Prioritization and Engagement in Data Governance

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Sue's LinkedIn: https://www.linkedin.com/in/suegeuens/

In this episode, Scott interviewed Sue Geuens, Director of Data Governance and Product Data at Elsevier. To be clear, she was only representing her own views on the episode.

We use the phrase MDM to mean master data management throughout the episode.

Some key takeaways/thoughts from Sue's point of view:

1. At the end of the day, if you want to do data governance well, it's about the people. Go talk to them, find out their specific needs and desires and work to tailor your language - and presumably your application of policies when possible - to their situations. People want good data, help them get there!
2. Relatedly, get good at telling stories about data work. Get people to lean in and get them involved. Personalize your communication!
3. While policies and standards are crucial, they are about creating better data for the organization. Try to leverage them as a carrot instead of a stick.
4. ?Controversial?: Don't talk about someone owning data. That's scary for most. Find ways to get them excited about owning the data without making it scary by using different phrasing.
5. The key to doing data governance well is getting people to care. We need them to care about the data because others have to use it. And that means the people are the most important focus.
6. Data governance is too focused on 'governance' and that means oversight. The word governance has a bad connotation for a reason - it makes many potential allies uncomfortable. So governance folks have to really work to make it less scary.
7. Don't focus so much on the data aspects of data work when talking with stakeholders. It's about achieving outcomes through data, not data work itself. Focus on what gets your business partners excited and that's (unfortunately) usually not the data itself.
8. It's easy to fall into thinking about what you want from others in governance. But where you can add far more value is starting with what others want from you and working back towards solutions that accomplish both your and their goals.
9. ?Controversial?: Prioritization in data governance work is crucial. A good method is looking for who shouts loudest for help. They are ready to lean in and are more likely to be leveraged as your governance advocates once you help them.
10. The two big reasons MDM initiatives have failed historically are 1) not having the governance, quality, and metadata embedded into the data and 2) striving for "perfect" data, that single golden record.
11. Relatedly, in data we don't need perfection, the juice isn't worth the squeeze. We need good enough and we need to reflect the realities that our world is ever changing and there are multiple perspectives on the same data that can all be right/correct.
12. It's easy to lose people if you start talking the 1s and 0s of data. Focus on finding stories that resonate with them. If they see the value of the work, you have a much better chance of getting them to actually do the work 😅
13. MDM in data mesh should be all about "ensuring that you get the right data for the right purpose at the right time for the right person."

Sue started the conversation as other data governance experts have - the word governance strikes at least discomfort if not fear into the hearts of many of our colleagues. We need to expect that discomfort and be active in dispelling the myths around data governance as it really is about achieving better outcomes for all. But that means more carrots than sticks, which can be a tall task when it comes to things like regulatory compliance. Basically, it's not easy 😅

Another aspect Sue pointed to is that many - most? - data people really like to talk data. So, instead of talking to outcomes, they talk about the data work, and data work for the sake of data work has kind of been one of the big historical challenges of data - instead we need to focus on the value that comes from the data work. If your business partners are already uncomfortable simply by the phrase data governance, not leaning into their value from the data work and target outcomes is likely to lose them even further. Start the conversation with what they might want from you, not what you might want from them.

Sue specifically said she starts partnering with people by focusing on those target outcomes and how might she be helpful to them. Especially, what are their expectations of her? By trying to walk in their shoes, she can come to better conclusions and find working solutions. It's about getting them to lean in. Scott note: and then she can trap them! In a virtuous bi-directional value trap of course…

Relatedly, prioritization in data governance is key in Sue's view. What are the problems that really matter? While the "who shouts loudest" test may not point to the most valuable problems, it often points to the problems people value most and thus you can find willing partners. Trying to enforce others to care about their data is a hard road but if people are ready for your help, you can make a huge difference and they are willing to lean in. Those are also likely to be your biggest advocates once you help them, gaining your governance efforts more momentum by leveraging champions.

There are many reasons why Sue believes people are skeptical of master data management (MDM). Historically, there were two big reasons MDM projects failed. The first is not really focusing on integrating MDM into the data so not having the governance, quality, and metadata embedded into the data and processes. The second is the drive towards perfection. Instead of focusing on what was good enough, there was this focus on the 'golden record'. That led to inflexibility, poor scaling, high costs, etc. Good data work isn't about being perfect, it's about being good enough.

Sue circled back to her focus on working with people. Good governance isn't about perfect data, it's about getting people to care about the quality of the data. That means working to get them to understand what is good enough and why should they care. It's not all just empathy - there needs to be some oversight and making it part of their job - but with humans in the loop, your data quality will be much better if you get people to care about who else uses their data and why.

When it comes to actually getting people to understand data governance work - whether MDM or anything else - Sue recommends personalizing your communication. While that may not scale perfectly, again, find your key stakeholders and partners. Stories about data work in a vacuum just don't resonate - Scott note: is there a physics/sound joke in there as there is no air for sound to resonate in a vacuum…? 😅 - Getting people to understand that the work has a purpose and it really is useful to specifically them is crucial. Don't talk to the 1s and 0s of data!

When it comes to specifically data ownership, Sue has seen just how scary that ownership word can be. It's not an easy task but we need to find ways to instill people with the excitement around ownership without the fear. Again, easier said than done but it's about getting things to the right place not about doing something right now. It will take time but it's better to do it right.

If you don’t take care with implementing data mesh well, Sue believes it will be a far bigger mess than if you didn't try data mesh at all. (Scott note: strong agree) You need to focus again back to what are you trying to accomplish and what data needs to be put in place to do that. MDM in data mesh should be about "ensuring that you get the right data for the right purpose at the right time for the right person."

In wrapping up, Sue emphasized the need for personalizing your communication around getting people to do data work with care and prioritize it. You need to be able to speak to them in their language and get them excited about the impact of the work.

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