered as a stepping stone for progressing to some more sophisticated analytical techniques. Historical data typically includes an organization’s financial and operational metrics as derived form operational reposts, invoices and financial statements.
In this tutorial, you will see a chart of how the profitability of a company has evolved over several years. When performing historical analyses past data does not have to solely come from within the organization. It can also come from external data such as consumer confidence index, interest rates, debt/gdp and any other economic variable that can have a significant impact.
The advantages of historical analyses offer simple explanations and is often used as a foundation for hypothesis generation. An example would be formulating a relationship between good weather and increased sales. Another application of historical analyses is benchmarking which effectively generates insights when comparing an organization’s past to that of its competitors over the same time period. Another advantage is that it is widely accepted and rather easy to understand and the data easy to find.
Check out the rest of the video to find out its disadvantages, and learn about its complement- variance analyses, and how it is applied in business.
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