Data Governance Frameworks for Enterprise Analytics Platforms

  • Authors

    • Dr. Jessica Rachel Brown Department of Psychology, University of Toronto, Canada Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2020PII6Q7V

    Published 09-04-2020

  • Data Governance, Enterprise Analytics, Data Quality, Metadata Management, Compliance, Data Stewardship, Governance Frameworks, Data Lifecycle

    Issue

    Vol. 3 No. 2 (2020)

    Section

    Articles

    How to Cite

    [1]
    J. R. Brown, “Data Governance Frameworks for Enterprise Analytics Platforms”, IJADSMC, vol. 3, no. 2, pp. 01–14, Sep. 2020, doi: 10.67228/30713498/IJADSMC-2020PII6Q7V.

  • Abstract

    Before 2019, enterprises were becoming more and more dependent on analytics platforms to derive actionable insights out of large amounts of both structured and unstructured data. Nevertheless, lack of strong data governance systems tended to cause inconsistencies, compliance issues and reduced trust in analytical products. This article is an in-depth examination of data governance models designed to support enterprise analytics platforms, its architectural elements, implementation plans, and the effects it has on operations. The paper highlights the importance of governance frameworks that guarantee quality, integrity, accessibility, and security of data in distributed systems. The suggested framework blends policy management, metadata management, data stewardship, and compliance monitoring into a single governance framework. It also highlights how governance practices can be aligned with business goals, regulation needs, and technology. This study offers a literature review and methodology review to determine the main issues around data silos, non-standardization, and scale limitations in large organizations. A model of governance lifecycle is presented that includes stages of data acquisition, data validation, data storage, data processing and consumption. The framework utilizes rule-based validation, role-based access control, and audit trail, to promote transparency and accountability. Also, the paper identifies the importance of enterprise data catalogs and lineage tracking to enhance data discoverability and traceability. The findings are that companies that implement organized governance systems record dramatic advancement in the quality of data, compliance rates, and the accuracy in analytics. A comparative analysis indicates quantifiable improvements in operative efficiency and effectiveness in decision making. The paper wraps up by highlighting the need to incorporate governance frameworks in enterprise analytics strategies to promote sustainable data-driven change.

  • References

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