Fourth Stage of Analytics Maturity: Analytical Enterprise

In my last post, I described the characteristics of organizations at the third, “Analytical Aspirations” stage of the analytics maturity model. In this post, we will dive into what an organization at the fourth stage, the “Analytical Enterprise” stage, of the analytics maturity model looks like.

These posts on analytics maturity stages are heavily influenced by the great work Competing on Analytics by Thomas Davenport. I’d highly recommend picking up a copy for you or your BI department.

Common Traits at this Stage

At this stage of analytics maturity, organizations have a well-developed analytics platform. Data centralization, integration, cleanliness, and governance all are mature in their manifestation. The challenges faced in the previous three stages have all been resolved. Questions asked shift from being reactive to proactive in nature – from “what happened last month” to “what will happen next month”.

The question that most frequently enters the analytics lexicon at this stage is “why”. The question of “what” (i.e. descriptive) shifts to a more prescriptive question. It is the medical equivalent of going from gathering a list of symptoms to attempting to understand why those symptoms are present.

How does this apply to analytics? Organizations at the “Analytical Enterprise” stage of analytics maturity start to ask why things are happening. The questions force a deeper dive into process analysis and how analytics can be embedded within the processes themselves. From “what is our membership doing” to “why is our membership doing what they are doing”.

Internally, analytics begins to drive performance. Clearly defined KPIs are measurable and transparent throughout the organization. Benchmarking analyses have been put in place by organizations at this stage to highlight the value yielded by analytics efforts. “Guesstimates” are nowhere in sight.

Cultural and Change Management Required at this Stage

There are only a few technological differences between this stage and the third stage of analytics. The analytics platform is a bit more developed and skills in the areas of statistical modeling and data science have been acquired.

The single greatest shift from stage three to stage four of analytics maturity is the organization’s cultural adaptation driven by data analytics. Analytics is no longer a “want to have” but a “need to have”. It is embedded in every discussion at nearly every level of the organization. Conversations occur about analytics can drive innovation thereby making analytics success the platform for innovative success. It should come as no surprise to readers of this post that most innovate and digitally transformative organizations are also highly successful with analytics. Analytics is, therefore, not an effect but the cause. Analytics propels innovative strategies and digital transformation efforts. It provides the foundation for what the vast majority of organizations seek to achieve over the next 3-7 years.

The next and final stage of the analytics maturity curve will describe those organizations whose business models are entirely driven by success with analytics. We’ll describe that in more detail in the next post!

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Posted in Analytics, DW/BI.

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