In any particular business domain, organizations find value in using maturity curves to both benchmark their behavior against peers and self-assess against an idealized future state. Although these curves are a useful positioning tool, they often place organizations at a neutral starting point — the origin of the coordinate plane. However, the origin, the position of an institution on the brink of reaping those rewards characteristic of the first quadrant of a maturity curve, requires a certain combination of scramble, organization, and messaging to achieve. That pre-reward progress deserves to be tracked and considered, as those first steps towards a behavioral shift are often the most difficult. In understanding such behavioral shifts, maturity curves must be modified.

The data governance and the analytics maturity curves are two such curves that typically spout from the origin into the first quadrant of the coordinate plane. Traditionally kept separate, the curves have many dependent features that deserve a kind of contingency. While the traditional data governance maturity curve follows organizations from no data capabilities to impact optimized data usage, the analytics maturity curve starts at a point found within the data governance maturity curve and ends at the same point, as seen in figure 1.

Figure 1, The Traditional Data Governance Maturity Curve on the left is closely related to the Traditional Analytics Maturity Curve on the right.

In order to start an analytics journey and place oneself on the analytics maturity curve, the understanding of data and the desire for its use are essential first steps. No Capabilities, Ad Hoc, Fragmented, and Interest on the data governance maturity curve are the steps in which an organization gains that understanding and interest in data. Before the point of Action, although understanding of the importance of data and the desire for accessible, quality data is achieved, competitiveness, productivity, and maturity of data or analytics capabilities are not. Those achievements are only made once data governance action is taken — as thought has no real meaning unless its followed by action (or so goes the saying).

To reflect the thought versus action discrepancy and account for the complete data & analytics journey organizations face, I am proposing the new, logistic maturity curve in figure 2 below.

Figure 2, The Data & Analytics Maturity Curve allows organizations to accurately place themselves in their Data/Analytics journey

It is challenging for organizations to take those first steps in proliferating data understanding and recognition, but after that behavioral change is made, seeing data as a transactional byproduct, then an asset, and finally something to be leveraged occurs swiftly — as reflected in quadrant three of figure 2.

Past the point of data governance action (data cleansing, consolidating, and securing), capitalization on the fruits of quality data may occur. This capitalization is reflected in the desire to reap the benefits at Standardized, where some independent actors are doing ad hoc analytics, through to the advanced analytics portion of the curve — predictive analytics onwards.

You may be wondering which organizations operate in quadrants two and four of this new data & analytics maturity curve. Certain luxury goods may reside in quadrant two. These goods are valuable because few can afford them and the act of signaling that one can does not come from mature data and analytics reflected in quadrant one, but rather discrete exclusivity. Organizations that are grossly misappropriating funds towards mature data and analytics capabilities regardless of whether or not that allocation is necessary or useful reside in quadrant four of figure 2.

Understanding where on the proposed data & analytics maturity curve your organization resides is essential to understanding the journey ahead and an accurate benchmark against peers. Data use does not start at net zero, but rather with a massive behavioral shift that can not be reflected in the positive quadrants of any data or analytics maturity curve until technical action is taken. Analytics advancements require those positive behavioral gains reflected in the early sections of the Traditional Data Governance Maturity Curve. Using this new maturity curve and modifying other curves to account for such imperative behavioral shifts is the first step in accurately projecting any journey an organization may face.

Consultant / Data & Analytics