#242 Making Data Accessible Makes Your Data Work Successful – More on PayPal’s Data Mesh Journey – Interview w/ Kim Thies

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Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.

Kim’s LinkedIn: https://www.linkedin.com/in/vtkthies/

Gemba Walk explanation #1: https://kanbantool.com/kanban-guide/gemba-walk

Gemba Walk explanation #2: https://safetyculture.com/topics/gemba-walk/

PayPal Data Contract Template OSS: https://github.com/paypal/data-contract-template/tree/main/docs

Start with why — how great leaders inspire action | Simon Sinek | TEDxPugetSound: https://www.youtube.com/watch?v=u4ZoJKF_VuA

In this episode, Scott interviewed Kim Thies, at time of recording a Leader on the Enterprise Data Team at PayPal and now SVP, Client Innovation & Data Solutions at ProfitOptics. To be clear, she was only representing her own views on the episode.

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

  1. When talking about data mesh to execs, it’s helpful to go back to basics: “these are the four main principles, and this is what we’ve built and why.” Scott note: I recommend you slightly alter the phrasing, especially around “Federated Computational Governance” πŸ˜‰
  2. Look to Simon Sinek and “Start with the Why”. Always investigate the why for the other party. What would be enticing to your business execs to lean in on data mesh? But data mesh for the sake of data mesh is not going to win over the business.
  3. The communication and relationship building aspects of data work are often overlooked and will serve you better than just about any architectural or technology decision. Build the relationships so your great data work will address actual business challenges and be leveraged!
  4. ?Controversial?: Similarly, learn ‘the art of the conversation’ so you can extract from people their needs/wants and then see how you can help them meet those. What isn’t going well where you can help is a great question to find new use cases where the stakeholder will be engaged – you’re directly addressing a business challenge for them.
  5. It’s probably better to start from needing a solution and data mesh is a good fit rather than using data mesh as a hammer and looking for nails. You shouldn’t want to do – or at least shouldn’t propose doing – data mesh for the sake of doing data mesh.
  6. If you have a clear business use case, it’s much easier to get people engaged and keep them engaged/involved – around data mesh or data work in general. Look to a tangible benefit – e.g. cost savings is a pretty easy first use case to go after.
  7. ?Controversial?: Data teams – especially data engineers – need to spend more time “experiencing” how customers/users actually use data to deliver on their business objectives. It will lead to better outcomes and better relationships with the business leaders/teams.
  8. Leverage the Gemba Walk philosophy: walk the ‘factory floor’ and talk to people far more often. Ask them how they get their work done. It doesn’t need to be overly formal, just collect information to help others do their job better with data.
  9. You don’t get to rest once you’ve gotten initial approval. Execs’ attention will not last, they will start to focus on other challenges. Keep pointing to the business challenges you are addressing – not the data work itself – to stay relevant and near top-of-mind.
  10. It’s not as if every aspect of your business starts doing a data mesh approach when you start your journey. There will be compromises and other parts of the business will likely choose other approaches. That’s okay and normal. Build your momentum and successes around data mesh but accept it won’t be right for everyone, especially at first.
  11. Data mesh will be received differently by every person or at least every persona. Each of the pillars might resonate differently. So be ready when speaking to focus on the aspect that is getting them to lean in. You need to balance the four pillars in your journey but not every conversation πŸ™‚
  12. There is probably far more transformation needed in your data practices than you expect. Even after hearing that, there is still probably far more than you expect. Processes and hearts + minds especially.
  13. In the current business environment – likely headed for recession – you might be able to get people bought in on data mesh simply for time-savings for the data team. In downturns, cost cutting becomes far more attractive.
  14. Once you get your mesh journey going and you have some interesting capabilities to offer, it’s important to go out and find additional use cases. Even if you’re proving a lot of value, people still probably need a little convincing or at least some additional understanding of what you’re doing, they won’t all come to you.
  15. To speak the same language as your business partners “you have to listen first.” It’s pretty easy to assume you mean the same thing but even foundational phrases like data product and data contract often have completely different meanings for people within the same organization.
  16. It’s incredibly easy to overlook user experience in data. Don’t fall into that trap! Scott note: we did a data user experience panel if you want to dig deeper, episode #190.
  17. ?Controversial?: Domain ownership is probably the most important aspect of data mesh because it puts the data back in the hands of the people who really know it best. Context loss is such a prevalent problem in data and data mesh solves for that quite well.

Kim started off with a bit about the PayPal journey to data mesh. They weren’t looking to do data mesh, they had a specific business problem of disparate data sets across multiple domains that needed to be combined with decent governance and observability. It just so happened that data mesh was a great fit, so they went the data mesh route. And there actually was an engineering-led conversation of the engineering team looking for a use case to use data mesh but that didn’t end up being the data mesh initiative that went to production. There was also a bit less scrutiny on a business-led journey because it was within a larger line of business/domain rather than it was the core data team strategy.

One thing Kim believes is that data teams, especially data engineers, do not spend enough time really understanding and “experiencing” how customers use data. They should go and pair closer to the business teams to deliver better solutions. That will lead to better outcomes but also better relationships. This is that aspect of data UX that is often overlooked. People want something to make their jobs easier or make them perform better, not just data. So how does data actually intersect with that?

To actually do that ‘experiencing’, Kim and team leveraged the Gemba Walk philosophy (see links above for a deeper dive). It’s a Japanese concept of going and ‘walking the factory floor’ to collect information. You don’t need to do super formal interviews, meet people where they are and ask them questions about their day-to-day. Get a sense of what really matters and how they do work. If you just ask people what they want, they might give you the Henry Ford ‘faster horse’ answer versus you discovering their points of friction.

Kim discussed the challenges of not just keeping but retaining buy-in and attention to your data mesh implementation – or really any data work. Once you get the approval, there are probably things that are more top of mind for your business partners, especially compared to the specific data work parts. So keep circling back to the business challenges you are addressing in conversations to stay near top of mind. Kim and team accomplished that by getting to a really rapid, tangible proof of concept. They quickly had something to show from the work that made it clear this could work at scale.

Starting from entirely inside one business unit meant Kim and team had a lot of autonomy. As soon as they were brought more into the central data team, things changed and there was much more focus on communication and sharing – and compromising too – especially around technology choices and approaches. Many teams were taking different approaches to similar challenges with the same technology so how do you get to best practices?

One thing that really stuck out to Kim about data mesh and driving buy-in is that different pillars resonate for different personas. Data scientists or even data engineers embedded into a domain often love the self-serve aspect. Not even for consumption but being able to actually self-serve their own data production needs. They aren’t afraid of the ownership principle of data mesh because they can own their own timelines and not get stuck in centralized bottlenecks. They might not even realize they are struggling pre data mesh because the central bottleneck/friction is so ingrained to their way of working, you can really make things far better for them but they wouldn’t have considered asking.

Kim believes you should be prepared to market your mesh capabilities once you have them up and running. That doesn’t have to be a sales-y approach but you want to go and find additional challenges people couldn’t solve but now could with mesh to gain more momentum, converts, and funding. Learning the art of the conversation is crucial. Is there a way that you can advance their business goals, address their challenges? If yes, they are more likely to lean in and stay bought-in to the use case. Don’t be afraid of a little sales and marketing tactics to get to better business outcomes for all.

It’s very easy to use the same term and mean something different. Just look at all the what is data mesh or what is a data product content out there. So Kim believes when talking to your internal partners, start from listening first so you understand how they are using different terms. It’s easy to jump to solutioning instead of understanding but your solutions will be lacking without the understanding πŸ™‚

It’s pretty easy to lose track of the customer experience in data mesh in Kim’s view. There are so many things to focus on and honestly, UX design in data hasn’t really been a thing. As an industry, we haven’t talked about UX design for the platform or the data product much before data mesh started to force the conversation. If you have all the capabilities in the world but a bad user experience, are people really going to use what you’ve built?

Circling back to communication, Kim talked about the challenges of communicating with execs and getting bogged down in the work done. She said it’s important to start from baseline communication around data mesh: “these are the four main principles, and this is what we’ve built and why.” If you start there, then people can really start to connect the work to what might be beneficial for them. And as she stated earlier, still look to start from the listening and understanding aspects first and then always start from the why. Scott note: see the YouTube link above.

In wrapping up, Kim talked about the through-lines of the conversation. Establish and build relationships, not just data products. Learning the actual problems/challenges is key. Talk to people where they are before you try to build something for them. Understand what’s actually going on and where their friction points actually are. And then also look for scalable business cases – the best way to discover those is by establishing the relationships πŸ™‚

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Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him on LinkedIn: https://www.linkedin.com/in/scotthirleman/

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All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

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