Understanding and Defining Dark Data for the Manufacturing Industry
| Autor: | Angelo Corallo, Manuela Marra, Mariangela Lazoi, Anna Maria Crespino, Vito Del Del Vecchio |
|---|---|
| Přispěvatelé: | Corallo, A., Crespino, A. M., Del Vecchio, V., Lazoi, M., Marra, M. |
| Rok vydání: | 2023 |
| Předmět: |
Big Data
Value (ethics) Data model dark data business.industry Computer science Strategy and Management Data management Digital transformation Companie Object recognition Focus group Data science Dark data Field (computer science) Tools manufacturing Systematic review Manufacturing Manufacturing Companies Big Data Tools Object recognition Data models Data mining Big data data management industry 4.0 Electrical and Electronic Engineering business Data mining |
| Zdroj: | IEEE Transactions on Engineering Management. 70:700-712 |
| ISSN: | 1558-0040 0018-9391 |
| DOI: | 10.1109/tem.2021.3051981 |
| Popis: | In industry 4.0 and digital transformation scenarios, manufacturing companies face the challenges of the exponential growth in the volume of data. A considerable part of the data that enterprises generate in large quantities, at a high speed, and in different forms can be defined as dark data. Companies are not able to extract their potential value for data management, storage, and maintenance issues, and, due to their unstructured form, they become unknown together with their informative value. Some researchers and professionals have addressed the issue of dark data but none of them have focused on the specificity of the manufacturing industry or on the data generated in it. Based on a lack in the literature, in this article, we explore the dark data through a systematic literature review and three focus groups with manufacturing companies. This article fills the gap and provides a valuable support for manufacturing companies to be aware of the presence of dark data in their scenarios, to identify them, and to stimulate initiatives for dark data exploitation. Furthermore, the study provides for the academic audience a reference paper for addressing future exploration in the field of data management in the companies for improving their innovative and operative capabilities. |
| Databáze: | OpenAIRE |
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