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In this episode, Scott interviewed Karolina Henzel, Data Enablement Tribe Lead at T-Mobile Polska. FYI, businesses and domains are fundamentally similar in this conversation and are used essentially interchangeably.
Some high-level takeaways/thoughts/summarizations from Karolina's view:
As a leader in the data governance team at T-Mobile Polska, Karolina has a considerable number of different aspects under her including the data platform, the data warehouse, data quality, etc. Much like data governance as a term has many aspects.
Karolina started off the conversation with an important and useful point she emphasized a few times: you need to have specific challenges you want to solve before you embark on something like a data mesh journey. Not just "let's be data driven" - what business challenges are you trying to solve? And look at them from a "why" perspective - why does tackling this challenge matter? Those challenges could be poor data quality, time spent on non-value-added tasks, data discovery issues, etc. Define the problems and the pain points.
It's important to understand that digital transformation is about business transformation first and foremost. What is your business trying to achieve? And what are the target/expected business outcomes? More revenue? Cost savings? Etc. You need to define the pain points and what doing something like data mesh will do for the organization to secure cooperation from business leaders. And don't count on patience as you work towards a big value delivery in the future - you need to continuously create "incremental value" along the way.
For Karolina, there were three main challenges they needed to address relative to data with their data mesh implementation: 1) data quality was a constant issue - typically stemming from lack of real ownership/accountability and no standard term definitions; 2) data discovery - it was very difficult for data consumers to find data; and 3) time-to-market for new data and insights - the data function was becoming a major bottleneck to the business side.
Data governance can't only be an IT problem/challenge, per Karolina. In their data mesh implementation, they are focusing the central governance team on creating the tools and frameworks for the distributed teams to leverage. For instance, the business and technical metadata comes from the domains but the data catalog is offered as part of the platform by the governance team. This separation of duties has allowed quick time to business benefits when bringing on new teams to their data mesh implementation.
Karolina and team knew they were facing issues with data so they started interviewing business representatives to ask what were their biggest challenges. The governance team heard repeatedly data quality was an issue but didn't know exactly why they were having data quality issues. So they moved to increase accountability, assigning data owners and data stewards. Collaborating with the owners and stewards, they were able to figure out a few major causes were: a lack of real ownership, no common definitions, no real standard measurement of quality, etc. And addressing those challenges resulted in some quick wins to get positive momentum towards delivering continuous incremental value.
At T-Mobile Polska, Karolina has seen how crucial having a C-level sponsor is to succeeding with something like a data mesh implementation. It is very easy to lose prioritization - there is always a more pressing short-term business need than producing high quality data so you need someone that can make sure that data work isn't unreasonably pushed out. Specifically, they created a data governance committee to have strategic supervision of the data governance and data quality efforts and identify the strategic initiatives to continuously deliver incremental value and put things on businesses roadmaps.
Scott asked a question he asks many people: what is the reason for creating new mesh data products at T-Mobile Polska? Karolina shared that data products are initially created to serve reporting specifically in most cases. They can expand to serve additional use cases but there is a specific use case in mind for each new mesh data product.
Karolina discussed some of the new ways of working and the challenges around the necessary mindset shifts to implement something like data mesh. People were just used to data engineering delivering the data. So producers were used to throwing things over the wall and data consumers were used to making asks to a highly data literate group of people. So, they are inventing new ways of working and processes to not have data engineering handling the communication between teams. Business owners are in charge of explaining why owning and serving their data as a product can add value to their org, what is in it for each person in their own org. One explanation that has resonated well - and been proved out repeatedly - is that by moving to a data mesh way of working, there is a significant reduction in time-to-market for new data and insights including for the producing domain.
As part of their data mesh implementation, Karolina and team have been restructuring KPIs to make it possible to measure the impact of the data work they are doing. Their focus is on the impact to the business, not technical focused KPIs. One big goal - with a few proof points thus far - has been a reduction in data work that doesn't add value - reducing the time your data science team spends on things that aren't valuable means they can put more value-add models into production. Another big goal, as previously mentioned, is reducing the time-to-market for new data and insights as many other data mesh implementers are seeing. And Karolina's team is driving buy-in through results by showing data producers how much impact they are having or could have by providing quality data.
As for how T-Mobile Polska started their journey, Karolina and team started with laying the foundation for good data governance. They first found the data owners and the data stewards in each business. Then they explained the new responsibilities for those roles and why they were necessary. The data owner is at the Director level, essentially the business or domain owner, and the data steward is more of a subject matter expert. And if there are complicated data needs, that domain needs a technical data steward - an embedded data engineer - as well; but not many domains need a technical data steward. Another thing specifically mentioned was leveraging "change agents", the people with the will and the capabilities to drive large-scale change.
Karolina then shared some of the issues they've had with data democratization. Similar to what Ust Oldfield mentioned in his episode, just giving access to data when people don't really understand how to leverage data can do more harm than good. So T-Mobile Polska is pushing the not as data literate people to the data catalog as their only point of interface with data on the mesh; the governance team is focused on enabling producers to create standardized reports and datasets to serve those people. The more technical folks have more options to interface with data with fewer technical guardrails.
In wrapping up, Karolina reiterated a few of her main points. 1) Focus everyone on what you are trying to accomplish - what are the priorities? What is the impact to the business? 2) Look to deliver incremental value continuously to build and maintain momentum in your data mesh implementation - without that incremental value, support for your implementation is likely to falter. And 3) C-Level management support is crucial to really drive an initiative like data mesh - without it, your work is likely to get deprioritized and will be continuously pushed out.
Karolina's LinkedIn: https://www.linkedin.com/in/karolina-henzel/
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