Keeping Up With Data — Week 30 Reading List

Source: https://towardsdatascience.com/9-distance-measures-in-data-science-918109d069fa

Digital transformation is (or is about to be) under way in many organisations. The main thrust is often targeting advanced analytics and AI use cases, technology, data governance and the likes. One element that is getting more attention lately is the ability to make data-driven decisions. However importantly, only 17% of companies are significantly encouraging employees to become more comfortable with data.

What made me select the following three articles for this week? First, is a great story; second, comes with a cool infographic; and the third one is about one of my favourite topics.

  • Building a data team at a mid-stage startup: a short story: Building a successful data team is easy. At least in theory. The sheer complexity of the job makes it nearly impossible in the reality. The daily realities bring so many challenges and exceptions to the principles you set to follow. Every day there is at least one moment when you fall off balance. Data leaders need to have business acumen, people skills and empathy, technical capabilities, but most importantly — resilience. (Erik Bernhardsson)
  • The Big Loop: artificial intelligence and machine learning: The continuous improvement paradigm holds for autonomous driving too. Just as the drivers are continuously learning and developing some kind of intuition, the AI models driving cars should too. And that’s what they are doing at Porsche using a Big Data Loop. The solution is demonstrated to work for early lane change detection, but in fact, the schema is easily applicable to many use cases. It demonstrates that rather than finding a solution to a difficult problem, it is better to create a system that keeps continuously improving a solution step-by-step. (Porsche)
  • Reimagining Experimentation Analysis at Netflix: All our biases are against us when we want to make a decision. And even when we decide to make a data-driven decision, it has to come with low barriers. Any obstacles, any complications make us question if an A/B test is worth the effort and we can easily change our mind and go with the gut feel (or HiPPO’s gut feel). The engineers at Netflix have built a platform that makes the experimentation analysis easy. Netflix is known for running on A/B testing culture. But now they are showing that it also needs to be accompanied with efforts to lower the barriers to technically do that. (The Netflix Tech Blog)

A lot of driving ahead of me in the upcoming days and checking the google maps I though it’s funny how the (long-distance) routes are sometimes closer to a Manhattan distance between the departure and the destination rather than Euclidean. Either way, that’s the reason behind using the caption image above.

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 DataDiligence.com

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Adam Votava

Adam Votava

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

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