Data governance used to be treated as a control function. Something you did to satisfy compliance, meet a regulator, or tick a box. What came through strongly in the latest Experian Data Governance Masterclass is that this view is increasingly seen as outdated. Rina Gami, co-founder of Konnexus and host of the Lead With Data podcast, opened by noting that the most successful governance teams often operate as enablers, working proactively with the business rather than pushing policies down from a distance.
“The most successful data governance teams are the ones that are driving it as an enabler, rather than pushing down policies and frameworks.”
– Rina Gami, Co-Founder Konnexus and host of Lead With Data
Why the change is happening now
Two forces appear to be driving the shift. First, delivery cycles are accelerating. Belinda Toniolo, data governance leader at RACV, described how work that once involved months of planning can now be stood up in weeks using fast test and learn approaches. Second, AI can amplify the consequences of weak data foundations. Andrew Andrews, regional advocate from the EDM Association highlighted that organisations can spin up proofs of concept quickly, but success rates drop when the underlying data is unclear, inconsistent, or missing context.
“That is a two week process now, where things are being stood up. They are throwing it out there, test and learn.”
– Belinda Toniolo, Head of Data Governance, RACV
AI has not replaced governance. In many cases, it has highlighted gaps
One of the most practical points came from Kylie Kirkby, who builds governance capability at Bendigo and Adelaide Bank, and Tim Moon from the DGX Group. AI still needs something to read. If datasets are poorly labelled, filled with unclear field names, or missing basic definitions, AI will still produce an output, but it may not be what you expect. Kylie also stressed that context is key. Without context, it is hard to judge whether an output is appropriate, accurate, or safe to use in decision making. This is where governance earns its place, not as red tape, but can help support more confident decision making.
“Even AI needs something to read from or something to ingest for it to work.”
– Kylie Kirkby, Head of Data Trust & Governance, Bendigo and Adelaide Bank
Minimum viable governance is increasingly becoming common
Another strong theme was the move to minimum viable governance. When delivery cycles are short and teams are running multiple change programs, governance has to be clear about what is essential versus what can be improved over time. Belinda described this as knowing what you would die on a hill over, including what you will not allow into a production environment even if you can tolerate it during a short test phase.
“Work out what is your minimum viable governance, what are the things you will die on a hill over.”
– Belinda Toniolo, Head of Data Governance, RACV
How to position governance as an enabler
Across the discussion, the advice was consistent. Rather than starting with the framework, one approach is to start with the business problem. Kylie described governance as a people business first, reflecting how governance roles are shifting away from being purely technical and increasingly requiring strong communication, influencing, and stakeholder engagement skills. Belinda emphasised meeting teams where they are, talking about productivity, outcomes, and customer impact rather than leading with governance terminology.
“I would really like my kids to put the open date on the milk.”
– Belinda Toniolo, Head of Data Governance, RACV
Data governance is becoming more central, not less. Organisations may be better positioned to move faster with confidence when they treat governance as part of how work gets done, not a separate gate at the end.
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