Keeping Up With Data #72

5 minutes for 5 hours’ worth of reading

Adam Votava
3 min readMar 4, 2022

Three things I’ve noticed in the last few weeks. First, more and more companies are actively exploring data mesh, which will likely lead to case studies and playbooks others can learn from. Second, there are still companies claiming to be built on AI/ML, when in fact they are run by a sophisticated set of rules or clever equations. And that’s fine! Just be careful about claiming something the customers don’t care about, and the investors will validate during their due diligence. And third, how come there are no widely used Python libraries for attribution models?

Three articles that made it to this week’s list:

  • Fashion DNA: Having an accurate representation of products and their properties is key for online retailers. It can be used for accurate pricing, categorisation on the website, or to make personal recommendations to customers. More-or-less manual tagging of the products can only get you so far. There is so much more that can be detected from an image. Or, even better, from multiple images. To do that, Zalando is using fashion DNA — “encodings extracted as hidden activations in a feedforward deep neural network.” (Zalando research)
  • Abundance Mentality is Key to Exploiting the Economics of Data: Quoting Bill Schmarzo’s article: “Data and analytic silos persist due to an outdated ‘scarcity mentality’ — a mindset where opportunities, resources, and successes are limited and as such must be guarded or hoarded. […] Economies of scale are the ability to rapidly learn, apply those learnings, and continuously learn and adapt from the application of those learnings. The economics of data and analytics is built around the concept of sharing, reusing, and continuously refining. That is, the more that we share and reuse, the more valuable these digital assets become.” (Data Science Central)
  • Netflix: A Culture of Learning: I’ve been following this series on A/B testing and culture of experimentation very closely. It is a great example that creating a data-driven culture is a complex problem — as the article puts it: “To support the current and future scale of experimentation […] Netflix has invested in culture, people, infrastructure, and internal education to make A/B testing broadly accessible across the company.” And you know who did the internal education of employees? Their co-CEO Reed Hastings. That’s what I call a senior sponsorship for data! (Netflix Technology blog)

I’m usually getting a lot of reading inspiration from LinkedIn. With the crazy and unnecessary war the content has obviously changed. But it’s just a minor discomfort compared to the millions of people affected by the war. Let’s hope it will be over soon!

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

Thanks for reading!

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Adam Votava

Data scientist | avid cyclist | amateur pianist (I'm sharing my personal opinion and experience, which should not to be considered professional advice)