#110 Disrupting – Not Destroying – Your Data Governance to Drive Incremental Value – Interview w/ Laura Madsen

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In this episode, Scott interviewed Laura Madsen, CEO at Moxy Analytics and author of the book Disrupting Data Governance.

For the purposes of this write-up, when discussing data governance, it refers to the way many large organizations handle data governance at scale – a way that is very rigid and causes bottlenecks. We all know we can’t stereotype or group every org together but general trends can be observed.

Some key takeaways/thoughts from Laura’s point of view:

  1. A big issue with today’s data governance is that the concept of data stewards – the people who own the data concepts – is from 30 years ago and hasn’t changed much despite the demands and scope changing dramatically.
  2. The data governance committee/council structure most organizations use is inherently inflexible and ineffectual. Those making the decisions don’t really understand what’s happening with the data under the covers and those who do understand have little ability to influence the wider committee outside their own domain. And thus, they become a major bottleneck.
  3. Data governance committees can be quite useful if they focus on communication and context exchange rather than driving decisions and work forward.
  4. To drive change in your data governance practices, you need to disrupt but not destroy. Start to break down the big picture into much smaller, bite-sized chunks that when you improve on them will incrementally drive value – Agile provides a good framework to approach this.
  5. You will absolutely have to throw out a LOT of your current data governance practices – over time – as you replace them with better ways of working. You will need to really evaluate each practice and assess if it will drive value or should be replaced.
  6. “Marie Kondo” your data governance practices – really look at your processes one by one and ask “does this spark value?” Reference: https://storables.com/storage-ideas/marie-kondo-method/
  7. Current data governance practices does no provide incremental value to most organizations, they are about compliance and risk mitigation. If you can drive value creation, you can more easily drive change. People want to enable value creation or at least are hesitant to stop it in most orgs. Look for small ways to drive incremental value to build momentum.
  8. The current data steward and data ownership model essentially rewards innaction more than action. Action has risk and risk mitigation is a large part of the data steward and data owner’s role. We need to change that relationship and reward enabling valuable use of data but within compliance.
  9. Laura is a fan of the hub-and-spoke model for data governance – and in general. To make hub-and-spoke work, 1) everyone has to really work on strong communication and 2) the central governance team cannot fall into the trap of trying to fix the data themselves, they must empower and enable the teams to fix their own data.
  10. Data governance teams must stop writing policies -> compliance and InfoSec should be doing that. Policies become something that can be audited – don’t give regulators the path to fining you.
  11. It’s crucial to understand that there is a “good enough” in data governance and it’s often good enough only for right now. Find that line of good enough for now, look to reevaluate, and find places that aren’t good enough or are just barely good enough to focus on. There will assuredly be lots.
  12. It will probably be very uncomfortable disrupting the way you do data governance – at first. Start to build that muscle memory with small, incrementally valuable changes.
  13. There must be balance between flexibility and rigidity in data governance. Too much flexibility causes chaos. Too much rigidity causes the pain you are probably feeling right now.
  14. Laura wants data governance professionals to know that she understands how difficult your role is and the work you do is very valuable. She sees you 😀
  15. To start to change your data governance ways from woes to “woah, this is working”, first start by rethinking who is accountable.

Laura started the conversation with her big question when it comes to data governance: How did we end up here? How did most organizations end up with a very rigid, not scalable, non value-add data governance approach? How are we doing data governance essentially the same way as 30+ years ago? How can that make sense given all the changes and advancements in tech/software in the last 3 decades? For Laura, it really doesn’t make much sense and we should disrupt that model.

With the data steward model as it currently “works”, the steward is someone in the business that has subject matter expertise but often has a hard time driving incremental change because of politics. So we need to work on flipping the incentives and role goals to drive incremental value from governance instead of becoming a costly bottleneck with data stewards and owners preventing use instead of encouraging it.

For Laura, the biggest issue with data governance right now is the governance councils and committees. They typically have worthwhile goals, ones every organization should strive for. But committee structure almost inherently means they will be ineffectual in driving high-value work. The data owners have no real line of sight to what’s going on with the data and the data stewards – who do have that line of sight – can’t move forward without approval. And after a few meetings of nothing really getting done, the required decision makers often stop attending. So the committee holds useless meetings instead of actually pushing work forward.

Committees and councils can have a benefit in Laura’s view if they are focused on communication instead of direct action. They can be great ways to share context internally, especially among key stakeholders.

Laura is a big fan of the hub-and-spoke model of organization to drive things forward with governance. The key to leveraging a model like hub-and-spoke is strong communication touch points between the centralized hub – the data governance team – and the spokes – the domains. A common failure point with data governance teams, especially in the hub-and-spoke model, is that the governance team tries to fix the data instead of enabling the teams to handle their own data. As Jay Sen reiterated: empower people, don’t try to do their jobs. Hub-and-spoke can probably work relative to data governance in data mesh but you have to be careful about what is centralized in the hub.

Look at the roles you have that support data governance. Reconsider who does what with a simple “RACI” model of Responsible, Accountable, Consulted, and Informed. Flip the script by removing accountability for activities when the role has no ability to impact the work – i.e., making a data steward responsible for an entire data domain when they have almost zero ability to change workflows, impact data quality, or align expectations, etc.

Laura recommends for people to stop “putting their heads down”, working so they can drive something. Instead, really think about why you want to change what you’re doing – what is your reason for wanting to disrupt your data governance? If it’s just to shake things up, that’s probably not going to go well. You will need some major force of will and perseverance to really make the change. So go in it for the right reasons.

So, how do you actually start to change your data governance practices and overall approach? Per Laura, start by evaluating the ways you currently do data governance and start to look for ways to break your approaches down into smaller pieces so you can do small-scale disruption and deliver incremental value. Delivering in small increments will make it quicker and easier to deliver value while lowering you chance of failure. As you start to show you are adding value, you will gain momentum as most organizations do not really drive value from their data governance. People are typically pretty happy to enable value creation.

Per Laura, you should really rethink the way you do all aspects of data governance. Nothing is sacred. Spend the time to really consider all aspects of your data governance and think if you should change it. And Laura even recommends to look to commit to tossing aside practices before you evaluate if they work. That way, you are having to pick things from the garbage pile rather than sticking with the status quo. It’s a bit of a psychology but could be useful.

Scott asked what typically causes organizations to really rethink their data governance. At least for Laura, she typically gets a call when the data governance leader leaves and the CDO or CIO need some help cleaning up the issues. And a data governance leader is typically a short-term role, per Laura. That leader typically drove so much through their own knowledge of “where the data bodies are buried” rather than through really scalable process so it can cause a major disruption when they leave.

When the opportunity and drive to change your organization’s data governance does arrive, Laura recommends rethinking data governance at the highest level. What are you really trying to accomplish? How do you get to “good enough”? How do you get comfortable with “good enough”? And it’s crucial to understand that good enough for now may not be good enough for the future and build in a plan to reevaluate processes. But that bridge solution is still viable and valuable. It can be quite challenging to change the way people have approached data governance for the last 30 years. Break it up into small changes and get moving, build the muscle memory of change.

For Laura, data governance is often a proxy or a reflection of your broader data culture. It’s important to seek balance in your data governance approach between flexibility and rigidity, much like your data culture. Too much flexibility will create too much chaos to move things forward – there is too little communication and/or coordination. Too much rigidity is essentially the world we are in today for most organizations and we can probably agree that’s not great.

Laura wrapped up on a few points. 1) Data governance is crucial to driving trust in data. Work with people to really communicate what is happening in your governance approach to increase that trust. 2) It’s very easy to try to tackle everything in data governance but focus on what matters. It’s okay to have some sharp edges, what will drive more value? And 3) Data governance work is incredibly hard. Laura wants you to know she gets it and you data governance folks are seen. You do incredibly difficult and valuable work.

Laura’s book, Disrupting Data Governance: https://smile.amazon.com/Disrupting-Data-Governance-Call-Action/dp/1634626532

Laura’s LinkedIn: https://www.linkedin.com/in/lauramadsen/

Moxy Analytics website: https://www.moxyanalytics.com/

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