Reimagining Data Functions in the AI Era: Fixing Ownership, Governance, and Trust

Over the past decade, organisations have invested heavily in data. Platforms have been modernised, architectures redesigned, and new tools introduced at pace. Yet, for many leadership teams, a persistent gap remains. The investment has not translated into consistent, measurable business outcomes.

In this episode of Enterprise Tech Talk, Saumitra Kalikar spoke with Surit Sethi about this exact challenge. What stood out in the discussion was not a lack of capability, but a lack of alignment in how organisations think about data.

Data is often described as a strategic asset. That is true, but it is only half the story. Increasingly, data is also a liability. Rising regulatory expectations, customer awareness around privacy, and the growing impact of cyber incidents mean that poorly managed data can quickly become a source of risk. The organisations that fail to recognise this duality often struggle to move beyond fragmented outcomes.

One of the most practical insights from the conversation was around ownership. Many organisations still talk about data ownership, but very few operationalise it effectively. When ownership sits outside the business, governance becomes an afterthought. When ownership sits within the business, supported by strong enterprise frameworks, behaviour starts to change. Decisions become more balanced, considering not just growth, but also risk and responsibility.

The definition of a modern data function also needs to shift. It is no longer about how current the technology stack is. It is about how resilient, trusted, and well-managed the function is. That includes clear ownership, embedded governance, strong risk controls, and an organisational culture that understands the consequences of how data is used.

Operating models are evolving in a similar direction. Traditional centralised structures are giving way to more outcome-focused, end-to-end teams. These teams are accountable for delivering business outcomes rather than just technical outputs. At the same time, some elements must remain central. Data strategy, governance frameworks, and control structures cannot be decentralised without introducing fragmentation and risk. The balance between autonomy and consistency is becoming a defining capability for leadership teams.

Funding models are also changing. The shift away from project-based funding towards sustained investment in teams and capabilities is helping organisations focus on outcomes rather than internal cost allocation. It is a subtle shift, but an important one. It changes the conversation from “who pays for this” to “what outcome matters most”.

Governance is another area where the mindset is evolving. Treating governance as a separate function is no longer sufficient. It needs to be embedded into how teams operate. Much like quality in modern software delivery, governance must be built into the process, not applied at the end. This is the only way organisations can meet the expectations of regulators and customers alike.

Perhaps the most important challenge, however, lies outside the data team. The biggest gap today is not in data engineering or architecture. It is in data literacy across the organisation. Unless business leaders understand their role in managing data, ownership will remain theoretical. Building this awareness requires deliberate effort and sustained leadership attention.

The discussion naturally extended into AI, where many organisations are currently focused. There is strong pressure to deliver AI outcomes quickly, but the reality is more nuanced. Moving too fast without the right data foundations introduces risk. Moving too slowly means missing opportunity. The right approach lies somewhere in between, grounded in a clear understanding of data readiness and organisational risk appetite.

The takeaway is straightforward, but not easy to execute. Building a modern data function is not a technology exercise. It is an organisational transformation. It requires clarity of ownership, disciplined governance, continuous investment, and a culture that treats data as everyone’s responsibility.