Earlier this year, I talked to Gilad Lotan, BuzzFeed’s VP about data governance and its place at BuzzFeed.
“The governance piece — and setting up transforming an organisation’s data operation — is definitely an area I think about a lot, and I know many folks are struggling,” he told me. “It’s not as flashy as talking about our latest and greatest machine learning algorithm, but in a way it’s much more important.”
Gilad shared some of his organisation’s recent technical and organisation rehaul with INMA’s spring Smart Data master class series — and his remark highlights the layered connection between data governance and the more practical matterns of how our data stack works.
As we rethink or expand our capabilities, we also reassess (or entirely discover) new problems that tie with our soon-to-be-expanded capabilities. More data flows. More data points. More data-powered systems. To rearrange a quote from the philosopher Biggie Smalls: “Mo Data Mo Problem.”
So perhaps this problem is on your mind as a growing concern as your data organisation matures and things only seem to get more complex. And to be sure, if you are realising you are having growing compliance problems, these can be quite symptomatic of governance issues.
Classically, a place where this can be manifested is when your organisation is getting Right to Be Forgotten requests from EU data subjects (or California data subjects, where the law is very similar). But it seems the same complaining individuals have to make repeated requests (for example) for e-mails to stop coming to them. This means there are broken data flows that have split user data across systems that don’t talk to each other or that your team doesn’t have a clear vision for the various paths of the data — so these Right to Be Forgotten requests aren’t executed fully when they come through.
Not only may you be getting a warning from a country’s data regulator (and don’t let that happen too often lest it becomes a full-on investigation and later a fine), but you are also expanding valuable amounts of your internal resources chasing data across several broken up tasks when these requests come in.
A reorg or a big engineering rehaul would make an interesting juncture point to rethink your data governance and frontload it: As you re-org or rebuild large data systems, you’re essentially auditing your own data organisation. You’re laying out a clean future picture of your data flows and the various systems that each contribute but also use data. You may just be at a time where you have your clearest picture of your organisation and how what it does touches everything else.
Karine Serfaty, chief data officer of The Economist (and one of the Smart Data Initiative’s advisory board members), also used the occasion of a re-org and technical upgrade to examine where the data governance functions would exist. At her organisation, data governance is blended in with other daily concerns like data architecture. It’s a close-to-base function, deeply built into operations.
This is a model that, based on my own sampling of data organisation leaders (definitely not an exhaustive or even representative list, to be sure), appears to be more common — the governance function being deeply woven with operations.
But the other way in which governance will be impeded or enabled is also at what time governance is brought in as you build out your data organisation and the data itself. There, there seems to be an agreement that you really ideally go with governance first as much as you are able to.
“I had the pleasure here to start from scratch,” said John Souleles, the chief data officer at Torstar in Canada and also an advisory board member of the Smart Data Initiative. “And I knew from the beginning when you’re building out the data warehouse … a lot of people think of data governance [is] at the end. We put it in at the beginning. In other companies, where you have legacy processes, usually governance is done at the end, because it’s a mishmash of legacy databases that you’ve got to try to figure out. But if you’re starting from scratch, it was easier to do, and we put governance and security at the beginning.”
As John noted, it may be “easier” but it’s certainly not “easy’”— not even because data systems are complex, but rather because organisations are complex and ultimately governance is about organising humans around rules they may or may not be interested to follow.
Still, what John notes here is that there is something more organic to thinking of governance earlier rather than trying to retro-engineer it on existing frameworks. And as your organisation reorganises teams or systems, frontloading governance into these projects can be a viable way to attack the governance beast, piece by piece.
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