DMMM: Data Management Maturity Model

Abstract

The assessment of the digital transformation progress is essential to understand and undertake in order to evaluate the level of maturity of data-driven companies, and to plan for improvement actions. For this purpose, we developed a maturity model assessment. The value proposition is to evaluate the current maturity state of an enterprise from a data and information management point of view while envisioning an evolution path from the current state to the target state. In this paper, we present a new perspective on how to construct maturity models to assess companies’ maturity in terms of data management and advanced analytics with a focus on building a set of tools to ease the application of our model and create a fact-based roadmap for evolution. Our Data Management Maturity Model (DMMM) was designed to support the digital transformation from an initial level to an optimized one. It covers the different aspects that can be encountered such as, the organizational structure, the systems, the data dimensions, and operations. This paper is also a representation of the technical tools we developed to ease their implementation through the DMMM user interface. It depicts the methodologies behind the development of the maturity scoring system, the model architecture, the assessment practice as well as the maturity levels resulting from the evaluation. Additionally, we set forth the technicalities behind the model capabilities, their mapping for a data-centric vision, and their linkage that brings consistency and traceability between the latter.