AI-Assisted Active Data and AI
Governance

Most businesses today are operating in a distributed computing environment with data spread across multiple types of data stores on-premises, in multiple clouds, in SaaS applications and at the edge. This makes data much harder to find and to govern. Yet, data governance is now very high priority in most organisations not just to remain compliant with legal and regulatory obligations but also to create a high quality, secure data foundation of reusable data products to underpin data and AI initiatives now happening across every department in the enterprise. With data and AI now strategic in the boardroom, data governance has become so important that companies classified as ‘leaders’ regard it as a strength that gives them commercial advantage and not just an initiative to remain compliant with legislation like GDPR.

To date however, data governance in many organisations has been fractured with different tools being used to support different data governance disciplines. This includes the use of different tools for data quality, data privacy, data access security, data sharing and data retention. Also, data catalogs, that can automatically discover and classify data in many different data stores are in often purchased independently of other data governance tools. Therefore, while automated data discovery is possible, to some extent, almost all data governance disciplines are still highly dependent on people manually keying and re-keying policies and other metadata across many different tools to try to ensure data remains governed and that governance policies are consistently enforced across the enterprise.
However, as more data is created and new data sources continue to appear, the challenge of manually understanding all data relationships and manually governing data is becoming almost impossible. In addition, doing this with multiple tools is also challenging as there are no industry standards to exchange metadata across those tools which means data governance tasks often have to be repeated again and again to ensure data is being correctly governed across a distributed data estate.

Given this increasingly difficult challenge, companies are looking for a more automated way to deal with data governance. To do this requires taking data governance to a new level by introducing AI-driven, active data governance. Active data governance is more than just keeping metadata up to date.

This is something that uses AI-driven automatic data discovery and data classification, tag-based policy management, and an AI-driven data governance action framework to continuously govern data more efficiently and effectively. An AI-driven data governance action framework needs to include data governance health metrics to monitor progress, data governance events, automated data governance issue detection, automated verification that actions have occurred, different types of governance
action services, data governance action processes and automated triggering of data governance action processes to ensure the heath of your data continues to improve.

This two-day in-depth class looks at this problem and shows how to successfully implement AI driven active data governance across a distributed data estate. This includes AI-driven active governance of data access security, data privacy, data sharing, data retention data quality and data usage. The class looks at the business problems caused by poorly governed data and how it can seriously impact business operations, cause unplanned operational costs, and destroy confidence in
accuracy of BI, machine learning model predictions and recommendations and Generative AI.

It also looks at requirements for AI-driven active data governance. Having understood the requirements, you will learn what should be in a governance programme. This includes data governance roles and responsibilities, processes, policies, technologies, and data governance capabilities to govern data across a distributed data estate . It looks at how to implement AI-driven active data governance by breaking the data governance problem down into a series of steps that need to be implemented and looks how to take advantage of emerging AI-driven data governance platforms to implement this.