Evolution of Data Management Systems

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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.

Carol's LinkedIn: https://www.linkedin.com/in/carol-assis/

Eduardo's LinkedIn: https://www.linkedin.com/in/eduardosan/

Continuous Integration book: https://www.amazon.com/Continuous-Integration-Improving-Software-Reducing/dp/0321336380

Measure What Matters book: https://www.amazon.com/Measure-What-Matters-Google-Foundation/dp/0525536221

Inspired by Marty Cagan: https://www.amazon.com/INSPIRED-Create-Tech-Products-Customers/dp/1119387507

Empowered by Marty Cagan: https://www.amazon.com/EMPOWERED-Ordinary-Extraordinary-Products-Silicon/dp/111969129X

In this episode, Scott interviewed Carol Assis, Data Analyst/Data Product Manager and Eduardo Santos, Professor and Consultant, both at Thoughtworks. To be clear, they were only representing their own views on the episode.

From here forward in this write-up, I will be generally combining both Carol and Eduardo's views into one rather than trying to specifically call out who said which part.

Some key takeaways/thoughts from Eduardo and Carol's point of view:

1. At the end of the day, the team that produces the data will get the most use out of it 9/10 times. Getting teams used to developing with data in mind isn't just useful for the organization, it is for maximizing their own team's success.
2. Continuous integration is a crucial concept in general for learning how to automate and focus on delivering more, which leads to focusing on value. Read the book :)
3. ?Controversial?: Data mesh is an extension of the continuous integration book/concept because of the focus on delivering value quickly and building to scale reliably.
4. There are many methodologies for understanding value delivery in software. We just have to adapt them better to data. Don't reinvent the wheel.
5. Far more organizations need to think about the goals of their products and then how to measure success against those goals _at product inception_. Design data into your products from the start.
6. Data people often make the data overly complicated for non-data people to grasp. What does the data tell us and what are some simple numbers? Then people can feel like they understand without going too deep into stochastic modeling or something.
7. Relatedly, engage data consumers' curiosity - including the producers that will consume their own data. Try to meet them where they are to get them to engage with data more. Lower the perceived bar to leveraging data.
8. Application development teams need convincing that working with data is 1) essential to understand their own success to further improve their products and 2) much easier than it has been historically. There is a LOT of scar tissue out there…
9. A potential good hook to get people to build their applications with data in mind is being able to show metrics and measure success from day one. The business side can get a better idea and are more likely to engage; it gives a communication bridge between developers and the business people.
10. Having data early in the application development cycle means you have more proof points for making your decisions - assuming you side with the data 😅 that makes it easier to justify decisions instead of people making guesses.
11. Measuring what matters is a crucial concept for the entire team to adopt. It will help people understand what data they need and why.
12. Pressing people on what to measure and then how to measure it crystalizes what bets they are making. How are they expecting users to interact with their applications?
13. For many business people, you may need someone playing the data translator role, translating data to business and business to data. Most organizations' data literacy is still quite low and again, you need to lower the bar to business people leveraging data.
14. ?Controversial?: You don't need to start with data products. Yes, they are great, but teaching your teams that data matters is groundwork to head in the direction of something more scalable. A spreadsheet is a fine place to start. Focus on delivering insights that deliver value, then work towards the productized aspects :)
15. Ask people what action they will take once they have data. Get them in the mindset that data drives action and data that won’t drive action isn't where they should focus.

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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