What is Descriptive Analytics?

Descriptive analytics is a field of statistics that focuses on gathering and summarizing raw data to be easily interpreted.

Generally, descriptive analytics concentrate on historical data, providing the context that is vital for understanding information and numbers.

The field is used across a variety of industries and needs, and can cover a diverse range of purposes, from inventory tracking to benchmarking yearly revenues and sales.

The field usually serves as a preliminary step in the business intelligence process, creating a foundation for further analysis and understanding.

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Essentially, descriptive analytics seeks answers about what happened, without performing the more complex analyses required in diagnostics and predictive models.

In business intelligence, descriptive analytics is usually the first step and will result in visualizations like pie charts, line graphs, bar charts, and other simpler graphical displays.

The field usually employs simpler mathematics and statistical tools (such as arithmetic, averages, and percent changes) instead of more complex calculations that predictive and prescriptive analytics perform. It also includes the initial stages of data aggregation and data mining in most data analytics software.

How Can I Use Descriptive Analytics?

Even without knowing it, many organizations use descriptive analytics extensively in their everyday operations.

For most businesses, descriptive analytics form the core of their everyday reporting. This includes simpler reports such as inventory, workflow, warehousing, and sales, which can be aggregated easily and provide a clear picture of a company’s operations.

One straightforward example of how descriptive analytics are used in operations revolves around annual revenue reports.

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On the surface, revenues of $1 million are a good thing. However, this raw number may be misleading without the benefit of context. Historical data can provide a clearer picture of the financial situation and show you how that $1 million in revenues compares to previous months’ or years’ sales.

Similarly, a warehouse may need to understand why specific items are constantly out of stock, or over-ordered.

A quick scan of historic data may show them that certain products have seasonal peaks and troughs, or that there have been too many orders of an unpopular product.

Even broader financial statistics fall in the descriptive analytics umbrella. For instance, data like return on invested capital, yearly sales, year-over-year revenues, and price to earnings ratios all arrive from performing descriptive analysis on financial figures.

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