History of the Data and Analytics Maturity Model
10/01/2019 by Tricia Aanderud Modernization - Analytics
As with any endeavor, your company must have a planned strategy to achieve its analytical goals. Even the best-intended projects die on the vine if not given the proper support. One way to achieve the goals is to use the strategy from an analytics maturity model (AMM).
The analytics maturity model (AMM) has its roots in the software capability maturity model (CMM). The model describes the five stages a company travels through to reach maturity.
In the early 1970s, the US Air Force tasked the SEI with answering a simple question. Why were their software projects slower than expected and often over budget? They needed a way to evaluate vendor business processes and practices consistently.
From this exercise, the process maturity framework was created. It would later become the basis for the CMM popularized by Watts Humphrey.
The CMM describes the stages that a company travels through to reach process formality and maturity. Each level is a measure of process maturity that the business must solve to have more predictable outcomes. The model is like a ladder; Each level represents a rung. Companies must reach each rung before climbing to the next one.
Most companies get to level 2 for software maturity, but few get past it. Usually, it is because leadership gives the subject lip service with little to no investment. Thus, there is no defined strategy for climbing the rest of the ladder.
The CMM is the basis for other maturity strategies. In the mid-2000s, The Data Warehouse Institute (TDWI) discussed achieving business intelligence (BI) maturity.
They based the explanation on the technical level, such as introducing data marts and databases. Other vendors released data maturity models, viewing it more as maturing the IT department. Others used a customer perspective. All customer service needs should drive analytic maturity.
All models shared a similar trait. They start at a tactical level, move to a strategic level, and ended with optimized processes.
Gartner produced the most widely embraced model. In 2008, they released a Web Analytics Maturity Model. This model detailed the steps going from data collection to using data to drive sales activities.
Later, their model applied to more general practices. This model closely followed the CMM, but its focus was on reaching maturity through business intelligence (BI) methods.
Unlike other models, this evolving model used the non-technical aspects of maturity, such as culture, people, and leadership.
While the marketing department can easily own web analytics maturity, this model needed the enterprise to be involved.
The company required focused areas called Business Intelligence Competency Centers (BICC). This work was so crucial that a C-level executive should be sponsoring it.
The model appealed to leadership to use big data to transform their companies.
In 2017, Gartner released the maturity model called 2.0. This model showed the BICC maturing into Analytics Centers of Excellence (ACoE). It also introduced the idea of a C-level executive responsible for overseeing analytics and data.
This strategy includes machine learning and artificial intelligence (AI). It described how companies should move toward adopting advanced analytics. All of these levels are significant achievements.
Companies need to move through these levels of analytical maturity to realize the full potential of their data strategies.
After reviewing the models of so many other vendors and even equipment manufacturers, we developed our own model.
Zencos has helped multiple customers improve their strategy using this model.