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Karolina’s LinkedIn: https://www.linkedin.com/in/karolina-henzel/
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:
- Business transformation and impact is what really matters. Digital transformation is just a mechanism to transform your business into being more digital native and focused. Data transformation is just a part of digital transformation. Transformation should all come down to driving positive business impact.
- To drive something like data mesh forward, you really need top management support, likely a C-level executive sponsor. Otherwise, it is very easy for work to get deprioritized and pushed out.
- Don’t take on a large-scale data initiative unless there are specific business challenges to address. Don’t do data mesh for the sake of “being data driven”; what are the issues and why will addressing them help your business? Explicitly define the problems and the pain points.
- To drive change, look for “change agents” in the domains. They are people with the will and capabilities to drive large-scale change. They aren’t always easy to find but once you start to identify them, patterns will emerge.
- The big pain points T-Mobile Polska was facing were: 1) poor/inconsistent data quality; 2) data discovery difficulties; and 3) slow time-to-market for new data and insights.
- T-Mobile Polska was able to move forward with data mesh because business representatives in the domains were bought in that addressing the data pain points would drive incremental business value – there would be a return on the data work investment.
- Look for quick wins and how to deliver continuous incremental value. If it is all about producing a big bang, you will very likely lose momentum, prioritization, and funding. Continuous value delivery is crucial to keeping people excited about data work.
- T-Mobile Polska’s data quality issues were caused mostly by a lack of accountability/ownership and not adhering to standard definitions across domains and reports.
- Lack of standard definitions – or at least very clearly differentiated definitions – can cause numbers to not match across reports. And that makes people not trust the data. So drive domains to clearly define terms and, if possible, look to create standard definitions across the organization.
- Find KPIs that are focused on what actually impacts the business. Dave Colls mentioned fitness functions to break down measuring progress against big challenges into smaller measurements. Karolina and team are making sure the end result is business impact, not technical-only change.
- You can drive buy-in that data producers should provide high quality data products by showing producers the impact their efforts are having. Can be a chicken and egg issue at first but you can take the results from one domain or business and show them to another to drive buy-in.
- T-Mobile Polska has a data owner in each domain that has high-level data ownership accountability. There is a data steward in each domain as well that is the subject matter expert. When needed, there is also a technical data steward (embedded data engineer) but it’s not necessary in many domains.
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.
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