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

• URL: http://arxiv.org/abs/2409.15137v1
• Date: Mon, 23 Sep 2024 15:41:56 GMT
• Title: Data governance: A Critical Foundation for Data Driven Decision-Making in Operations and Supply Chains
• Authors: Xuejiao Li, Yang Cheng, Charles Møller,
• Abstract summary: This study aims to call attention on Data Governance (DG) research in the field of operations and supply chain management (OSCM) 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.
• Score: 5.909817496975273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Data Issues in Industrial AI System: A Meta-Review and Research Strategy [10.540603300770885]
  Artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. How to address these data issues stands as a significant concern confronting both industry and academia.
  arXiv  Detail & Related papers  (2024-06-22T08:36:59Z)
• A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective [16.904588676267526]
  We identify two major related research fields, domain shift and concept drift. In this review, we regroup domain shift and concept drift into a single research problem, namely the data change problem. We propose a three-phase problem categorization scheme to link the key ideas in the two technical fields.
  arXiv  Detail & Related papers  (2024-02-20T01:16:01Z)
• Uncovering the Hidden Potential of Event-Driven Architecture: A Research Agenda [1.5501208213584152]
  Event-driven architecture has been widely adopted in the software industry. However, little is known about the effects of event-driven architecture on performance, stability, and software monitoring. This article proposes an agenda for future research based on the scarcity of literature in the field of event-oriented architecture.
  arXiv  Detail & Related papers  (2023-08-10T01:03:47Z)
• Advanced Data Augmentation Approaches: A Comprehensive Survey and Future directions [57.30984060215482]
  We provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classification, object detection and semantic segmentation.
  arXiv  Detail & Related papers  (2023-01-07T11:37:32Z)
• Mapping STI ecosystems via Open Data: overcoming the limitations of conflicting taxonomies. A case study for Climate Change Research in Denmark [0.0]
  Science, Technology and Innovation (STI) decision-makers often need to have a clear vision of what is researched and by whom to design effective policies. A major challenge to be faced in this context is the difficulty in accessing the relevant data and in combining information coming from different sources. Here, we present a proof-of-concept study of the use of Open Resources to map the research landscape on the Sustainable Development Goal (SDG) 13-Climate Action, for an entire country, Denmark, and we map it on the 25 ERC panels.
  arXiv  Detail & Related papers  (2022-09-19T10:59:39Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection [136.71290968343826]
  Trustworthy graph learning (TwGL) aims to solve the above problems from a technical viewpoint. In contrast to conventional graph learning research which mainly cares about model performance, TwGL considers various reliability and safety aspects.
  arXiv  Detail & Related papers  (2022-05-20T08:10:35Z)
• A survey study of success factors in data science projects [0.0]
  Agile data science lifecycle is the most widely used framework, but only 25% of the survey participants state to follow a data science project methodology. Professionals who adhere to a project methodology place greater emphasis on the project's potential risks and pitfalls.
  arXiv  Detail & Related papers  (2022-01-17T09:50:46Z)
• Domain Generalization: A Survey [146.68420112164577]
  Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning. For the first time, a comprehensive literature review is provided to summarize the ten-year development in DG.
  arXiv  Detail & Related papers  (2021-03-03T16:12:22Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements [55.33496599723126]
  disinformation including fake news has become a global phenomenon due to its explosive growth. Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation.
  arXiv  Detail & Related papers  (2020-01-02T21:01:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.