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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:
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 :)
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 :)
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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