#163 Improving the User Experience for All Parties – Early UX Learnings from Data Mesh at DNB – Interview w/ Alice Parker

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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.

Alice’s LinkedIn: https://www.linkedin.com/in/aliceparker/

No Silver Bullet: Essence and Accident in Software Engineering by Fred Brooks: https://www.cgl.ucsf.edu/Outreach/pc204/NoSilverBullet.html

IBM Research paper mentioned: https://dl.acm.org/doi/10.1145/3290605.3300356

Microsoft Research paper mentioned: https://dl.acm.org/doi/10.1145/2884781.2884783

In this episode, Scott interviewed Alice Parker, Data Engineer at DNB.

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

  1. It’s easy for people to confuse user experience (UX) and user interface (UI). But UX is far deeper than most understand. We need to design systems and experiences that make working with data – as a producer or a consumer – far easier and more delightful.
  2. People are very willing to talk about their challenges – show some empathy and give them the space to talk about what is holding them back and what they could do if you worked with them to address those challenges.
  3. Data consumers need three major things to work well with data: 1) domain expertise, 2) time, and 3) to be able to “converse” with their data.
  4. Ensure your data quanta – or really any aspect of your data mesh implementation – are documented for all your user personas. There may be different needs for each persona type. A data scientist probably doesn’t need as detailed of explanation of lineage if they can see the transformations compared to a business analyst.
  5. As part of designing our UX, we need to focus on “how to help users achieve their goals effectively, efficiently, and with satisfaction, how you can minimize risks, and how you can enhance the maintenance of tasks to be completed effectively.”
  6. To get UX right, you have to understand “the limitations of the humans using it.” Because you have to design your experience and your risk tolerances around those limitations.
  7. “Systems and technologies evolve incredibly quickly, but unfortunately, humans don’t.” Have empathy for that. The human brain’s processing power does not evolve at the same exponential rate as Moore’s Law, so take into account the limitations of cognitive load.
  8. It’s easy to fall into the trap of building one-size-fits-all experiences. But in data mesh, especially as we raise people’s data literacy, their data capabilities, we need to make sure we understand persona needs and desires so we can design for them.
  9. Interviews are crucial to understand what your personas actually need. Speak with them, reflect back their pain, understand what they are trying to accomplish, and then work to build something that addresses their needs without being tied to any one use case, domain, or person.
  10. Your user experience will change and – hopefully – improve. Communicate with your users what you are doing and why, bring them in to the conversation. It’s important that they understand you don’t have a magic wand but you do have a sympathetic ear 🙂
  11. Prioritization is key to delivering on an improving user experience in data mesh. There will always be a deluge of requests, figure out what are actual requirements instead of nice-to-haves and then assess your capability to deliver and the cost/benefit of delivering. Then communicate your prioritization.

Alice started by talking about her recent Master’s thesis which was studying human computer interaction, specifically around data mesh at DNB. And it’s a huge topic. To start though, she emphasized the difference between user experience (UX) and user interface (UI). Your experience is impacted by much more than just the visual buttons you click – or knobs and levers you move if on a more industrial setup – so user experience is much deeper than many think. Zhamak has mentioned user experience – in multiple contexts – as a crucial factor in getting data mesh right. It’s a rarely explored topic in data, especially designing your architecture to provide a better experience for data producers and consumers.

According to Alice, there is even an ISO standard focused on experience. It’s about “how to help users achieve their goals effectively, efficiently, and with satisfaction, how you can minimize risks, and how you can enhance the maintenance of tasks to be completed effectively.” And breaking that down into each piece can be a pretty deep conversation. How do we ensure satisfaction of data users? What is risk when it comes to data? Risk of interpretation? Misuse? A crucial aspect of UX is understanding the capabilities and limitations of the people using it.

As Alice noted, “systems and technologies evolve incredibly quickly, but unfortunately, humans don’t.” And we can’t think of every person as being the same with the same needs and capabilities. Which unfortunately means, one size will not fit all when thinking about building your platforms for data mesh. We have to design to serve persona needs. And to actually understand persona needs, we need to speak with them.

When listing out the different personas, just on the data consumer side Alice mentioned data analysts, data engineers, data scientists, data stewards, and business owners. On the producer side you have data engineers, data scientists, software engineers, and business owners. Then in kind of the in-between, other personas you have platform engineers, data governance people, etc. So you have to think about what each persona needs and then design for each persona. The personas even have different terminology, different ontologies they use. If only this were that easy 🙂

What this all leads to is going and actually talking to your potential users to find their needs – which is what Alice did with interviewing data consumers for her Master’s thesis. Similar to what Jen Tedrow mentioned in episode 98, you need to go and listen to their pain points and then abstract away the use cases to see what are the bigger needs. And you can do that in a very informal way too. But you can really only get that feedback through conversation and explicit effort. Scott Note: this is EXTREMELY true… the number of times I’ve asked for feedback and gotten crickets is… yeah…

Alice noted it’s important to let people know when their requirements won’t be met, or won’t be met on their timeline. That open communication will get them to trust you. It’s okay to say no to a change to user experience, especially if you have good reason for it that you explicitly communicate to them. You can work with them to maybe find the quick wins that gets them additional value now. Prioritize what can be done now and explain why for your prioritizations.

Documentation is one crucial aspect that has been lacking in data according to Alice. Yes, data mesh calls for data quanta to be well documented but we need to ask who are we actually documenting for? If you have 5 different consumer personas for your data product, is it documented so all of them can actually use it? And then how do we make it as easy as possible for data producers to actually create and update that documentation? Does documentation only have to be written? What are some low friction ways to share the context of what a data quantum is all about?

It’s crucial to think about incremental progress – and showing that incremental progress – on your user experience in Alice’s experience. Every system everywhere will have frustrated users. Such is life. And you can’t solve most challenges in a day. But look for ways to iterate towards a better UX and circle back and show people you are improving it – if that’s data product creation cycle time, show them your prioritizations that sped up that cycle time and show them the improvements, show them you listened and reacted. They might still be grumpy but at least they have reasons to be less so.

As other guests have noted, Alice has seen most people are very willing to share about their current challenges. If you go with the right attitude – to listen and empathize – you can learn a ton. People want to feel seen and heard. And it might help more with your prioritization than you’d expect. As Alla Hale said in episode 122: “what would having this unlock for you?”

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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