Analytics Maturity Models: A Core Component Of Analytics Strategy And How To Better Understand Your Current State

Analytics Maturity Models: A Core Component Of Analytics Strategy And How To Better Understand Your Current State

June 17th, 2019

At CompassRed, we pride ourselves on being good at listening to the needs of our clients.  In several recent (coffee powered) conversations, from small/medium sized businesses to global corporations, we heard a few recurring themes from our clients. Here are a few:

  • Internal analytics teams are being asked to do more than ever with predictive analytics…even if the team isn’t delivering more basic analytics in a programmatic way.  

  • The evolving marketplace is changing consumer expectations in terms of experience and communication: “If company XYZ can leverage “all” known data about a customer, why can’t we?"

  • There is not a consistent language across analytics practitioners, business leaders and the general public. Machine learning at company X doesn’t line up to what Company Y is calling machine learning.  Words matter when it comes to what’s actually being delivered by ML/AI/predictive companies.

With these types of challenges, where can a business leader get started on building the future where their team is fully leveraging their data?

Current State to get to a Future State

One of the foundational building blocks of our data strategy we begin with is a solid understanding of your “Current State”. Said another way, “where are we now”? You may be able to see where you’re going, but an assessment grounded in the current state is a key input to any strategic roadmap.   Understanding what is being asked of your team is essential.

A specific tool that we use often at CompassRed for a better view of the “Current State” is to look at a “Maturity Model” to help understand the current state.  “Maturity Models” allow us to assess qualitatively people/culture, processes/structures, and objects/technology.  The models allow us, along with our clients, to assess where the organization resides as it relates to different stages of development on the analytics journey.  More importantly, it allows our clients to “see themselves” and assess their current state of their analytics resources.

There are a few models we use and each have varied categories of comparison. At CompassRed, we use a modified version of DELTA (Data, Enterprise, Leadership, Targets, Analysts) that borrows from “Analytics at Work” by Davenport et al.  Other models will vary the dimensions for comparison.   Depending upon the industry, we modify the model and various dimensions.  For example, in healthcare, we look at the “The Healthcare Analytics Adoption Model” by Sanders et al. Categorized from Level 0 (Fragmented point solutions) to Level 8 (Personalized Medicine and Prescriptive Analytics) the model describes each level and the characteristics of organizations in that bucket.   This type of framework is very useful to someone trying to understand where they are and what type of investment may be necessary. It’s easy to communicate this to other stakeholders and gain buy-in to correctly identifying generally how the organization would fit on the model. It also helps paint the “Future State” and where your organization has the possibility to reach.

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Maturity models aren’t perfect, as occasionally they force over simplification. ie…you should pick a level or range.  This washes out some of the nuances that are necessary. For example, it’s entirely possible that your company is mostly a level 2 but has some more advanced characteristics of a more advanced organization.  There are others, but as a grounding tool, the pros outweigh the cons.

In short - after you’ve done some dreaming big about all the ways your team can better use data and designing the perfect future state, it’s best to create a realistic picture of the Current State and the current analytic capabilities of your organization.

This will pay off as it grounds your roadmap and plans in reality and benchmarks your organization against standards..  This grounding will give your team and initiatives a much better chance of delivering on the promise of analytics, machine learning and artificial intelligence.