Data analytics maturity: Don’t leave people and culture out of the equation

Do a search on “analytics maturity model” and you’ll get a lot of results that focus on pure technical capability. The typical progression starts with understanding the past, to making probabilistic statements about the future, to determining how to actually influence outcomes. This is one valid measure of maturity, but as a model for Advancement it is incomplete.

The progression runs like this:

  1. Descriptive analytics (reporting and business intelligence that answers “What happened? What is happening?”), advancing through …
  2. Predictive analytics (forecasting or predictive modeling that answers “What will happen? Who is most likely to do ‘x’ in future?”), and reaching a pinnacle at …
  3. Prescriptive analytics (answering the questions “Why did ‘x’ happen” and “How can we make ‘y’ happen?”).

As a result of investments in people, skills, and tools, our shop is proficient in descriptive and predictive analytics. Aside from potential one-off projects, though, we do not aspire to tackle the peaks of prescriptive analytics.

Prescriptive analytics seems more suited to mechanistic systems producing massive amounts of data, not social systems made of complex human behaviour producing relatively small amounts of data.

We might go there someday, but getting crazy sophisticated with analytics is not today’s goal. The goal is to consistently use data to inform decision-making. The tool could be machine learning, or it could be basic bar charts.

Some maturity models are better than others. Find one that addresses the people and process dimension, that enables you to assess the maturity of your culture of decision-making across the whole organization.

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