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Overview of Data Governance

Business talk | English

Theatre 25: Track 1

Wednesday - 11.45 to 12.25 - Business

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Effective data governance is foundational for AI adoption in enterprise, but it’s an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more—partly based on interviewing experts in this field about issues and best practices. Join in to learn what impact machine learning has on data governance and vice versa.

On the one hand, poor data governance can lead to system data quality issues, lack of data availability, and other risks, which means that the people within an organization cannot leverage data effective, thus limiting ROI. On the other hand, there’s a galaxy of compliance issues, aimed at preventing risks if people leverage data inappropriately. (While many are quick to mention GDPR, there are many standards in play, depending on the business vertical.) However, several other issues create drivers for data governance: how security concerns are reshaping the structure of web apps; ethics, bias, and others needs for ML transparency; the implications of “democratizing data and analytics,” both pro and con; the priority for reproducibility in analytics workflows; and unexpected ways in which open source is evolving rapidly in highly regulated environments.
Although many practices emerged from the era of data warehouses, big data changed the game and began drawing attention from regulators. Facebook, Twitter, and other tech giants now testify before US senators, who in turn struggle to grasp basic concepts in IT. Meanwhile the IT landscape is evolving rapidly: new forms of hardware and networking, serverless cloud offerings, and edge computing, for example, are redefining even the basic concepts related to data governance. Ultimately, risk management plays the “thin edge of the wedge” for these changes in enterprise, while the mantle of responsibility for data governance moves toward the emerging chief data officer role.