Framework Benchmarking for Industrial Data Governance: A Coverage-Oriented Assessment

Abstract

Industry 4.0 industrial environments rely on highly interconnected and data-intensive ecosystems in which effective data governance is critical to ensure data quality, security, interoperability, compliance, and controlled data sharing. Although several data governance reference frameworks are widely adopted in practice, they were primarily conceived for generic corporate contexts, and their suitability for industrial settings remains insufficiently assessed. This paper presents a coverage-oriented benchmarking of established data governance frameworks with respect to the specific challenges of industrial data governance. Eleven challenges have been identified in prior literature, operationalised into thirtythree measurable items and evaluated using a five-level Likert scale (0-4) based exclusively on authoritative primary framework documentation.. Item-level scores were aggregated into normalised challenge-level and overall coverage indices, enabling systematic comparison across frameworks. The analysis benchmarks four reference frameworks: DAMA-DMBOK, ISACA-COBIT 5, the Data Governance Institute (DGI) framework, and IBM's data governance framework. Results reveal substantial but non-uniform coverage across frameworks: DAMA-DMBOK achieves nearcomplete coverage, whereas IBM, DGI, and ISACA-COBIT 5 exhibit moderate and heterogeneous coverage, with recurrent gaps in privacy conditions, human-related governance risks, scalability across expanding industrial data ecosystems, and operational rules for cross-boundary data sharing and reuse. The proposed benchmarking approach provides a transparent and reproducible basis for framework selection and adaptation in Industry 4.0 contexts and highlights the need for integrative governance models tailored to industrial data ecosystems.