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Data Governance in the Context of Data Fabric and Data Mesh

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Data Fabric and Data Mesh Approaches with AI

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Abstract

Companies generate and manage large amounts of sensitive information about their employees, customers, and business during their operational and analytical activities. This information gives companies a competitive advantage and at the same time brings great risks. The exposure of sensitive information can lead to serious consequences, such as lawsuits. Therefore, companies need to implement a purposeful and well-planned data governance platform.

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Notes

  1. 1.

    See Reference [1] for more information on DAMA-DMBOK2.

  2. 2.

    See Reference [2] for more information on Forrester’s report.

  3. 3.

    See Reference [3] for more information on Gartner’s report.

  4. 4.

    Please, review the section on trustworthy AI in Chapter 5.

  5. 5.

    See Chapter 11 for more information on data architecture.

  6. 6.

    See Reference [4] for more information about metadata.

  7. 7.

    See Reference [5] for more information about master data.

  8. 8.

    Chapter 8 and Chapter 13 explain these concepts in more detail.

  9. 9.

    See Reference [6] for more details on GDPR in the EU.

  10. 10.

    See more details about NLP in Chapter 6.

  11. 11.

    See Reference [7] for more information about quality dimension.

  12. 12.

    See more information about duplicate record removal in Chapter 8.

  13. 13.

    Please, review the section “Automated Data Quality Assessment” in the previous chapter.

Author information

Authors and Affiliations

  1. Sindelfingen, Germany

    Eberhard Hechler

  2. Charlotte, NC, USA

    Maryela Weihrauch

  3. San Jose, CA, USA

    Yan (Catherine) Wu

Authors
  1. Eberhard Hechler
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  2. Maryela Weihrauch
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  3. Yan (Catherine) Wu
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© 2023 The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature

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Hechler, E., Weihrauch, M., Wu, Y.(. (2023). Data Governance in the Context of Data Fabric and Data Mesh. In: Data Fabric and Data Mesh Approaches with AI. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9253-2_15

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