Keeping Up With Data — Week 18 Reading List


Many companies want to ‘start with data’ properly. So, they wait for the perfect moment — when new system is implemented, more data is collected, a team is hired, transformation is finished and so on. Unfortunately, the perfect moment never comes. Just as in life. I typically suggest: just do it. Start with a data strategy built around the imperfections rather than keep waiting forever. But the first step is always the hardest.

To make is a bit easier, check out some of the articles that caught my attention this week.

  • Scaling Data: Data Informed to Data Driven to Data Led: This struck a chord with me: “data needs to be seen as a strategic lever for growth”. Though the article is (probably) targeted at start-up founders, there is a lot of things that are generally valid. Hiring a team and implementing a set of tools is not a data strategy. One needs to start with the points of lever, reflect the stage of the business and then bring the right team and tools. My stance is that the ‘blueprint’ is rather overfitting to tech start-ups and the ‘team’ category should be broader — other employees, clients, suppliers matter too. But I really enjoyed this article as it validates a lot of my observations from more mature businesses, even in traditional industries. (Reforge)
  • BAYESIAN AND FREQUENTIST RESULTS ARE NOT THE SAME, EVER: Two different interpretations of probability, two different results. Are they comparable? Should we compare them? “The whole point of Bayesian methods is that a posterior distribution is more useful than a point estimate or an interval because you can use it to guide decision-making under uncertainty”, says a Bayesian statistician. The question should be, what do you need to solve your problem? Point estimate or posterior distribution? (Probably overthinking it)
  • Inside Netflix’s Quest to End Scrolling: “Let’s watch a movie tonight,” has become a running joke at home (since our son was born, we watch like a movie a year). I’m not suffering from a choice paralysis of what to watch! But I’m curious about this next step in the evolution of recommendation engines. ‘Play something’ will not only suggest but actually make a decision for you. Let’s see how it will land. Are recommendation engines advanced enough? It would be great as I can’t wait for ‘buy something’ functionality for online grocery shopping! (Vulture)

Another recommendation system (on Spotify) led me to an album by European Jazz Trio and their Piano Jazz meets Classic album. The album is four hours long and accompanied me during tonight’s reading about data.

In case you missed the last week’s issue of Keeping up with data

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 | Interim CDO at

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Adam Votava

Adam Votava

Data scientist with corporate, consulting and start-up experience | avid cyclist | amateur pianist | Interim CDO at

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