Keeping Up With Data #84
Because data can be used as a reflection of the real world and analytics to solve real-world problems, the variety a data professional can have in their work life is amazing. From personalisation in marketing, to soccer analytics, to designing and managing data ecosystems. And these are just some of the challenges I’ve encountered this week!
And as it is often the case, the weekly reading list reflects that. Reading about a topic is always one of the first steps in my problem solving process.
- Forget personalisation, it’s impossible and it doesn’t work: Great myth-busting article against personalised marketing — the mantra of the recent decade. The authors’ case against personalisation has two arguments: (1) Personalisation cannot be done, because third-party data is extremely unreliable; and (2) personalisation is not desirable, because marketing works by reaching everybody with the same message to create shared associations. An article that talks straight to the importance of data representativeness, quality and reliability, as well as making a case against blindly using machine learning models to solve the wrong problems. (MarketingWeek)
- Possession Is The Puzzle Of Soccer Analytics. These Models Are Trying To Solve It. I’m obsessed with the notion of data reflecting the real world. Soccer analytics is a nice example. There are many statistics to look at and it’s tempting to throw them all in a game prediction model (like I did five years ago) and hope for the best. But hope is never a good strategy, and one should first think about how to reflect a game with data first. Just like Sarah Rudd did ten years ago. Her possession value concept has led to a great advancement in answering the holly grail of soccer analytics: ‘How do we model the state of the game at any time, and what can we get from that about future reward?’ (FiveThirtyEight)
- A path towards a data platform that aligns data, value, and people: A refreshingly new approach to viewing data ecosystems. Everyone is talking about treating data as a product. But then business teams are confronted with technology-first views on what data is and where ‘their’ data products live. Petr is suggesting flipping the axes and showing data flows as business processes and fading technology layers to the background. He argues that it will help reduce complexity of data ecosystems and align data, value, and people. Wouldn’t that be great? (petr@substack)
Giro d’Italia will conclude this weekend. Will Jan Hirt add another stage win tomorrow?