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  • Writer's pictureKatherine Chiodo

The diagnostic analytics landscape

In our previous post, we discussed Descriptive Analytics, the first stage of analytics maturity. As we progress through each of the stages, it is essential to keep in mind that this does not mean that organizations no longer have a need for descriptive analytics. Instead, as we move around throughout the framework, previous types do not become obsolete, but rather become the basis for producing further insights.

Imagine you woke up one morning and found that all the cookies were gone from the cookie jar. You would wonder who ate them and why. To solve the mystery, you would look for clues - maybe there are some crumbs on the table or some chocolate on someone's face. Then, you would use those clues to figure out what happened. That's what diagnostic analytics does - it helps you figure out why something happened by looking at the data. In this stage, organizations move beyond just understanding what happened in the past and begin to analyze why it happened.


Some common questions that you may seek to answer during this stage:

  1. What were the root causes of a particular outcome or event?

  2. What factors contributed to a change in performance?

  3. Which customer segments had the highest churn rate, and why?

  4. What are the factors that influence customer satisfaction/loyalty?

  5. What are the key drivers of cost in our business processes?

  6. What marketing campaigns or channels had the highest conversion rates, and why?

  7. Which areas of our website or app had the highest user engagement, and why?

By answering these questions, organizations can identify patterns and relationships in the data that help them understand why certain outcomes occurred. This helps organizations make more informed decisions and take targeted actions to address specific issues or opportunities.


In order to understand the “why”, organizations need to invest in a few key components:

  1. Centralized data warehouse Analyzing the data in your application database is often not enough to have a full picture of the story. That’s where a data warehouse is vital. It gives you the opportunity to load various sources of data into a centralized repository, clean it up and add additional logic, and document it. The analytics (and data) engineering functionality really becomes a necessity at this stage. It allows your data team to bring in the various sources via ETL/ELT, transform it, and then present it in visualization tools in order to create that self-service culture for your organization.

  2. Analytics platform An analytics platform that can allow your organization to self-serve and begin its own reporting and insight generation is important because it will reduce the amount of data gating we discussed in the descriptive analytics stage. It will inspire the organization to invest in tooling that promotes data discovery and analytics across the entire organization. With this comes the importance (and need!) for data modeling, cleaning, and centralized documentation. The data that enters your analytics platform must be in good shape for your data consumers to make sense of. Having numerous integrations pulling data from various sources is one thing, but if your consumers can’t understand and utilize it effectively - there really isn’t a point.


If this resonated with you, reach out to us and see how we can help you stand up an infrastructure that sets you up for success! If this doesn’t sound like your organization's analytics maturity, stay tuned for the next post where we focus on the predictive stage.


Katherine Chiodo, Technical Writer & Data Operations


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