If you’re lucky enough to have a Data Culture team in your organisation, it’s probably separate from your Data Governance team – which makes sense, because they do quite different work. But look closer and you’ll see that they support each other’s objectives. After all, how can colleagues ‘do the right thing’ with data if guidelines are ambiguous and processes are unclear? And what use is a set of standards and policies that people don’t see the value of following?
At a recent Data Culture Club event we welcomed Data Culture members and Data Governance guests to consider both sides of the argument. We had a fantastic panel featuring James Finlason (then Data Governance Lead at the John Lewis Partnership), Nicola Askham (The Data Governance Coach) and Steve Prokopiou (Data & AI Literacy Expert), followed by roundtable discussions which surfaced some thought-provoking opinions and ideas.
Both Data Governance & Data Culture are hard to define
Our opening discussion revealed that definitions of Data Governance and Data Culture are not universal, even among experienced practitioners well-versed in best practice. Indeed, some organisations have started rebranding one or the either ‘Data Enablement’, which can further blur the lines. Our panellists did agree, though, that they serve different purposes.
James likened Data Governance to the ‘rules of the road’ – instructions and guardrails that define what is allowed – but also useful information to help people do the right thing. And despite its reputation as the ‘business prevention department’, it does help organisations operate more smoothly and efficiently. Just imagine the chaos if roads had no lane lines or traffic lights: vehicles would have to inch along in order to avoid collisions…
Data Culture is more about mindset and a collective understanding of ‘the way things are done around here’ – and in particular how people behave when no-one is prompting or policing them. It gives people the confidence and understanding they need to use data in ever more valuable and innovative ways.
So ostensibly the two disciplines focus on quite different tasks: Data Governance teams are creating policies and standards, while Data Culture teams are creating training courses and data communities. But the point is that we need both. Perhaps Data Governance is the “what”, while Data Culture is more about the “how”. The exact boundary may vary, but the two are closely connected.
Data Culture can help people see the value of Data Governance
Too often, Data Governance is seen as an additional burden that people have to be nagged to do. Our experts on the panel had long since given up trying to persuade organisations to care about Data Governance for its own sake. Instead, they found it more beneficial to show how it can help people achieve something they were already interested in – delivering their company’s ideas, strategy or objectives; or at a more human level, impressing the boss or out-performing a competitor. It’s important to understand the business context well enough to make Data Governance relevant, by asking people what they are trying to achieve and what is getting in their way.
Nobody wants to do Data Governance!
In a strong Data Culture, where data is already considered a useful tool to help make day-to-day decisions, organisations already understand the importance of data being high quality and well-managed because they need it to be trustworthy. They want to do the right thing, so they welcome clarity around procedures and ownership.
Data Governance can boost the visibility of Data Culture
Data Culture is a relatively new discipline, and can sometimes be seen as a ‘nice to have’. Unfortunately in typical transformation projects it’s the technology and processes that receive attention first, while the ‘people bit’ is postponed until later – and then becomes vulnerable when budgets tighten.
Data Governance is already established as an essential data management capability in many organisations – particularly those in heavily regulated industries like Financial Services. If the mutual benefits of Data Governance and Data Culture can be recognised, it may give Data Culture teams some protection when times are tough.
Unless everyone’s doing it, it’s not really culture, is it?
Data Governance and Data Culture face similar challenges
Finding the influencers
Both Data Governance and Data Culture teams need support from Leadership, but it’s worth looking beyond the obvious senior stakeholders. Business managers and executive assistants will often know what will resonate, as well as the pressures they are under. Formal authority matters, but informal influence can be just as useful when you are trying to change behaviour.
Working out when to let go
As one of our panellists pointed out, “the Data Governance team shouldn’t be doing Data Governance!”. Once the framework has been designed and implemented, the day-to-day running of it should transfer over to Data Owners so they become both accountable and responsible for the tasks. Otherwise it simply doesn’t scale.
Similarly, the Data Culture team can set up data academies and champion schemes and make them as relevant and engaging as possible, but they need individual colleagues to get involved. There’s only so much you can do to help before people have to do it themselves.
Adjusting to the impact of AI
The discussion noted that Data Governance has become a much more prominent subject as organisations grapple with AI Governance. At the Data Culture Club we’ve debated whether there’s such a thing as ‘AI culture’ and how the two might differ.
In both cases, the attention and enthusiasm for AI is welcome, but it creates another communication challenge: organisations can become so focused on AI that they overlook the underlying data. The conclusion was simple: good AI depends on good data, so the two conversations need to stay connected.
Proving value
Showing return on investment has always been tricky in this area, and there’s no simple set of KPIs the panellists could point to. Indeed, as activities become more proactive, some of the value lies in problems that never happen – and those are much harder to measure.
The advice was not to be shy about the successes you can demonstrate. Data teams often deliver something useful and immediately move on to the next problem. Instead, build a portfolio of simple examples, assign a monetary value on them where you can, and keep communicating them – almost like an internal marketing exercise.
Managing expectations
The inevitable question also surfaced: when will Data Governance or Data Culture work be ‘done’? The consensus was that it is an ongoing organisational capability, not a project with a neat completion date. Frameworks and approaches have to evolve as the organisation, regulation and technology change; people also need reminders and renewed engagement. One audience member captured the oddity of the question perfectly: “nobody ever asks when Corporate Governance or HR will be ‘done’!”
The practicalities
Defining the org chart
The organisational structures represented in the room were strikingly different. Some people worked in a data enablement function containing governance, quality and culture; some had culture and governance as distinct teams within a central data office; others sat in analytics, CISO or elsewhere. We concluded that it helps if you’re all working for the same boss as objectives and timelines will be aligned, but in practice it depends more on personalities and how closely you can work together day-to-day.
Drawing the line
One group described Data Culture as the “wide-angle lens”: governance provides rules and expectations, while culture is the broader environment in which people learn how things are done by watching the people around them. In that sense, culture can act as a force multiplier for governance. If expectations are reinforced socially as well as formally, new colleagues absorb them as part of ‘who we are and how we act’.
The relationship between the two can almost be a vicious cycle. Weak governance can contribute to poor data quality; poor culture can mean that governance is ignored or inconsistently applied; bad data then undermines trust and makes the cultural problem worse. The encouraging flip-side is that the cycle can work in the other direction too. Better governance can support better behaviours, while a stronger culture makes governance easier to embed.
If the culture is supportive, implementing governance can feel like pushing on an open door. But large organisations can contain many different subcultures, and what one person considers the right culture may not fit another part of the business. That led to a more nuanced question: should you always try to change the culture you find, or sometimes adapt your approach to it? The group emphasised listening to what is actually happening rather than beginning with a fixed view of what should be happening. Culture is not an abstract target state; it is the lived context in which governance has to work.
If the culture is ‘right’, governance is pushing on an open door.
Making it useful
It’s always important to speak the language of the business, align governance to business goals and make the problem shared. The phrase “my data is wrong – what are you going to do about it?” captured a mindset several people recognised. If data quality and governance are seen as the data team’s problem, ownership will always be limited. Trust matters too: trust in the data, and trust in the people asking the organisation to govern it.
Governance feels enabling when people can see that it helps them use data with greater confidence, make better decisions and move more quickly – not simply because the governance team tells them it is important.
Final takeaways
Strong governance builds clarity, ownership and trust. Strong culture drives curiosity, confidence and consistent use of data in everyday decisions. When these two elements evolve together, organisations move faster, make better decisions and create lasting impact. When they don’t, governance becomes bureaucracy and culture becomes chaos.
- Governance and culture are different, but treating them as unrelated disciplines makes both harder.
- Connect them to outcomes people already care about. Best practice is rarely a compelling reason on its own.
- Listen before prescribing. Business language, context and relationships matter as much as technical expertise.
- Make good data behaviour part of everyday work – ideally from the moment someone joins the organisation.
- Ownership has to extend beyond the data team. It cannot scale if central teams keep doing the work on everyone else’s behalf.
- Communicate successes. If it prevents problems, saves time or improves decisions, make sure people hear about it.
- AI has increased interest, but it still depends on solid data foundations.
- There is no finish line. Governance and Culture both need to evolve as the organisation changes.
Many thanks to our panellists, table moderators, members and guests for sharing their experiences so openly. This is part of what makes the Data Culture Club useful: not a room full of polished answers, but practitioners comparing what actually works, what doesn’t, and what they are still figuring out.
If you missed this one, we hope this catch-up gives you something useful to take back to your organisation. But, as ever, the best bits were the conversations in the room – so we hope to see you at a future Data Culture Club event.
Araminta Huitson
Data Culture Lead & Data Culture Club Founder
Building better data cultures, together
We’ve nurtured a community of Data Culture leaders who meet regularly, online and in-person, to share ideas, swap advice and get real about what works (and what doesn’t) when it comes to building healthy data cultures that last.
With more than 100 members representing multiple countries, and working for a range of industries, we’re able to bring together a great mix of perspectives helping shape the discipline.
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