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#52 What is your analytics maturity level?

I recently had a very interesting discussion with Markku Mäenpää and he pointed out me the idea of data analytics maturity for organizational impact and for getting the best business outcomes. After that discussion, I started to study this topic with Gartner data analytics maturity model.

Gartner categorizes the maturity of data analytics based on the system's (and simultaneously organization´s) ability to not only provide data but also directly assist decision-making. More advanced analytics systems can help organizations anticipate the impact of future decisions and draw conclusions about the optimal choice. The four-step maturity model can help assess the current state of data analytics systems and reveal the optimal path forward.

four maturity level steps

  1. Descriptive: What happened? Basic reporting functions from IT analytics systems enable business management to monitor key performance indicators (KPIs) as well as other primary metrics.

  2. Diagnostic: Why did it happen? To understand what happened and why it happen, you need to dive deep into the data. For example, accidents at work or sick leave can be viewed on a site-by-site, shift-by-job basis and compared to the orientation and training received by employees. This justifies an attempt to look for seasonalities between events and the reasons that influenced them. With the help on diagnostive analytics and reporting users can drill into where performance is above or below plan and address the likely causes.

  3. Predictive: What will happen? Predicting the performance of future metrics can allow organizations to anticipate challenges before they occur. Systems are able to provide a proactive opportunity to prevent problems before they arise.

  4. Prescriptive: How can we make it happen? To achieve this level, e.g. machine learning, artificial intelligence and big data are needed to deal with possible outcomes, and one that offers the optimal combination of possibilities with minimal risk is recommended. As the highest level this phase can have cognitive analytics and even capability to answer questions, “what don’t we know about yet?

When assessing the challenges and needs of data analytics solutions, decision makers can assess the capabilities of each solution individually and as part of an entity based on what stage the organization is at and where it wants to be in the near future.

These considerations allow e.g. IT managers to develop criteria that allow for an objective comparison between available solutions and available analytics vendors and their products. This, in turn, allows for strategy formulation and systematic development.

Original source: Gartner - The maturity of data analytics


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