Many organisations still see data governance as a data team's responsibility. What is the biggest mindset shift required to make business leaders truly accountable for data outcomes?
I think across industries, the biggest mindset shift is this: data isn't owned by the people who manage it. It's owned by the people whose decisions depend on it. Too often, data governance gets positioned as something the technology or platform teams own. The reality is that data matters because it influences a decision, an outcome. Whether that's a customer decision, an investment decision, or a risk decision, someone in the business is accountable for the outcome.
A fundamental question I always ask is: who owns the consequences if the data and therefore an insight or a decision is wrong?
I love that question because it changes the discussion almost instantly. People stop talking about systems and start talking about customers, outcomes and decisions. That's where the conversation becomes much more meaningful.
To me, the real shift is moving from management of data to ownership of the decisions that data supports. But the real opportunity is creating a culture and ways of working where trusted data gives leaders the confidence to make better decisions every day.
When you're trying to gain executive buy-in, what business outcomes resonate most strongly today - cost reduction, risk management, customer experience, AI readiness, or something else?
Different organisations will naturally prioritise different outcomes, but the strongest conversations start with a problem worth solving. Leaders care about growth, efficiency, customer outcomes and managing risk. AI can help with all of those things, but technology on its own is rarely the starting point. Too often organisations start with shiny new technology and then go looking for a use case. I believe the most successful organisations will start with the outcome, identify the decision they want to improve, and only then determine whether AI is the right solution.
What I find exciting is that we're moving beyond the hype cycle. The conversation has shifted from what's possible to what's valuable, and that's a much more interesting problem to solve.
What we're seeing across the market is that the organisations making the most progress aren't necessarily the ones running the most pilots. They're the ones thinking about and pivoting investments to put data foundations in place to move beyond experimentation and into everyday business use. I think we're past the point of debating whether AI has potential. Most leaders already see the opportunity. What we see now are much more practical and real questions: Can we trust it? Can we scale it? Can we use it responsibly?
In many ways, AI readiness has become the modern business case for data governance led by outcome accountability.
As organisations race to implement AI, what data challenges are becoming impossible to ignore, and how does strong data ownership help address them?
One thing that's become really obvious over the last year is that AI has very little tolerance for poor data. Across industries, many organisations are discovering that AI shines a light on issues they've been carrying for years, whether that's inconsistent definitions, patchy ownership or varying levels of data quality.
What leaders are looking for is confidence. They want to understand where information came from, whether it can be relied on, who is responsible for it and most importantly I think, they are looking for do my data experts stand behind this data.
This is where I believe Data Stewardship needs to evolve. Historically, stewardship has often been viewed as a data management function focused on documentation, definitions, and quality monitoring. Those capabilities remain important, but they are no longer the end goal.
I think the future of data stewardship is about connecting data to the business decisions, risks, customer outcomes and AI use cases it influences. Responsible AI cannot exist without responsible data.
Looking back on your journey, what is one assumption about data governance that turned out to be wrong, and what did you learn from it?
One assumption I made earlier in my career was that if we could build the right governance frameworks, people would naturally adopt them. What I have come to realise is alongside a good framework organisational behavior changes, accountability and relevance to the business matters equally. People rarely change behaviour because a framework exists. They change when they understand why something matters and how it helps them do their job better.
Another key assumption I made which I was wrong about and has evolved significantly is the relationship between AI and Data Governance. I think as organisations scale AI, the data and AI landscapes must coexist as part of the same accountability ecosystem. AI is only as trustworthy as the data, definitions, context and controls that underpin it.
As governance scales across the organisation, how do you balance consistency and control with the need for teams to move quickly and innovate?
I don't think the answer is more approvals, more committees or more governance forums.
In fact, that often slows things down.
The organisations doing this well are clear about what needs to be standardised and what can be managed closer to the business. You need common guardrails, shared definitions and clear expectations. But you also need teams to have enough autonomy to move quickly.
The thing I worry about most right now isn't structured data. It's everything else.
Some of the most valuable information in a business sits in documents, conversations and reports. If AI is increasingly using that information, we need to think much harder about how it's managed and understood. When I speak with peers across the industry, management of unstructured data is a challenge that's coming up increasingly. I suspect it will become one of the biggest governance conversations over the next few years.
Three years from now, what do you think will distinguish organisations that successfully turn data into business outcomes from those that continue to struggle?
Three years from now, I don't think the front-runners will be decided by technology.
What will separate them is whether people trust the information they're using, whether they understand how decisions are being made, and whether accountability is clear when something goes wrong.
The organisations that succeed will stop treating data as something managed by specialists and start treating it as part of how the business operates. They will have clear business ownership, product-oriented data assets, strong semantic foundations and governance embedded in day-to-day decision-making.
That's what excites me most about this space. AI will continue to evolve, but the organisations that thrive will be the ones that connect data, technology and business decision-making in a meaningful way.
At the end of the day, it has never really been about the technology. It's about helping people solve problems and make better decisions.
Asmita Deshpande will be speaking at CDAO Melbourne 2026, where she will share her perspectives on data accountability, AI readiness and trusted data foundations. These themes will also be explored at CDAIO New Zealand 2026, CDAIO Sydney 2027 and CDAIO Brisbane 2027.