In order to set your data governance initiative up for data mesh, you first need to understand traditional data governance. What does it even mean? For me, functional data governance - at an organisation of scale - is not centralised in day-to-day decision-making. The central team just can't have the context or the knowledge across all the data to make good decisions quickly, if at all.

There absolutely needs to be a central team to provide support and knowledge and set federated teams up to succeed, they have a focus on friction-reduction and value-add work. To do that, you need to create standards and processes, but you need keep your frameworks, processes, and standards as simple as possible - no one single, all-encompassing standard please!

Data quality is key

As an example of functional governance, think about universal data quality standards. Every use case may require a different combination of data quality - why optimise for completeness if it's not needed?

Data Governance should be focused on helping business stakeholders define aspects of data quality and how to measure it - that way, data consumers can understand the quality of what they consume without learning different standards for each new data source, but we aren't setting data quality requirements that aren't helpful or useful.

Data mesh for the people

Data mesh is very much about the people side. That means the data governance team needs to collaborate with people outside the central team to iterate and improve upon your data governance approach. Feedback leading to improvements is necessary, the data governance team can't issue decrees from on high.

A part of data mesh that excites me is trying to solve for the age-old challenges of ensuring the data is the right data and that we get it in front of the right people to answer questions about the business - lowering friction to leveraging our data. What "right" means is always somewhat open to interpretation of course.

Whether you are doing data mesh or not, I believe data governance can't be about obstacles. That is how data governance got a bad reputation. The phrase should spark joy, not fear or revulsion.

Instead, it must be about making it easy for data consumers to find the right data and then being able to find the right people and documentation to help them understand that data. Governance is about providing low-friction ways to provide access and drive understanding of your data and how to properly use it.

Who does what?

One of the biggest lessons I have learned working on a data mesh implementation is that while in data mesh, there are a few new responsibilities that are called out explicitly, that might fall under different roles in different domains.

Some responsibilities may fall under a data owner in one domain and under a data steward or mesh data product owner in another. The differing role types are data owner, mesh data product owner, and data steward.

Find a standard setup for roles and responsibilities and then let the domains move responsibilities around as needed - don't make the domains come up with everything from scratch but don't hold on to your standard setup closely either. Everything in data mesh is about iteration and evolution!

When will I know my data governance is ‘good enough’?

No matter what, you won't get your data governance perfect when starting. Especially with something as immature as data mesh is right now. So have clear indications but nothing set in stone. Think about what capabilities are needed early to drive value: is that some complicated interoperability standards or some data quality definitions/measurement to enable people to understand and trust the data? Probably data quality definitions.

Every data governance approach should be tailored to the organisation, but it should start from a few building blocks:

Policy: as it mandates who will be required to do what and why? Domains just don't do data governance out of the goodness of their hearts.
Processes and standards: lay out what you are trying to achieve and why and then give people an easy way to achieve that. That drives consistency and reduces friction, a win-win.
Roles and responsibilities: it's very crucial to assign ownership and layout who exactly owns what; we've all been to meetings with no clear next steps, and they are almost always a waste of time. Who owns driving things forward? Be clear about it.

My top data mesh governance advice

Look for a relatively simple first use case. What has a high chance of success where you can also get some momentum and learnings?
Don't only look for the simple use cases early in your journey. That can lead to not being able to actually face the hard parts when they come. And with data, of course the hard parts will come.
Communicate early and often that you want to collaborate with people and that things will change. Solicit feedback and make constituents part of shaping your governance.
Make it clear the central team is there to help and not control - help around compliance, help with reducing friction, etc.

A general sentiment that has worked well for me in the past is telling people outside the data governance team: ‘if you don't get more value from data governance than you put into, we'll change our data governance frameworks’.

The data governance team may feel the pressure but if you aren't adding value with your governance, outside of regulatory compliance, why are you doing it? Teams will want to participate if you give them a reason to so find the value-add reasons to.

Final thoughts

If you are looking at data mesh as only making data more accessible and usable to existing data consumers… that's a big, missed opportunity. Data mesh can make data more accessible to more employees, driving better decisions. We need data literacy to get to that target outcome but implementing data mesh will lead to a lot of wasted potential if we don't expand the data consumer pool.

And don’t forget - you can actually drive buy-in for data governance. Whilst trying to sell everyone on upping your data governance game with the same message is not likely to succeed, data governance really does have value for all participants!

If it doesn't, you need to change your governance approach. So drive that home; tailor the message and speak to your organisations pain points and how you can help them address the pain.

Don't forget if you have any questions, you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

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