Evinact Partner Michelle Teis explains why Chief Data Officers need to rethink how data is valued, governed and operationalised in 2026.
The pressure on Chief Data Officers is building, and it’s coming from several directions at once.
AI is changing what organisations expect from data. Privacy reform is changing what they are accountable for. And many teams are still working with systems, governance and operating models built for a very different environment.
That combination is starting to expose weaknesses that many organisations have been able to tolerate, defer or work around for years.
What once felt manageable is beginning to affect execution, risk and competitiveness much more directly. There are three areas where that pressure is becoming most visible, and where Chief Data Officers need to act now.
Data isn’t being treated as an asset
Most organisations talk about data as an asset, but very few manage it that way.
As AI becomes more embedded in decision-making, the quality, availability and usability of data will have a growing influence on the outcomes organisations can achieve. But despite all the rhetoric, data is still rarely treated with the same discipline applied to other high-value assets.
In most cases, the focus remains on the cost of collecting and storing data, rather than the value it creates. There is often no clear way to assess what data is worth, how it contributes to outcomes, or how that value changes over time.
If data were genuinely being treated as an asset, we would expect to see more mature and consistent approaches to valuation. We aren’t there yet. Formal frameworks are only just beginning to emerge, and even then, they remain early and uneven.
You can see this clearly in the public sector, where significant investment goes into collecting and managing data across all levels of government. In many cases, that effort is still duplicated. The same information is collected, stored and processed multiple times in different places, often without a clear view of how it could be shared or reused more effectively.
The opportunity is straightforward enough. Understand what’s common, collect once, and use it many times.
Organisations that can clearly measure and communicate the value their data creates will be in a much stronger position to move quickly, make better decisions and realise more value from AI. Right now, most are still some way from that.
Privacy reform is starting to bite
Australia’s data privacy legislation has been under reform for the past few years, but 2026 is expected to be a turning point as key changes begin to take effect.
One of the most significant developments is the introduction of a statutory tort. For the first time in Australia, individuals will be able to take legal action if they believe their personal information has been misused. That marks a major shift for organisations and introduces a new level of direct legal liability, bringing privacy more firmly into the legal domain.
Another major change is due to take effect from December 2026. Any organisation using automated decision-making that affects an individual will need to disclose that in its privacy policy. In practice, that means organisations will need to explain what they are doing, how those decisions are being made, and what data is being used.
Many organisations haven’t done the work required to prepare for that level of transparency.
This has practical implications across a range of use cases. A recruiter using AI or automated tools to assess a pool of résumés, for example, may need to declare that process if it affects an individual. The same applies to financial institutions using AI to help inform decisions about eligibility for funding.
There’s a lot on the horizon, and many organisations have not fully considered what these changes will require.
The current operating model won’t hold
Most data teams are still built for yesterday’s job.
The way organisations have traditionally structured their data functions made sense when the focus was on collection, storage and reporting. But that model is under pressure.
Competitive advantage is increasingly going to organisations that can turn data into evidence, and evidence into action. That’s the work we do every day at Evinact, helping organisations use data to support decisions, shape operations and respond in real time.
That changes what organisations need from their data functions.
Many teams are still built around traditional roles like data engineering and BI. Those capabilities still matter, but they are no longer enough on their own. There is growing demand for people who can work effectively with AI systems, assess outputs critically, and design workflows that embed automated or AI-assisted decision-making into everyday operations.
Those are different capabilities, and in many organisations, they are still emerging rather than established.
At the same time, there is often a widening gap between what technology now makes possible and what the organisation’s systems, processes and platforms are actually set up to support. In many cases, the underlying environment has not kept pace with how data is being used or what the business now expects from it.
That becomes a strategic issue very quickly. It affects how fast an organisation can respond, how confidently it can act on insight, and how well it can keep up in a more competitive environment.
For some organisations, that will mean confronting the reality that their current team structure and operating model are no longer fit for purpose.
There is no single model to move towards. The right approach will depend on how quickly AI is being adopted, where value is being created, and how the organisation’s competitive environment is changing.
What is consistent is the need to evolve capabilities, ways of working and the infrastructure that supports them.
What happens next
None of these issues are entirely new. What’s changing is how quickly they are becoming impossible to ignore.
There’s still a tendency to treat them as future problems. Something to prepare for later, rather than respond to now. That window is narrowing.
Those that act early to better understand the value of their data, strengthen how it is governed, and rethink how their teams and systems operate will be in a much stronger position than those that delay.
This is increasingly where we are working with organisations at Evinact. We’re helping teams make these shifts practical by building a clearer view of data value, improving how personal information is managed and used, and reshaping operating models so organisations can respond with more confidence.
That has real implications, because the question is no longer whether these changes are coming. It’s whether organisations are ready to act while there’s still time to do it well.
