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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 in terms of data capabilities and to plan for improvement actions. Maturity models evaluate the performance and the execution of processes in terms of the predefined goals and strategies that the organisation has set for its long-term alignment with the value and culture. 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 and to draw the target maturity state that an organisation would like to reach based on the resources, goals, and ambitions. This model envisions and proposes an evolution path from the current state to the target state. This can be used as a compass to navigate throughout the digital transformation journey.
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 to evolve from the current state to the target maturity state, which is also defined by this same model. Our Data Management Maturity Model (DMMM) model was designed to support the digital transformation from an initial level to an optimised one. It covers the different aspects that can be encountered in the majority of organisations: the organisational 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 of the different data dimensions present across organisations. Additionally, we set forth the technicalities behind the capabilities of the model, their mapping for a data-centric vision, and their linkage that brings consistency and traceability between the latter.
Syrine Ferjaoui, Oumaima Belghith, Cyrine Zitoun, Sabri Skhiri, DMMM: Data Management Maturity Model, Proc. of the International Conference on Advanced Enterprise Information System, 2021.
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The paper can be downloaded below.