Webinar Recap: Operationalising Data Governance – Turning Theory Into a Reality
In the previous sessions of the series, we explored the various elements that make up the data governance stack. As we arrived at the final session, I was joined by panellists Jan Ulrych, VP of Research and Education at Manta and Christopher Glover, Field CTO at BigID to discuss data governance, its value, challenges, and its crucial role in enhancing efficiency and decision-making.
The business case for data governance and implementation strategies
The webinar began with a focus on the business case for developing data governance programs, particularly their potential impact on financial, organisational, and operational aspects. We discussed how the implementation of these programs should be based on each organisation’s specific compliance requirements, needs, and challenges. Jan suggested an agile methodology with short sprints, enabling a fail-fast approach to quickly address any emerging issues.
Christopher recommended an impact-based approach and advised setting up specific details for a phased governance program. He also emphasised the importance of finding specific use cases, implementing quick, measurable deliverables, and offering demonstrable value that helps team members be successful in their roles.
Challenges in data governance and the role of the Chief Data Officer
One of the key topics discussed was the challenges faced during the implementation of data governance programs. These included obtaining the right level of executive sponsorship and other internal issues, like the typical short tenure of a Chief Data Officer (CDO). Both Christopher and Jan offered strategies to define or quantify the value of a data governance program, including setting smaller, incremental goals to improve organisational efficiency.
We also touched upon the ‘failure to launch’ concept, emphasising the need for business buy-in and operational readiness to ensure the sustainable success of data projects. Jan stressed the need to understand challenges from different perspectives and suggested that governance should provide value to all involved parties.
The future of data governance: AI, ML, and data mesh
As we delved deeper into the discussion, Jan brought up the increasing importance of governance with the advent of concepts such as data mesh, AI, and Machine Learning (ML). He suggested that the governance process must accompany these new approaches for efficient implementation and use. Christopher echoed these views, proposing that AI could be a tool to help manage governance and that governance can be applied to different areas of the organisation.
Securing departmental buy-in and revamping data governance approaches
In the latter part of the webinar, we explored the difficulty of securing departmental buy-in for data governance changes. Christopher proposed the need to demonstrate the advantages of improved governance, such as streamlining processes and ensuring data safety. He noted that leaders need to perceive these changes as solutions rather than burdens.
We then discussed the need to revamp complete data governance approaches. Jan stressed the need for iterative improvements and quick wins over a blanket approach. He suggested starting with a single department that has major room for improvement, such as one with a high number of changes or incidents.
The final session was a fitting conclusion to the webinar series as it gave a thorough overview of the significance of data governance, its challenges and its future.
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