Education

AI-Assisted Active Data and AI Governance

– The Route to Healthy Data, Analytics and AI

Request information on running this seminar as an onsite (can be given as Live Streaming training)

Click here for a full brochure

DATES

12-13 June 2025 – Amsterdam (Agent: Quest for Knowledge)

19-20 June 2025 – Italy, Live Streaming (Agent: Technology Transfer)

OVERVIEW

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, datagovernance 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.  The class will cover:

• Data governance disciplines including data curation, data quality, data privacy, data access security, data sharing, data retention, and data usage
• Current problems with data governance today
• Requirements to dramatically improve data governance using AI and automation
• The need for an integrated data governance platform, AI augmentation and AI-automation
• Establishing health metrics to measure effectiveness of your data governance program
• Understanding the core AI-assisted data governance services you need to discover, classify and curate data
• Creating a Data Governance Action Framework for your enterprise
• Data governance observability – monitoring the health of your data
• AI-Assisted data governance action automation
• Implementing AI-assisted governance of different data governance disciplines
• Implementing AI governance to manage and avoid risk

AUDIENCE

This seminar is intended for CDOs, CIO’s, Heads of Data Governance, CISOs, Business Analysts, data scientists, BI Managers, data warehousing professionals, data architects, solution architects,  Data strategists, Database administrators, IT consultants.

LEARNING OBJECTIVES

Attendees will learn how to set up an AI-driven enterprise data governance program to systematically govern data and content across their distributed data estate. Using a data governance framework and key technologies like data catalogs,  automated data discovery, automated data classification , machine learning, Generative AI, AI-agents, decision automation, data marketplaces and workflow. Attendees will learn what is needed to discover, classify and govern data and content. This includes creation of health controls and how to implement AI-assisted data and AI governance. It also includes automated data discovery, data curation, data access security, data privacy, data loss prevention, data sharing, data retention, and data quality.

MODULES

• Module 1: What is active data governance and why do we need it?
• Module 2: What are the requirements for AI-driven active data governance to govern data across a distributed data estate?
• Module 3: Foundational Active Data Governance services – The importance of a business glossary
• Module 4: Foundational Active Data Governance services – Auto data discovery, cataloguing and mapping to a business glossary
• Module 5: Foundational Active Data Governance services – AI-driven data and content classification
• Module 6: Implementing AI-Assisted governance of data security across your distributed data estate
• Module 7: Implementing AI-Assisted governance of data privacy across your distributed data estate
• Module 8: Implementing AI-Assisted governance of data retention across your distributed data estate
• Module 9: Implementing AI-Assisted governance of data sharing across your distributed data estate
• Module 10: Implementing AI-Assisted governance of data quality across your distributed data estate
• Module 11: Implementing AI-Governance

Click here for a full brochure

Back