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Exploring descriptive analytics and how it works

The traditional Analytics Maturity Framework is a model that helps organizations understand the level of their analytic capabilities. The model progresses from easy-to-implement types of analysis to some more difficult types measured through the value they provide & how difficult they are to execute. This post will cover the ins, outs, and in-betweens of descriptive analytics - the first stage of maturity in the framework.

Imagine you took a picture of your garden every day for a year. Then, you put all the pictures together and looked at them. You could see how your garden changed throughout the seasons - when the flowers bloomed, when the leaves fell, and when snow covered the ground. You could also count how many flowers you had, or how many different colors there were. That's what descriptive analytics does - it provides an understanding of a situation or event only after it has happened or developed. This stage of analytics maturity only provides a retrospective view of the data but does not provide any predictions or recommendations for the future.


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

  • What are the key trends and patterns in our data?

  • What are the top-performing products, services, or regions?

  • What are the biggest drivers of revenue?

  • How has our performance changed over time?

  • What are the characteristics of our most valuable customers?

  • What are the sources of our website traffic?

  • What is the distribution of our sales by channel or product category?


By answering these questions, organizations can gain valuable insights into their business and make data-driven decisions through curated reports and dashboards. Ultimately, the goal of answering these questions is to be able to better understand the drivers of success for your business and use them to accelerate your growth. Collecting data is one thing, but using it to make better decisions in the present is what separates data-led organizations from those that are data-driven.


It is imperative that at this stage, organizations do not invest too heavily in tooling and software. Most of this, can and is likely already being done via Excel spreadsheets by the respective teams measuring their efforts. For example, it is primarily the Marketing team that will be concerned with measuring website traffic, and tools like Google Analytics will already do this and provide in-house reporting for you. Additionally, there is no need for a centralized data warehouse at this stage of your analytics journey, as it makes the most sense to be cost-efficient. The only data flowing into your Production database is likely going to be from your application, with all external data sources still living in the applications that collect it.


This is where the Excel spreadsheet comes into play! Data from your external sources is manually extracted and entered into your spreadsheet so that the business can apply manual calculations (company-defined metrics) to your data so that it can be shared with the people who need it to make informed decisions. These metrics are also likely not defined in a data lexicon, so an organizational-wide understanding of your KPIs doesn’t exist. This stage of analytics maturity probably has a large amount of data-gating, which limits the number of people you inspire to be more data-driven. Organizations at this stage likely don’t need any Data or Analytics Engineers but rather line-of-business analysts who can ensure data collected from your tools are properly extracted and tracked in your external spreadsheets.


When the organization can only answer questions about the past, they are typically still early in their data journey, and just getting started. This is where we lay the foundation for a data-driven culture. By asking the right questions with the appropriate amount of tooling you can help answer some of the business's most important questions.


If this sounds like your organization or data team, reach out to us and we can help! We specialize in helping businesses understand their data, propel their data-driven culture and ensure they are spending their money in the right places! If this didn't resonate and you feel like your organization has already surpassed this stage, stay tuned for the next post where we focus on the diagnostic stage.


Katherine Chiodo

Technical Writer & Data Operations


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