Abstract
AbstractAs organizations accumulate vast amounts of data for analysis, a significant challenge remains in fully understanding these datasets to extract accurate information and generate real-world impact. Particularly, the high dimensionality of datasets and the lack of sufficient documentation, specifically the provision of metadata, often limit the potential to exploit the full value of data via analytical methods. To address these issues, this study proposes a hybrid approach to metadata generation, that leverages both the in-depth knowledge of domain experts and the scalability of automated processes. The approach centers on two key design principles—semanticization and contextualization—to facilitate the understanding of high-dimensional datasets. A real-world case study conducted at a leading pharmaceutical company validates the effectiveness of this approach, demonstrating improved collaboration and knowledge sharing among users. By addressing the challenges in metadata generation, this research contributes significantly toward empowering organizations to make more effective, data-driven decisions.
Funder
Karlsruher Institut für Technologie (KIT)
Publisher
Springer Science and Business Media LLC
Subject
Management of Technology and Innovation,Marketing,Computer Science Applications,Economics and Econometrics,Business and International Management
Reference75 articles.
1. Abdel-Karim, B. M., Pfeuffer, N., & Hinz, O. (2021). Machine learning in information systems - A bibliographic review and open research issues. Electronic Markets, 31(3), 643–670. https://doi.org/10.1007/s12525-021-00459-2
2. Abedjan, Z., Golab, L., & Naumann, F. (2015). Profiling relational data: A survey. VLDB Journal, 24(4), 557–581.
3. Alt, R. (2021). How to organize for AI? An interview with Yao-Hua Tan. Electronic Markets, 31(3), 639–642. https://doi.org/10.1007/s12525-021-00497-w
4. Arnab. (2020). Microsoft Azure Predictive Maintenance | Kaggle. https://www.kaggle.com/datasets/arnabbiswas1/microsoft-azure-predictive-maintenance
5. Axenie, C., & Bortoli, S. (2020). Predictive maintenance dataset. https://doi.org/10.5281/ZENODO.3653909