Governing the meaning of industrial data: a semantic information governance framework and maturity model for intelligent industry

Authors

1 Department of Management, Marketing, & Information Systems, Sam Houston State University, Houston, 77341, US
Corresponding author: sxr177@shsu.edu

DOI:

https://doi.org/10.63646/EFGN7343

Keywords:

Data stewardship, semantic interoperability, industrial internet of things, asset administration shell, unified namespace, capability assessment, IT/OT integration

Abstract

Industrial organizations increasingly connect operational technology to enterprises, analytics and artificial-intelligence applications, yet connectivity alone does not make industrial data interpretable or reusable. Event-driven backbones, unified namespaces and asset-centered digital representations organize how data move and where context is stored, but they do not determine what the exchanged information means, who is accountable for that meaning, or how it is allowed to change. This study addresses the resulting governance gap. Following a design-science research process, a semantic information governance framework is derived from international standards, data-governance bodies of knowledge, scholarly literature on industrial integration and documented failure modes of multi-protocol deployments. The framework organizes 25 recurring governance decisions into five domains: semantic definition, model curation, provenance and traceability, protection and sovereignty, and stewardship and consumability. Each decision is described through five maturity levels, ranging from meaning that exists only in integration code to definitions that are machine-readable, automatically validated and published with declared fitness for named consumers. Assessment uses an artefact-based rubric and a weakest-link aggregation rule, reporting a capability profile rather than a single score, because interpretation fails at the least-governed decision rather than on average. The framework is demonstrated analytically through three industrial archetypes, namely brownfield retrofit monitoring, a multi-vendor line integrated with execution and analytics systems, and a cross-organizational service interaction, and is evaluated against published requirements for maturity-model development. The demonstration indicates that bottlenecks concentrate in identifier schemes, time semantics, the boundary between events and persistent models, the treatment of non-nominal information, and change control, rather than in protocol coverage. The framework offers an assessable, incremental instrument for deciding what to govern first when preparing operational information for reuse.

References

[1] European Commission, Directorate-General for Research and Innovation, M. Breque, L. De Nul, A. Petridis, Industry 5.0: Towards a sustainable, human-centric and resilient European industry, Publications Office of the European Union, Luxembourg, 2021. https://doi.org/10.2777/308407.

[2] X. Xu, Y. Lu, B. Vogel-Heuser, L. Wang, Industry 4.0 and Industry 5.0 — Inception, conception and perception, Journal of Manufacturing Systems 61 (2021) 530–535. https://doi.org/10.1016/j.jmsy.2021.10.006.

[3] C. Gröger, There is no AI without data, Communications of the ACM 64 (11) (2021) 98–108. https://doi.org/10.1145/3448247.

[4] N. Sambasivan, S. Kapania, H. Highfill, D. Akrong, P. Paritosh, L.M. Aroyo, “Everyone wants to do the model work, not the data work”: Data cascades in high-stakes AI, in: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, ACM, New York, 2021, pp. 1–15. https://doi.org/10.1145/3411764.3445518.

[5] N. Naik, Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP, in: 2017 IEEE International Systems Engineering Symposium (ISSE), Vienna, Austria, 2017, pp. 1–7. https://doi.org/10.1109/SysEng.2017.8088251.

[6] S. Profanter, A. Tekat, K. Dorofeev, M. Rickert, A. Knoll, OPC UA versus ROS, DDS, and MQTT: Performance evaluation of Industry 4.0 protocols, in: 2019 IEEE International Conference on Industrial Technology (ICIT), Melbourne, Australia, 2019, pp. 955–962. https://doi.org/10.1109/ICIT.2019.8755050.

[7] C. Wagner, J. Grothoff, U. Epple, R. Drath, S. Malakuti, S. Grüner, M. Hoffmeister, P. Zimmermann, The role of the Industry 4.0 Asset Administration Shell and the digital twin during the life cycle of a plant, in: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, Cyprus, 2017, pp. 1–8. https://doi.org/10.1109/ETFA.2017.8247583.

[8] E.Y. Nakagawa, P.O. Antonino, F. Schnicke, R. Capilla, T. Kuhn, P. Liggesmeyer, Industry 4.0 reference architectures: State of the art and future trends, Computers & Industrial Engineering 156 (2021) 107241. https://doi.org/10.1016/j.cie.2021.107241.

[9] V. Jirkovský, M. Obitko, V. Mařík, Understanding data heterogeneity in the context of cyber-physical systems integration, IEEE Transactions on Industrial Informatics 13 (2) (2017) 660–667. https://doi.org/10.1109/TII.2016.2596101.

[10] A. Zeid, S. Sundaram, M. Moghaddam, S. Kamarthi, T. Marion, Interoperability in smart manufacturing: Research challenges, Machines 7 (2) (2019) 21. https://doi.org/10.3390/machines7020021.

[11] V. Khatri, C.V. Brown, Designing data governance, Communications of the ACM 53 (1) (2010) 148–152. https://doi.org/10.1145/1629175.1629210.

[12] R. Abraham, J. Schneider, J. vom Brocke, Data governance: A conceptual framework, structured review, and research agenda, International Journal of Information Management 49 (2019) 424–438. https://doi.org/10.1016/j.ijinfomgt.2019.07.008.

[13] I. Alhassan, D. Sammon, M. Daly, Data governance activities: an analysis of the literature, Journal of Decision Systems 25 (sup1) (2016) 64–75. https://doi.org/10.1080/12460125.2016.1187397.

[14] Y. Lu, Industry 4.0: A survey on technologies, applications and open research issues, Journal of Industrial Information Integration 6 (2017) 1–10. https://doi.org/10.1016/j.jii.2017.04.005.

[15] O. Givehchi, K. Landsdorf, P. Simoens, A.W. Colombo, Interoperability for industrial cyber-physical systems: An approach for legacy systems, IEEE Transactions on Industrial Informatics 13 (6) (2017) 3370–3378. https://doi.org/10.1109/TII.2017.2740434.

[16] S. Mittal, M.A. Khan, D. Romero, T. Wuest, A critical review of smart manufacturing and Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs), Journal of Manufacturing Systems 49 (2018) 194–214. https://doi.org/10.1016/j.jmsy.2018.10.005.

[17] A. Schumacher, S. Erol, W. Sihn, A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises, Procedia CIRP 52 (2016) 161–166. https://doi.org/10.1016/j.procir.2016.07.040.

[18] R. Venanzi, G. Di Modica, L. Foschini, P. Bellavista, Towards IT/OT integration in industry digitalization: A comprehensive survey, Journal of Network and Computer Applications 245 (2026) 104373. https://doi.org/10.1016/j.jnca.2025.104373

[19] L. Freitas, M. Silva, G. Vale, C. Avram, H. Lopes, F. Pereira, N. Leal, J. Machado, OPC UA and MQTT performance analysis within a unified namespace context, Internet of Things 33 (2025) 101734. https://doi.org/10.1016/j.iot.2025.101734

[20] D. Chen, G. Doumeingts, F. Vernadat, Architectures for enterprise integration and interoperability: Past, present and future, Computers in Industry 59 (7) (2008) 647–659. https://doi.org/10.1016/j.compind.2007.12.016.

[21] European Commission, Directorate-General for Informatics, New European Interoperability Framework: Promoting seamless services and data flows for European public administrations, Publications Office of the European Union, Luxembourg, 2017. https://doi.org/10.2799/78681.

[22] Y. Lu, K.C. Morris, S. Frechette, Current standards landscape for smart manufacturing systems, NISTIR 8107, National Institute of Standards and Technology, Gaithersburg, MD, 2016. https://doi.org/10.6028/NIST.IR.8107.

[23] A. Busboom, Automated generation of OPC UA information models — A review and outlook, Journal of Industrial Information Integration 39 (2024) 100602. https://doi.org/10.1016/j.jii.2024.100602.

[24] E. Tantik, R. Anderl, Integrated data model and structure for the Asset Administration Shell in Industrie 4.0, Procedia CIRP 60 (2017) 86–91. https://doi.org/10.1016/j.procir.2017.01.048.

[25] S.R. Bader, M. Maleshkova, The Semantic Asset Administration Shell, in: M. Acosta, P. Cudré-Mauroux, M. Maleshkova, T. Pellegrini, H. Sack, Y. Sure-Vetter (Eds.), Semantic Systems. The Power of AI and Knowledge Graphs (SEMANTiCS 2019), Lecture Notes in Computer Science, vol. 11702, Springer, Cham, 2019, pp. 159–174. https://doi.org/10.1007/978-3-030-33220-4_12.

[26] S. Cavalieri, M.G. Salafia, Insights into mapping solutions based on OPC UA information model applied to the Industry 4.0 Asset Administration Shell, Computers 9 (2) (2020) 28. https://doi.org/10.3390/computers9020028.

[27] R.Y. Wang, D.M. Strong, Beyond accuracy: What data quality means to data consumers, Journal of Management Information Systems 12 (4) (1996) 5–33. https://doi.org/10.1080/07421222.1996.11518099.

[28] M.D. Wilkinson, M. Dumontier, I.J. Aalbersberg, et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data 3 (2016) 160018. https://doi.org/10.1038/sdata.2016.18.

[29] B. Otto, M. Jarke, Designing a multi-sided data platform: findings from the International Data Spaces case, Electronic Markets 29 (4) (2019) 561–580. https://doi.org/10.1007/s12525-019-00362-x.

[30] J. Zrenner, F.O. Möller, C. Jung, A. Eitel, B. Otto, Usage control architecture options for data sovereignty in business ecosystems, Journal of Enterprise Information Management 32 (3) (2019) 477–495. https://doi.org/10.1108/JEIM-03-2018-0058.

[31] J. Becker, R. Knackstedt, J. Pöppelbuß, Developing maturity models for IT management — A procedure model and its application, Business & Information Systems Engineering 1 (3) (2009) 213–222. https://doi.org/10.1007/s12599-009-0044-5.

[32] M. Röglinger, J. Pöppelbuß, J. Becker, Maturity models in business process management, Business Process Management Journal 18 (2) (2012) 328–346. https://doi.org/10.1108/14637151211225225.

[33] R. Wendler, The maturity of maturity model research: A systematic mapping study, Information and Software Technology 54 (12) (2012) 1317–1339. https://doi.org/10.1016/j.infsof.2012.07.007.

[34] K. Peffers, T. Tuunanen, M.A. Rothenberger, S. Chatterjee, A design science research methodology for information systems research, Journal of Management Information Systems 24 (3) (2007) 45–77. https://doi.org/10.2753/MIS0742-1222240302.

[35] A.R. Hevner, S.T. March, J. Park, S. Ram, Design science in information systems research, MIS Quarterly 28 (1) (2004) 75–105. https://doi.org/10.2307/25148625.

[36] B.T. Hazen, C.A. Boone, J.D. Ezell, L.A. Jones-Farmer, Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications, International Journal of Production Economics 154 (2014) 72–80. https://doi.org/10.1016/j.ijpe.2014.04.018.