Keeping Up With Data — Week 12 Reading List

5 minutes for 5 hours’ worth of reading

Source: https://eng.uber.com/ubers-journey-toward-better-data-culture-from-first-principles/

oing extra mile despite 95-hours week might be the norm at Goldman Sachs, but it’s not for me. So please excuse the brevity of this week’s reading list because I had a pretty busy week and I feel I’ve shown enough solidarity with the investment bankers — and other start-up founders!

So, here it comes. Short. And concise. I hope.

  • Uber’s Journey Toward Better Data Culture From First Principles: Better data culture sounds like a sensible goal. But what does it mean and how to get there? Uber engineers laid down the basic principles of a better data culture and it worked from them. When describing the first principle — data as a code — they say that “creation, deprecation, and critical changes to data … should go through the design review process … where consumers’ views are taken into account”. This is, imho, immensely important. A significant culture shift will be required for people to stop thinking about data in isolation but rather in flows used to solve business issues. (Uber Engineering)
  • Chief Data Officers Struggle To Make A Business Impact: Sometimes is good to check the rear-view mirror and look a few years back. Despite the evolution in analytics and data stack, CDOs seem to be facing very similar problems now as they were a few years ago. How to manage data as an asset? How to forge data culture? How to create a data-driven organisation? Start with a strategy! Because “firms that seize the initiative, take a long view, persist in their efforts, and learn from their experience, will lead the way”. (Forbes)
  • The Top 5 Data Trends for CDOs to Watch Out for in 2021: Staying with the CDO, but this time looking forward. It’s hard to imagine that someone would miss these topics but it’s helpful to have them highlighted in one place. For me, it is interesting to see the order of the topics. As a CDO, I’d focus on them in a different order — start with platform lead, then data quality, followed by data stack, metadata and ‘lakehouses’ at the end. But I guess the priorities are often ranked by costs rather than opportunities. (Prukalpa @ Towards Data Science)

Apart from the week being very busy it was also really exciting and packed with new learnings. I’ve been educated about available managed Kubeflow solutions, had many insightful conversations with clients and other data scientists and even met an author of a book about leading a data science team!

Thanks for reading!

Please feel free to share your thoughts or reading tips in the comments.

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Data scientist with corporate, consulting and start-up experience | avid cyclist | amateur pianist | CEO & co-founder at DataDiligence.com

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