Executive Summary

Measuring data maturity has long been locked in the domain of those who often looked at data for data’s sake, wholly apart from the context of value creation.

This paper examines how we can increase the degree of sophistication we apply to both our inert data and our current data
in order to achieve value creation within the context of our business environment. Increasing that sophistication simultaneously increases our ability to move beyond simple data aggregation and into the application of data science techniques to begin to predictively model our maintenance processes and operational activities with the eventual goal of scaling our activities through partial, or even full, automation.

We also discuss The Action Deficit problem that occurs when an organisation or industry fails to act on the decision support output provided by its analyses, and how it must be overcome through learning in order to transition from old, habitual ways to new data-driven frontiers.