Data Mesh Radio #142 How to Leverage an Empathetic Ear to Add Value Through Data Governance - Interview w/ Karin Håkansson
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Karin's LinkedIn: https://www.linkedin.com/in/karin-hakansson/
In this episode, Scott interviewed Karin Håkansson, Data Governance Lead consulting on a large data mesh implementation. To be clear, Karin was only representing her own views on the episode.
If there is one theme that resonated throughout the conversation, it was have more and deeper conversations with your data governance teams. Don't make assumptions and as a data governance team, don't make decrees - explain what you are trying to accomplish and why. And look to help where possible instead of telling people how to do things.
Some key takeaways/thoughts from Karin's point of view:
- Data governance teams need to get closer to both data producers and consumers; but when doing that, emphasize it isn't about control or oversight. You are there to help.
- Data producers and consumers need to share with the governance team about their pains and challenges - the governance team in most orgs is there to help. So get specific about where you need help. Emphasis on specific!
- To get closer to data producers and/or consumers, identify a few obvious data problem areas and just reach out to those involved/impacted. Focus on asking not telling when digging into the pain points. And involve all stakeholders in assessing potential solutions to get them bought in to the eventual solution.
- A control-based data governance approach doesn't add value to your data. We need new ways of working in data governance focused on adding value, not adding roadblocks.
- Data mesh creates an environment where we need to look at things differently. And it provides the excuse to look at them differently too. Challenge your base assumptions.
- If your organization isn't open to finding and implementing new ways of working, it will be challenging - at best - to implement data mesh. Really ask if you are ready for the change before implementing it.
- Data governance teams need to focus on finding long-term solutions and balancing those with temporary fixes. Be extremely clear about what is a temporary fix and why it's temporary and must be replaced.
- Always keep people informed as to progress - even if it is "no progress". Humans aren't good with uncertainty.
- Data governance teams can easily be overwhelmed if you don't develop good ways to prioritize. Find the most important - not necessarily the most obvious - pain points and assess the value of fixing them.
Karin started the conversation sharing her background - before being "pulled" into the world of data governance - as a software engineer, data engineer, and more. She's always worked with data but only recently started focusing on data governance. Her prior background has given her the perspective needed to understand problems from the perspective of both data producers and consumers and how to best help them as the data governance team.
So, with that perspective, Karin is focused on finding new ways to get closer to both the data producers and consumers - but focusing on how that closer relationship serves as a helping hand, not in an oversight or control standpoint. And now seeing things from the data governance team's perspective, she and her team are there to be helpful and add value.
When asked how she recommends getting closer to data producers and consumers, Karin said finding a somewhat obvious data challenge - where are people complaining loudly? - and initiate a conversation as someone who might be able to help. And when you do get to speak with the stakeholders, focus on listening - ask, don't tell. Really dig into the pain points instead of trying to jump to a solution as quickly as possible. Usually the pain points are pretty well known. Start from a blameless perspective - choices were made and we need to fix the situation, not assign blame. And by involving the business people in the conversation - especially in assessing the solutions - they are more bought in to the outcome because they helped pick it.
In data mesh, we have to do data governance a bit differently according to Karin. No longer the ever watchful control tower - or Sauron's Eye -, we need to find ways to make data governance agile and flexible - Laura Madsen focused on this as well in her episode. A data governance approach based on control won't actually add value to your data - and why have governance if it isn't adding value? Historically, a lot of data governance teams have focused on creating a standard way of working instead of standard outcomes. Governance people need to involve stakeholders in determining ideal outcomes, not have a checklist-type approach. Data producers and consumers running into issues should be able to go to the governance team for help; and they should ask what the governance team is trying to achieve and they might find better ways of working together.
For Karin, data mesh creates an environment where we need to - and have an excuse to - look at things differently. We must challenge our base assumptions and, where necessary, look for new ways of working. But we also have lots of existing approaches that will work as well. We need to evaluate and be open to change when it is useful. Before embarking on data mesh or evaluating a major change in ways of working, you should ask if you are really ready for that change.
While the implementation Karin is working on hasn't deployed a considerable number of data products, what she's seen so far relative to data product ownership is promising. When they are really giving people full trust in owning their data, she believes they will step up. But you need to make sure they understand the new responsibilities, the new potential roles, and the value the data products bring to the organization. And drive more buy-in by helping them find additional value in understanding their internal data consumers - it might lead to additional insights for their own domains.
In wrapping up, Karin shared her thoughts on implementing temporary fixes - it's very easy for a data governance team to run around trying to do temporary fixes but it won't scale. Temporary fixes can have value but you must look for longer-term solutions to replace them. And be explicit with stakeholders about what is and what isn't temporary. As you build towards long-term solutions, always keep your stakeholders up-to-date on progress, even if that is "no progress". Humans don't do well with uncertainty. Data governance teams can become easily overwhelmed - you can't do it all. You must find a good way to prioritize - find the most important pain points and assess the value of fixing them. And really consider value, not just how much pain it seems to cause.
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