Bridging the Gaps: A Comprehensive Review of Data Management Capability Maturity Models in the Digital Era

As digital transformation accelerates, organisations are increasingly investing in data management infrastructures and setting related strategic goals. However, expected returns are often not realised due to ineffective data management practices. To address this, Capability Maturity Models (CMMs) have been developed to assess and enhance data management capabilities across areas such as strategy, policy, leadership, culture, processes, and value-added activities. This study presents a Systematic Literature Review (SLR) covering 28 studies published between 2012 and 2022, with an additional snowballing extension up to January 2024 related to data management maturity, encompassing both fully established Capability Maturity Models (CMMs) and conceptually significant contributions. The studies were evaluated based on two main criteria: (1) the application of a Design Science approach, comprising four key steps—problem definition, comparison of existing models, model development, and evaluation; and (2) the comprehensiveness, clarity, and usability of the models, assessed through four sub-criteria. The analysis identified eleven major shortcomings in the existing literature, including limited synthesis of prior work, lack of scientifically grounded iterative development, insufficient validation and verification, and inadequate coverage of the complete data lifecycle—particularly in relation to legal and ISO compliance. Moreover, critical elements such as a Stage 0 for data awareness, transition tables, and practical self-assessment schemes were often missing. These findings underscore the need for a scientifically rigorous and practically applicable CMM that addresses these gaps and offers a holistic framework for advancing data management maturity.