Moat was Commended in the Digital Transformation category of the Housing Technology Awards 2026.
Many years ago, I visited Japan and something struck me almost immediately. The streets were remarkably clean yet rubbish bins were surprisingly hard to find.
Over time, I realised the cleanliness wasn’t primarily the result of infrastructure but culture. People carried their rubbish home, and the behaviour existed regardless of whether bins were present.
I’ve often thought about that when reflecting on technology and data transformation at Moat. Over the past few years, we’ve introduced many of the things organisations typically associate with ‘maturity’, such as governance forums, improved change control, modern reporting platforms, structured data ownership, self-service analytics and clearer operational processes.
All of these things matter. But one of the most important lessons from our journey is this: organisations can become very good at implementing technology without fundamentally changing their behaviour.
The moment of honesty
In 2023, we underwent several external reviews across our technology, data and operating model. The findings weren’t catastrophic nor were they unique to us. In many ways, they reflected the reality of how housing organisations evolve over time.
Our processes varied across teams, reporting relied heavily on manual effort and reconciliation, changes weren’t always reviewed consistently for cross-functional impact and data ownership existed more in principle than in practice.
The uncomfortable part wasn’t hearing these observations for the first time (because most of us already knew them) but seeing them articulated independently and objectively. That created an important decision point; we could continue to evolve incrementally or deliberately strengthen the foundations of how we operated.
We chose the latter.
Strengthening the foundations
Much of our work since then has focused less on introducing new technologies and more on creating a clearer operational discipline.
We strengthened the mechanisms through which the organisation makes decisions. Our change advisory board was developed so that cross-functional technology changes could be reviewed more consistently, with better visibility of the downstream impacts before implementation.
A data steering panel was established (with senior sponsorship) to provide a clearer forum for decisions relating to data risk, GDPR, subject access requests and governance. We also introduced a business partnering model so our operational leaders had a more structured route into shaping requirements and identifying where systems, reporting and digital processes were creating friction in day-to-day services.
Alongside this, we improved our IT service management processes so our colleagues had clearer routes to raise incidents, bugs and change requests, while ensuring these were triaged and reviewed by the right teams.
We also introduced a formal data governance framework, supported by external coaching, to clarify the responsibilities of data owners and data stewards. Defining the roles was relatively easy but embedding accountability into day-to-day operational behaviour was far harder.
A data practice community was established to support colleagues working with data, share good ideas and develop our self-service analytics capability more responsibly across Moat.
None of these changes were particularly glamorous; most were structural, operational and at times quite slow-moving but they all mattered.
Building the platform
In parallel, we modernised significant parts of our reporting and data landscape.
Power BI adoption grew from just five per cent of the organisation to around 60 per cent, helping to reduce our corporate reliance on static reporting and manual spreadsheet production.
We also deployed Microsoft Fabric as our strategic data platform and have been building out a ‘medallion’ architecture to improve structure, lineage and governance across our datasets. Over time, this will support reusable data products that align with our longer-term self-service analytics goals.
These changes have materially improved access to information and reduced friction in how data is consumed across the organisation.
But technology implementation is usually the visible part of transformation; behavioural change is slower, quieter and significantly harder.
Where this becomes real
The clearest examples are often found in operational areas where the stakes are high and the work is constantly evolving.
Damp and mould is a good example. Like many housing providers, we’ve had to strengthen our approach because expectations around resident safety, compliance and early intervention have increased.
Our response hasn’t been a single technology project; it has been a gradual strengthening of our whole operating model.
We continued to upgrade our damp and mould workflow in our CRM system so that cases could be logged, triaged, tracked and reviewed more consistently. We then developed Power BI reporting to give clearer visibility of case volumes, trends, repeat visits and emerging hotspots. More recently, we extended those foundations with an predictive AI model for earlier identification of properties with an increased risk of damp and mould.
Each layer mattered. The CRM workflow helped structure the process, Power BI helped make the work visible and the predictive model helped us to look further ahead.
But none of those things would work properly without the surrounding behaviours. Colleagues need to capture the right information at the right time. Teams need to trust and use the reporting. Surveyors and operational specialists need to apply judgement to our models’ outputs. Leaders need to use the insight to shape their priorities and follow through on decisions.
That’s where technology starts to become transformation. Not when a system is launched but when the organisation changes how it works around it.
For our residents, this matters because better data isn’t an abstract improvement. It supports earlier identification of risk, better informed conversations and more consistent follow-up in areas that directly affect people’s homes and wellbeing.
What changed and what didn’t
Today, we have stronger governance, better visibility and more mature platforms than we did a few years ago, yet some things remain stubbornly familiar.
Spreadsheets haven’t disappeared, data quality problems still originate in operational processes, data ownership still requires continual reinforcement, and access to better information doesn’t automatically change decision-making overnight. However, this isn’t failure, it’s simply the reality of organisational change.
Modern platforms can create the conditions for better decision-making but they can’t replace accountability, curiosity or operational discipline. That part is cultural and culture changes far slower than technology.
Not a re-tooling exercise
One of the risks in our sector is that transformation becomes framed primarily as a technology problem: buy the platform, deploy the dashboard, write the governance policy and implement the workflow. These are all necessary steps, but none of them, on its own, creates maturity.
Mature organisations aren’t defined by whether governance exists, they are defined by what people do when nobody is reminding them.
The same is true of data. Progress begins when operational teams challenge poor-quality information themselves, when leaders consistently use trusted data in decision-making and when governance becomes embedded into normal operational practice rather than treated as a separate exercise.
Looking ahead
We’ve made meaningful progress over the past few years. Our governance is stronger, our platforms are more modern and our data capability is significantly more mature than it was before.
There is still more to do across our systems, tooling and data landscape. But one of the strongest lessons from our journey is that the next stage depends just as much on embedded behaviours as it does on technology.
And that’s why I still think back to those streets in Japan. There are many reasons why public rubbish bins are less common there, but part of the answer is cultural. People take responsibility for their own waste and over time that behaviour becomes normalised.
Technology and data transformation can feel very similar. Governance forums, reporting platforms and operating processes are important foundations, but real progress starts to emerge when the behaviours become part of the culture.
In data terms, that means the people who create and own information think about its quality, consistency, retention and disposal as part of their everyday work, rather than seeing it as something owned elsewhere by governance or technology teams.
That takes time, and often far longer than implementing the technology itself. But it’s also the difference between transformation that’s merely introduced and transformation that genuinely persists.
Chi Fox is the head of data and business systems at Moat. The housing provider was Commended in the Digital Transformation category of the Housing Technology Awards 2026.
