Cloud-Based Data Lakes for Enterprise Knowledge Management

  • Authors

    • Dr. Silvia Diallo School of Multidisciplinary Studies, River State university, Nigeria. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2021PI7F3M

    Published 03-04-2021

  • Cloud Computing, Data Lake, Knowledge Management, Big Data Analytics, Metadata Management, Enterprise Information Systems, Machine Learning, Data Governance

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    [1]
    S. Diallo, “Cloud-Based Data Lakes for Enterprise Knowledge Management”, IJADSMC, vol. 4, no. 1, pp. 01–15, Mar. 2021, doi: 10.67228/30713498/IJADSMC-2021PI7F3M.

  • Abstract

    Cloud-based data lakes have proven to be a cornerstone architecture of enterprise knowledge management (EKM) where an organization can both store, integrate, and analyze vast amounts of heterogeneous data in the native form. The traditional data warehouses have strict constraints on its schema that restricts flexibility and scalability in managing unstructured and semi-structured data. Conversely, data lakes are able to offer schema-on-read services and resource provisioning elasticity, which facilitates the use of advanced analytics, artificial intelligence (AI) and knowledge discovery. This paper explores the architecture, the functionality, and the governance model of cloud based data lakes as it applies to the enterprise knowledge management. There is a thorough review of the literature available, indicating the shift on the centralized data repository towards the various cloud-based knowledge platforms. It offers a conceptual approach to the study that unites metadata management, security policies, and knowledge extraction through the use of machine learning. The experimental outcomes were also using a simulated enterprise dataset showing an increase in the accessibility of data, reuse of knowledge, and latency in making decisions. The results show cloud-based data lakes can greatly ensure better enterprise knowledge work processes through scalability, interoperability, and efficiency of analysis. The paper then ends by discussing the problem of implementing the results and research opportunities in the future of semantic enrichment and autonomous data governance.

  • References

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