Types of Business Analytics: Part 3 – Diagnostic Analytics

What are Diagnostic Analytics?

Diagnostic analytics, just like descriptive analytics, uses historical data to answer a question. But instead of focusing on “what,” diagnostic analytics addresses the critical question of “why” an occurrence or anomaly occurred within your data.

Diagnostic analytics also happen to be the most overlooked and skipped step within the analytics maturity model. Anecdotally, we see most customers attempting to go from “what happened” to “what will happen” without ever taking the time to address the “why did it happen” step. This type of analytics helps companies answer questions such as:

  • Why did our company sales decrease in the previous quarter?
  • Why are we seeing an increase in customer churn?
  • Why are a specific basket of products vastly outperforming their prior year sales figures?

Diagnostic analytics tends to be more accessible and fit a wider range of use cases than machine learning or predictive analytics. You might even find that it solves some business problems you earmarked for predictive analytics use cases.

How Do You Get Started with Diagnostic Analytics?

Being at the diagnostic analytics phase likely means you’ve adopted a modern analytics tool. Most modern analytics tools contain a variety of search-based, or lightweight artificial intelligence capabilities. These features allow for detailed insights a layer deeper. To be clear, these are an effective lightweight means to address diagnostic analytics use cases but are not a means to a full-scale implementation.

Diagnostic analytics is an important step in the maturity model that unfortunately tends to get skipped or obscured. If you cannot infer why your sales decreased 20% in 2020, then jumping to predictive analytics and trying to answer “what will happen to sales in 2021” is a stretch in advancing upward in the analytics maturity model.

Continue with Ember as we take a look at the third type of Business Analytics: Predictive Analytics.

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