Data governance: A Critical Foundation for Data Driven Decision-Making in Operations and Supply Chains

TL;DR

This paper emphasizes the importance of data governance in operations and supply chains within Industry 4.0, identifying key data issues and proposing a research framework to improve data management practices.

Contribution

It highlights research gaps in data governance for operations and supply chains, based on literature review and case studies, and proposes a new research framework.

Findings

Identified four main causes of data issues in industry.

Developed a three-pronged research framework for data governance.

Highlighted the need for further research and practical guidance in DG.

Abstract

In the context of Industry 4.0, the manufacturing sector is increasingly facing the challenge of data usability, which is becoming a widespread phenomenon and a new contemporary concern. In response, Data Governance (DG) emerges as a viable avenue to address data challenges. This study aims to call attention on DG research in the field of operations and supply chain management (OSCM). Based on literature research, we investigate research gaps in academia. Built upon three case studies, we exanimated and analyzed real life data issues in the industry. Four types of cause related to data issues were found: 1) human factors, 2) lack of written rules and regulations, 3) ineffective technological hardware and software, and 4) lack of resources. Subsequently, a three-pronged research framework was suggested. This paper highlights the urgency for research on DG in OSCM, outlines a research pathway for fellow scholars, and offers guidance to industry in the design and implementation of DG strategies.