Common Misconceptions About Data Governance

<rant> During an interview with a #business leader from a large company, we discussed his view on data and its governance. He saw it as more of an impediment than an enabler. He had a rich agenda of use cases and processes he wanted to enhance, but data was consistently a bottleneck. He was uncertain about which data to obtain and from where, and existing processes suffered from unclear data sources and responsibilities for addressing gaps. The following day, I attended a session for another project in the same company. We needed data from several systems for a marketing analytics use case, including the CRM and ERP systems, both undergoing transformations. The consensus was that the best solution would be to manage the upstream data as an asset, appoint an owner, stabilize it, ensure minimally required quality control, and catalog it as part of the future state. However, the same leader interrupted, saying, "I don't have time for this. We need to resolve this quickly. Just use the #data we already have and make local transformations." I’ve seen the exact same thing happen so many times that my response to it sometimes now is just a shrug. And this does not just occur in client organizations – I’ve seen it just as often in the consulting organizations. So many times, when I discuss the importance of minimally required data governance considerations, I see frowns appear across the room or the video conference call. “We have to get the business what they want,” the credo runs. My frustration is twofold. Firstly, there's the hypocrisy. The same people who complain about historic data issues often refuse to prevent future ones when they have the chance. Because it is exactly when new systems and solutions are being created that the cost to implement governance and quality controls is the lowest and when you have the best opportunity to drive good data management. Secondly, there's the misconception that intelligent #datagovernance is expensive and time-consuming. It doesn't have to be. Ensuring correct data modeling, auto-discoverable data flows, and capturing requirements (that already exist in BRD documents anyway) in data governance catalogs and dictionaries are relatively low-cost. However, it does require commitment and a thoughtful approach. </rant>

One data governance myth that I think needs to be busted is that it’s a business driven function. While most data related decisions are business driven, almost all of them need to be facilitated by technical teams. Business people know what they’re talking about when they say “installed base, customer, contract, order, and price” It’s only when these concepts need to be technically instantiated when rigor needs to be applied. Technical teams need to guide business teams to the decision points for business people to make those decisions. Additionally, I would argue that data engineers should be responsible for capturing semantic definitions of data that they ingest, even if it is on business stewards to review and correct it. Most data teams that I have worked with gain a strong understanding of their data domain and there’s no reason that they should be excluded from the semantic process. So is data governance a business problem or a technical problem? It’s both, and it’s totally acceptable for technical teams to lead data governance so long as they don’t take it over and exclude business teams from the process.

Most companies think data governance is a tech issue. But 86% are failing because it's actually a leadership issue: Companies keep throwing fancy tools and expensive software at their data governance problems. But the numbers tell a different story • 44% have zero formal data governance • Only 13% achieved high data maturity • 86% fail to meet basic data objectives Why? Because they treat it as a tech problem when it's really a leadership challenge. Think about it: You can buy the most expensive security system for your house. But if nobody knows who has the keys, when to lock up, or how to use the alarm — what's the point? Same with data. The real problem isn't technology. It's: • Unclear ownership • No accountability • Missing leadership support • Zero culture of data responsibility Want to know the wildest part? Companies waste 20-40% of their IT budgets fixing poor data governance. That's millions thrown away because leaders won't step up and own the problem. Here's what actually works: 1. Make data a C-suite priority ↳ Not an "IT thing". 2. Define clear ownership ↳ If everyone’s responsible, no one is. 3. Create data standards ↳ Simple rules everyone follows. 4. Build a data-conscious culture ↳ Train people, reward good practices. 5. Measure what matters ↳ Track business impact, not just technical metrics. The companies crushing it with data governance all started by changing how their leaders think about data. Everything else followed. If you’ve seen this happen in your company, drop me a DM. I’d love to hear your story.