Here's how you can improve data governance by leveraging new technology.

-Use AI-driven data catalogs that automatically scan, classify, and tag data across your organization. This helps in maintaining a centralized inventory of data assets, their lineage, and associated metadata. -Employ machine learning algorithms to monitor data quality in real-time. -Adopt encryption and tokenization techniques to protect sensitive data both at rest and in transit. -Implement tools that assist in regulatory compliance, such as consent management platforms and data anonymization solutions. -Develop dashboards that provide real-time visibility into data usage, access patterns, and compliance metrics.

There are over 30 data governance software tools that I would classify as true data governance solutions. There are at least 100 or more products that do some limited scope data governance. It takes many years to understand the tool landscape and I assure you that the vendors will not readily inform you of their limitations. For example, it takes months just to properly configure the security, IT authorizations, build user roles, customize the user role experience (most users don't want to see everything the tool can do as it complicates their experience), data classifications, metadata structure, and initial data stewardship workflows.

Many of the cloud vendors provide compliance and risk management solutions which can support your data governance initiatives across hybrid data estates. Capabilities such as information protection (sensitivity labels), electronic discovery (identifying assets), data lifecycle management are available through services such as Microsoft Purview. This also supports all of the major regulations such as GDPR and HIPPA - and there is also the option to customise these capabilities if needed. I also like the fact you there are out of the box classifiers (identification of legal documents or contracts, IP etc.) and also the ability to implement custom classifiers for more specific information record/ document types.

The decision between on-prem and cloud installations depend on many factors including: client preference, location of existing and future applications, user locations, security requirements, and vendor capabilities.

Cloud solutions, depending on their flavor, offer little governance or security. This is by design. Solution providers want the accountability of data mishaps to fall on the client, not themselves. The governance features (generic) listed for a random cloud offering are data management features, not data governance features. Applying data governance policies uniformly across all data assets (this is also something not usually done, as the ask is limited to critical data) is also a data management responsibility.

Start by implementing advanced data governance solutions that automate policy enforcement, data lineage tracking, and access control mechanisms. Utilise AI and machine learning algorithms to identify and mitigate data quality issues in real-time, ensuring data accuracy and consistency across systems. Deploy blockchain technology to secure data transactions and maintain an immutable record of data lineage and modifications. Integrate metadata management systems to provide comprehensive visibility into data assets, facilitating easier discovery, understanding, and utilisation. Embrace cloud based solutions for scalable and agile data governance practices, enabling rapid deployment and updates without compromising security or compliance.