Analytics maturity model

An analytics maturity model lays out stages an organization typically passes through as its use of data becomes more capable and more embedded in how decisions get made, commonly moving from scattered, ad hoc reporting toward governed, self-service analytics and eventually toward predictive and prescriptive use of data. Organizations use it to self-assess where they currently stand and to plan what to invest in next.

Maturity is usually described along two related dimensions: the sophistication of analysis itself, often summarized as a progression from descriptive to diagnostic to predictive to prescriptive analytics, and the organizational side, data governance, tooling, skills and culture, that determines whether that analysis actually gets used. No single maturity model is the industry standard; several consulting firms and vendors publish their own versions with different stage counts and labels, so the value is in the underlying diagnostic more than any specific framework's wording.

Maturity assessments matter because they give leadership a shared vocabulary for what "better at analytics" means and help justify investment in things like a data strategy or an analytics center of excellence, and can inform how Analytics ROI is framed at each stage. A common pitfall is treating the stages as a straight ladder that every part of the organization must climb in lockstep, when in practice different business units and data domains often sit at different maturity levels at the same time.