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🐰 #39 ThoughtWorks Tech Radar, OS Incentives and GitHub's CoPilot; ThDPTh #39 🐰

Three Data Point Thursday
🐰 #39 ThoughtWorks Tech Radar, OS Incentives and GitHub's CoPilot; ThDPTh #39 🐰
By Sven Balnojan  • Issue #39 • View online
How “product-thinking” really should work, how to set the right incentives in your os project for contributions, and why I love Github’s Copilot feature.
Data will power every piece of our existence in the near future. I collect “Data Points” to help understand & shape this future.
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🔮 (1) ThoughtWorks Tech Radar 24
I always enjoy ThoughtWorks Technology radar, but the current one is particularly well suited for my recent thinking about data meshes & data platforms:
“Increasingly, organizations are adopting a platform team concept: set up a dedicated group that creates and supports internal platform capabilities — cloud native, continuous delivery, modern observability, AuthZ/N patterns, service mesh, and so on — then use those capabilities to accelerate application development, reduce operational complexity and improve time to market. This growing maturity is welcome and we first featured this technique in the Radar in 2017. But with increasing maturity, we’re also discovering antipatterns that organizations should avoid. For example, “one platform to rule them all” may not be optimal, “big platform up front” may take years to deliver value and “build it and they will come” might end up as a wasted effort. Instead, using a product-thinking approach can help you clarify what each of your internal platforms should provide, depending on its customers. Companies that put their platform teams behind a ticketing system like an old-school operations silo find the same disadvantages of misaligned prioritization: slow feedback and response, resource allocation contention and other well-known problems caused by the silo. We’ve also seen several new tools and integration patterns for teams and technologies emerge, allowing more effective partitioning of both.”
Now what ThoughtWorks is advocating is to follow the “product-thinking approach”. I think “product-thinking” can be misunderstood and is misunderstood a lot! There are at least two versions of “product-thinking” when it comes to platforms.
Version 1: I (product-)think we need a data platform. Let’s pick a team, task them with building one, and then see where things go.
Version 2:
  • Step 1: Put the responsibility for X inside each single team (or unit, the knowledge system is an internal product aimed at individuals, there are other products aimed at hole business units)
  • Step 2: Make them accountable. Wait and see what solutions emerge.
  • Step 3: Support the solutions with products, if necessary.
This is the process a lot of product teams evolve, how Borg (later Kubernetes) emerged out of a need of one product team launching a new Google search engine. They built Borg to do that. After it was there, virtually all workloads switched over to Borg. But that’s the thing, the product or big parts of it, already were there.
Now here’s the key difference: It seems to me from the outside, that a lot of data mesh implementations and data platform implementations follow Version 1; There usually isn’t any part of the platform there. This will likely end in what ThoughtWorks describes as again “one platform to rule them all”.
So what can we do? It’s starting to start thin! And then even thinner, let a suitable solution emerge. Start with a simple wiki page, without any team at all to manage the platform. If that doesn’t catch on, chances are you don’t actually need a platform yet.
Oh, and of course I recommend every single edition of the Tech Radar.
Resources
🎄 (2) GitHub's CoPilot AI Programmer
I just stumbled over GitHub’s CoPilot, an AI programmer that assists you. Just like a real copilot, it’s the thing that does all the boring stuff while the pilot does the heavy lifting and then goes to sleep (sorry to all pilots & copilots for this analogy!).
The thing is, every expert programmer will say “Nah I don’t need this.”; But that’s the thing, it’s not for the expert programmer! It’s similar to the Surfing AI or Kayaking AI I mentioned before in this newsletter, it’s about bringing expert coaches to the masses which in turn will enjoy a huge lift up.
I definitely like the direction this is going.
Resources
🔮🔮🔮 Data Company Corner 🔮🔮🔮
Stuff that might be interesting for anyone at the front line of the data world, inside a data company, inspired by much positive feedback from my article on commercial open source software data companies.
🐰 (3) Contribution Incentives
This piece is just a link to read and think, think about incentives for contribution. As I’ve mentioned a dozen times, I think contribution will make or break your data company (because every data company should be heavy in the open-source space). So how do I get people involved deeply?
I just read how the blockchain company Mirror.xyz does it. They have something called a “Token Race”, details in the link. The thing is, the token race is not just about gamification, it has two very unique mechanisms:
  • Participating allows people to actually enter the platform (so it’s a true enabler)
  • It allows the community to have THE say (not just A say) in the direction of where the platform contributions should go.
If you compare that to other efforts to build strong contribution communities, this is truly different and possibly stronger. If you want some thought food to compare to, check out Airbytes “Connector Competition”. And keep in mind that Airbyte totally blows my mind with the speed they are producing connectors at.
I like the quote from mirror.xyz:
“​​Joining Mirror does not only make you a community member. It makes you a co-owner of the platform. As a result, our platform is a sum of our contributors.”
I think we should think about contributors to OS the same way, they are not just contributors, they are co-owners.
Resources
🎄 In Other News & Thanks
Thanks for reading this far! I’d also love it if you shared this newsletter with people whom you think might be interested in it.
P.S.: I share things that matter, not the most recent ones. I share books, research papers, and tools. I try to provide a simple way of understanding all these things. I tend to be opinionated. You can always hit the unsubscribe button!
Did you enjoy this issue?
Sven Balnojan

Data; Business Intelligence; Machine Learning, Artificial Intelligence; Everything about what powers our future.

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