2#11 - Karin Håkansson - Data Governance in a Mesh (Eng)

MetaDAMA - Data Management in the Nordics

«Data Mesh promises so much, so of course everyone is talking about it.»

I had the pleasure to chat with Karin Håkansson about Data Governance in a Mesh. Karin has worked with Data Governance and Data Mesh and is really active in the Data Mesh Community, speaking at podcasts and moderating the LinkedIn Group «Data Governance - Data Mesh»

Here are my key takeaways from our conversation:

Data in Retail

• The culture in retail is about innovation, experimentation, new products. So governance has to adapt to this environment in order to be successful.
• If retail would do, what we do in data a fashion retailer would sell yarn instead of t-shirts.
• Retail knows what the customer wants, before the customer wants it. What would happen if we in data think like retailers?
• Its more about understanding the business better, then making the business data literate.

Data Governance

• Data Governance best practices in the DMBOK is still relevant, also in a Data Mesh setting.
• Data Governance has been on a journey from compliance driven to business value driven.
• Centralized Data governance creates a bottleneck. Decentralized Governance creates silos. So federated Data Governance is the middle ground.
• Create incentives to create trust.
• If you utilize your platform correctly, you can have high expectations towards computational governance.

Data Mesh

• Data Mesh comes with a cost - you need to invest in Data Mesh.
• But more than anything Data Mesh implementation is an enormous change effort.
• If you don’t know why Data Mesh, you will implement something else
• Implement Data Mesh in an agile way: «start small, fail fast and iterate»
• To start with Data Mesh, work with a business team that is eager to get started and sees the benefits - «You have to have business onboard, otherwise its not going to work.»
• Always check if you get the value that you expected.
• When you do it, make sure you get Governance, business and tech teams to work together and a re aligned on the why.
• Make sure two upskill for Data Mesh - its is fundamentally different: talk about it, have debates, book clubs, ++
• The 4 elements of data mesh: Can you implement those in a sequence or should you look at them as a unite to implement within a limited scope?
• Start finding ways for people to work together (eg. Common goal, and environment where it is ok to share).
• A good first step is to find an example of data with a certain issue or limitation and talk with the business user about exactly this.
• Data Governance, as much as Data Mesh, is about change management: You need to get close to the business and collaborate actively.
• Your first two steps should be:

1. Start with one business unite, an early adopter.
2. Find their most critical data and talk about actual data.

MDM and Data Mesh

• Are we still hunting for that golden record? How do we work with MDM in a mesh? This is not solved yet.
• You can refer to data, instead of collecting data in a MDM system.
• Maybe the best approach so far are global IDs to track data cross domains, but how you link your data might become the new MDM.
• «You still need to connect the data, but you don’t need to collect the data.» - MDM in a Mesh.

Domains, federation and responsibility

• If you federate responsibility to the domains, they also need the resources and competency to fulfill these responsibilities.
• If the domain data teams are successful in abstracting the complexity, it will become easy to create data products.
• If you scale too fast (faster than your data platform), you might end up having to duplicate teams.