Welcome to the Future Ventures podcast on scaling and clarity. On today's show, we have Stan Christians, co-founder and chief data citizen of Collibra, one of the companies that helped define the modern data governance category. What started as academic research in semantic technologies in Belgium has grown into a global platform used by the world's largest enterprises to manage data trust, lineage, governance, and AI oversight. On today's episode, we explore why AI is forcing companies to rethink their data architecture, what most organizations misunderstand about governance, and why the future of enterprise AI may depend less on models and more on context. Stan, welcome to the stage.
Hi Maxim, this is Stan. It's a pleasure to talk to you today and find out more about how you're thinking about this. Data governance has always been an issue. I used to work with EY and Deloitte, and we had data governance engagements as far back as I can remember. But now with AI, it has risen in prominence.
Walk us through the origin story. Why did you decide to focus on this research, and how did it evolve into the company Collibra is today?
Well, Maxim, you're taking me back to the early years. We've been doing this for about 18 years, so we have a good view of how governance requirements evolve. In 2008, we were a spin-off from the Vrije Universiteit Brussel in Brussels. We came from a computer science lab doing research on semantics, back when the semantic web was big. We decided to take our research on applying semantics in computer systems and collaboratively managing context into the market. That's how we discovered that large organizations have data problems, and with AI, those problems are becoming significantly larger.
I completely agree. From your vantage point, what are the foundational elements that companies are missing around data that prevent them from unlocking the full value of AI?
It has to do with how organizations treat data. Many treat data as an exhaust rather than an asset. If you treat data as an asset, you organize for it with responsibilities, processes, and systems. If you treat it as exhaust, it's hard to get value. The biggest problem is that organizations are still growing up to treat data as an asset. Every 5 to 10 years, a new shiny technology like self-service BI, big data, or now AI distracts them, leading to fragmentation. That fragmentation makes treating data as an asset even harder.
Exactly. I've seen real examples. In an SAP S4 HANA implementation, the data stream was always the bottleneck. We've built data lakes and ESG platforms, and the data preparation was exorbitant. In Calgary, oil field service companies like Schlumberger became 10 times more valuable than oil and gas companies by turning data into assets. Many companies generate data but don't use it meaningfully.
Exactly, Maxim. And if you're the taxes of Canada, I hope that includes the barbecue effect. I'd love to join you for Calgary barbecues. But to your point, large companies always hit the wall of needing to take care of their data. AI will accelerate the need to solve that problem properly. Those that do will benefit more and pull away faster.
So Stan, have you been to Calgary before?
Oh, you're missing out. It's one of the most dynamic cities in North America, with 2 million people. It has the Calgary Stampede, the biggest outdoor show in the world, for 10 days in July. It's a cowboy show with chuckwagon races, barrel racing, bull riding. The city shuts down and parties. I've lived here for 20 years and love it.
Calgary Stampede?
Yeah, that sounds interesting.
It's incredible. The city shuts down for 10 days of party, concerts, and events. I love Calgary.
It sounds wonderful. I might have to free up a week in July to experience it.
Absolutely. Joking aside, you should come. To give you context, the ESG data platform we built cost about $30 million, and the overall data platform spending was $60 million. These are big dollars, but foundational. So, what are the foundational elements enterprise companies should have in place to ensure data quality, governance, and continuous quality data?
The essence is not rocket science. For any asset, you need to find it, understand it, and trust it. You need a system of record like Collibra. You need responsibility assigned, both individual and organizational, like a data office. And you need processes for access, which can be agile or more regulated. Responsibility, process, and a system of record enable people to work at scale.
Based on your work, how many companies actually have that in place, and what are the biggest misconceptions executives have about data governance?
In the enterprise market, over the last 10-15 years, all have some responsibility established. The number of chief data officers has grown from about 10 in 2012 to over 150,000 now. But the role needs to mature. Governance is often seen as slowing things down, but you're always doing some form of governance. The CDO role has evolved through versions: defensive, offensive, digital transformation, data products, startup. Version six will be about AI. The perception that the role doesn't score goals comes from its defensive origins. But with AI, data is needed inevitably. The challenge is that many don't see the connection. Data people often say 'garbage in, garbage out' and wait for AI people to come to them, or they throw up objections. That's not helpful. The best posture is constructive collaboration. CDOs can become Chief Data and AI Officers, or a Chief AI Officer might subsume data responsibility.
Kicking the ball really hard and scoring to the other side of the field, those are unique times.
