It may have been the hottest day of the year last week, but that didn’t deter attendees at a packed
FinTech North in Leeds. And it seemed apt that the keynote speakers took to a stage above a glass floor above a data centre, because data was front and centre of the day. And what struck me most was where we are now with data projects compared to the 1990s.
Don’t chase the data utopia
Laura Hughes talked about how organisations get from where they are to anything useful with AI. Her point was simple but sharp. “Data-first” does not mean “perfect data platform first”.
She walked through a fictional fintech in the lending space that set out to rebuild its whole data platform before delivering anything because it found 4 key areas it wanted to transform. The approved investment got spent (millions of £s), and the only thing of real value it enabled was a chatbot. Meanwhile, the work that mattered, an affordability model built on open banking data, kept sliding behind foundations nobody could ever quite finish because of this technical utopia being aimed at.
Avoid the data trap
This is the same trap we see all the time. When the data is messy (or software projects become too complex), the instinct is to fix it all first or take on a massive rebuild. Build the catalogue, define everything, get the platform right, then start. The problem is the programme stalls long before it gets near the problem it was meant to solve or the catalyst that started the conversation.
What struck me is how familiar this felt. We saw the same thing with software in the 90s. Projects that were treated as deeply technical drifted away from the business outcome and quietly failed after they became academic exercises to pad out CVs with the latest technology. Product ownership and agile grew to fix that. Now the cycle is repeating itself with data projects, which feel just as technical. If you are watching it happen in your own org, Laura is well worth talking to.
What data-first means
The fix Laura described is refreshingly practical. Govern the data you are using. Solve for the highest-impact outcomes for the organisation first. Then build the foundations underneath as you go. Confidence in your data comes from using it on something real, not from waiting until it is pristine. Dual-running platforms as one matures and one dies can be messy, but it’s a whole lot better than missing the point or not getting a return on investment for your business.
A more inclusive fintech industry
The rest of the day backed up how much is happening in the FS Sector outside of London. There was a strong session on financial inclusion, covering the people fintech still leaves behind and the risk that “inclusion as a tick box” quietly competes specialist providers out of the market. Around seven million people are financially excluded and up to 20 million are potentially vulnerable, so this is not a small problem.
Open Banking: a mixed bag?
There was a great conversation on Open Banking with some differing opinions on what it's delivered. It didn’t live up to the hype in the run-up, but there was clear signalling that payment innovation takes a lot of time, and our banking industry is still run on platforms built for another era.
Open Finance is coming down the line, and hopefully the combination of more Open will eventually deliver on the promises of connected ecosystems for managing your finances. The vision of an AI assistant in your pocket helping you manage your finances is ever closer, though consumer trust in AI remains the real barrier for apps that touch your wallet.
A rising tide lifts all boats
Finally – I think it's amazing that at these events there is representation from fintechs, legal professionals, the regulator, product organisations and consultants such as Softwire. It brings a ton of variety to the conversation but is a hugely beneficial ecosystem.
