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The 2022 Data World in Three Words: “I Was Wrong” — 6 Truths I didn’t understand 365 days ago.

Photo by Michal Matlon on Unsplash, modified by the author.

(1) The Data Mesh is there to stay, but it’s not coming as fast as expected.

The data mesh keeps haunting me, whenever I think I’m done writing about it, it comes back. At the beginning of the year, I was finishing the book “Data Mesh in Action” going through all kinds of data meshes out in the real world and drafting data mesh blueprints for start-ups. My position at the beginning of the year was “the data mesh is coming to every company & every industry, fast”.

(2) “Just do what the software engineers do” isn’t going to cut it in the data space.

I tend to argue, the data mesh is yet another decentralization move, just as all the others that the software engineering & product world already performed. So at the beginning of the year, it only seemed natural to me to keep on saying “just transfer practice X from software engineering to data and you’ll be so much better off”.

  1. Data people still have largely a different background, culture & practices than software engineers
  2. The data value creation process looks different than the software one
  1. The data mesh as “just another decentralization movement” makes sense only, if the company culture supports it.
  2. Machine learning integrated into products only makes sense, if the company has product management that openly embraces this.
  3. If your company considers data to be a sidekick, then no big investment in best practices makes sense.
  4. Pushing data into your CI system makes sense only as long as it stays small. Yes, it’s called “dbt — data build tool” — but it is not the same as a software build process, not for the vast majority of cases.

(3) 99,9% of companies are not aware of what data is important and what to do with it.

At the beginning of 2022, I believed 90% of companies are wrong about these big ideas. But over the year 2022, two things happened:

  1. I realized more kinds of data are important than I believed to be before (see below)
  2. A few major trends actually moved the world in the opposite direction!
  1. Real-time data use (not to be confused with event-driven architectures)
  2. Unstructured data (and everything that builds on top of a use case)
  3. Company external data (and everything that builds on top of as a use case)
  4. Turning data into actions

(4) Even a collapsed crypto world has so much to teach the data world.

Photo by Kanchanara on Unsplash.
  1. Systems thinking tops product thinking.
  2. Community-led innovation tops company-internal innovation in terms of breadth.
  3. Open tops closed.

(5) OS is hard, like really hard to pull off. And nobody talks about it.

Publishing open source for business, a lot of companies pull this off right? Google is leveraging open-source solutions like Kubernetes to achieve huge business goals. RedHat, Automattic, and GitLab, all built successful companies on open source. It should be obvious and easy to publish open source to achieve business goals.

(6) Open Source is not the only option for data companies, but key challenges need to be solved.

In How to Become The Next 30 Billion $$$ Data Company, I argued that the only way to become a great data company is to rely heavily on open-source.

  1. some kind of standard/ protocol,
  2. on a large degree of openness,
  3. and need to make network effects key to your company strategy.



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Sven Balnojan

DataOps @ Meltano | Data PM | co-author of “Data Mesh in Action” | Join my free data newsletter “Three Data Point Thursday” at