Empowering Engineers to Build User-Centric Data Platforms

Please Rate and Review us on your podcast app of choice!

Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.

Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.

Michael's LinkedIn: https://www.linkedin.com/in/mjtoland/

Marta's LinkedIn: https://www.linkedin.com/in/diazmarta/

Sadie's LinkedIn: https://www.linkedin.com/in/sadie-martin-06404125/

Sean's LinkedIn: https://www.linkedin.com/in/seangustafson/

The Magic of Platforms by Gregor Hohpe: https://platformengineering.org/talks-library/the-magic-of-platforms

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

In this episode, guest host Michael Toland Senior Product Manager at Pathfinder Product Labs/Testdouble and host of the upcoming Data Product Management in Action Podcast facilitated a discussion with Sadie Martin, Product Manager at Fivetran (guest of episode #64), Sean Gustafson, Director of Engineering - Data Platform at Delivery Hero (guest of episode #274), and Marta Diaz, Product Manager Data Platform at Adevinta Spain. As per usual, all guests were only reflecting their own views.

The topic for this panel was how to treat your data platform as a product. While many people in the data space are talking about data products, not nearly as many are treating the platform used for creating and managing those data products as a product itself. This is about moving beyond the IT services model for your data work. Platforms have life-cycles and need product management principles too! Also, in data mesh, it is crucial to understand that 'platform' can be plural, it doesn't have to be one monolithic platform, users don't care.

Scott note: As per usual, I share my takeaways rather than trying to reflect the nuance of the panelists' views individually.

Scott's Top Takeaways:

1. You will hear "product mindset" a lot in this panel. It's important to embrace product management as a mindset and not an exact set of things to do and approaches to take. The whole point of a product mindset is to find what works and deliver reliably while focusing on value.
2. Be ruthless. Ruthlessly prioritize and ruthlessly focus on user centricity. In data, we have a tendency to fall in love with the tools instead of the jobs to be done. But a good platform is about making it easy to get high-value work done and that's rarely about exposing tooling to users.
3. Your platform isn't going to magically drive usage. There is change management required to get people to leverage your data platform more. Be prepared for that change management work and closely aligning with users, whether they are 'data people' or not.
4. Platforms and products in general are about scalability. Through a platform, instead of reacting to tickets, you build services and capabilities to address the problems that caused the tickets - far more scalable than reacting to the individual tickets, addressing the disease not the symptom. By building your platform as a product, you focus on what actual capabilities are within scope so you can continue to manage and expand your data platform.
5. As much as it would be amazing to build a data platform from scratch - think how amazing you could build it! - in many cases, you will have to build off of existing services and platforms. Don't be too dogmatic - what matters is continuing to get better not being perfect. Give yourself the space to improve the platform but products live in the real world and the real world of your organization has current/existing business needs and constraints.
6. Products - especially in software - are as much or more about evolution as they are about their form/function at initial launch. The same should happen with your platform. And the same should happen with your vision. Don't get locked in, don't get tunnel vision.
7. Generally speaking, most data platforms are still not serving the role a software platform - data or otherwise - should serve. They are still about the tech instead of the capabilities. You aren't alone if your platform doesn't meet the ideal vision. Your role is to make it better but it's still probably not going to be a sparkling beacon of perfection anytime soon 😅
8. Your data platform needs to be aligned to your company culture. So you have to meet people where they are and properly set down the easy path to where you want them to go. It's a long journey.

Other Important Takeaways (many touch on similar points from different aspects):

1. The product mindset is for the entire team, not just the platform leader and/or product manager. Your entire team will have to change their approach and mindset. Not overnight but it's hard to break old ticket-taker habits.
2. Make sure to put your work in the context of the business. It's easy to get bogged down in the platform engineering aspects or the data tools but your data platform is there to serve business purposes. Focus on what delivers value for the business.
3. Your platform doesn't have to address all your organization's data challenges at once, right at the start. Find where you are seeing the biggest challenges with delivering value - maybe listen back to episode #297 on the Data Value Chain - and look to focus there first. Build up to better.
4. "Build with [your users], not for them." Don't treat your platform as a project. Yes, that's the subject of the panel but it's very important to always keep close at heart.
5. User experience is such a crucial aspect of good software platforms. It's probably even more important when it comes to data platforms if you want to bring new users to producing and consuming data. But it's HARD and rarely discussed.
6. What is the goal of your platform? That might sound like an obvious question but it's not really when you go to answer it. Maybe it's "make it easier to work with data" but easier for who? Really map out good outcomes of a well-made platform.
7. Good products - at least good software products - aren't designed to be perfect at launch. Give yourself the space to not be perfect but set yourself up to understand where things can be improved and then iterate. Test, learn, iterate.
8. What are your data platform KPIs? What will measure if you are being successful? Really consider what bets you are making and why. Usage isn't always good but it's a good first indicator as you are getting moving.
9. To make your platform a product, you have to come back to the vision (or goal mentioned above). Otherwise, you have a collection of services. How is that vision tied to business value? If you need budget, do those holding the purse strings care about your tech decisions or business impact?
10. A good platform helps people get what they need done with the agency to do it. It limits - and where possible eliminates - bottlenecks.
11. A crucial aspect of product management is taking user needs and then translating that to build something to serve those needs. But when it comes to your data platform, the users and the builders (the data engineering team) usually don't speak the same language so you will probably have to spend more time translating between the users and the team building the platform than even in software. Be prepared for you data team to grumble about more meetings too 😅
12. Combine Simon Sinek's 'Start from the why' and user centricity. The why should be about what are you enabling your users to actually accomplish and what value that drives. Really focus on building something that drives value for the business instead of leveraging the coolest tech.
13. Relatedly, it's likely going to be very difficult to measure the return on investment on your data platform. Be prepared for those conversations but it can be pretty squishy.
14. At their heart, good products are about creating good user experiences and delivering/capturing value. Products need to deliver value to users and producers need to capture value as well. That is a complex topic when the value captured is internal, but it is still an appropriate mindset. If you aren't able to prove value, will you get further funding?
15. Consider your company needs relative to data maturity. An incredibly cutting edge platform for an organization that has low data maturity is not the right fit. Even if you have a cutting edge vision for their work, your 4 year old won't be able to out sculpt Michealangelo. Be realistic and use the platform to drive towards better data capabilities but you have to meet your organization at least close to where they are.
16. You might have difficulty getting people bought in on the vision of your data platform. If people view data as ticket-takers/a service instead of an integral part of the company's way of doing business, be prepared for the fun of lots of stakeholder alignment work.
17. Where possible, don't only look to sell people on your vision. Try to change - or possibly nudge at most - people's behavior through your platform. It's a subtle art and hard to pull off but it will mean people do what you and they both need but without having to build a million PowerPoint decks 😅
18. It's important to separate your concept of a data platform and the approach of treating it as a product. Otherwise, your platform can easily fall into the trap of designing to user wants instead of what is a platform's function - to make it easier to do work in a scalable, reliable, and repeatable fashion. You need to consider the purpose of the platform instead of throw product management at a collection of tools.
19. Leaders/execs want change. Leaders/exec do not want _to change_. Be prepared for that.
20. Users aren't necessarily ready for the data platform to be a product. They are used to the ticket-taker model. Be prepared to shift their mindset too, not just that of the (data) engineers building the platform. It won't be a simple switch over either - very deep topic…
21. Relatedly, you will probably need a longer run-way to transition your team to a product model for the platform. It is a mindset shift but it's also a big change in the ways of working. You will need to have some patience, you can't switch from a ticket model to only building your platform as a product overnight.
22. A key potential area of value for the platform is making it fast to prototype and get data in consumers' hands. It can reduce a ton of back and forth and also help you quickly discover if further work on a potential data product is useful or if it should be scrapped. Research spikes are very important in data and enabling them can be very valuable - if not always valued 😅
23. Make sure to think about the actual scope of your data platform. Products have scope, don't try to be all things to all people and don't bite off more than you can chew.
24. There's a reason SaaS offerings in the data space have post-sales and customer success engineers. Many of your users won't just simply be able to read the documentation and use your platform no matter how well you build and document it. Be prepared to ensure success through a bit of hand-holding. Ensuring usage that drives value is (usually) what makes software and data offerings successful.

Learn more about Data Mesh Understanding: https://datameshunderstanding.com/about

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/

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

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