Keeping Up With Data — Week 21 Reading List

5 minutes for 5 hours’ worth of reading

Adam Votava
3 min readMay 28, 2021

Many years ago, I asked myself ‘how do sales people read dashboards’ — in order to design new dashboards around their subconscious behaviours. And when I didn’t find the answers, I asked experts from leading BI platforms. They were confused by my questions and responded with something along the lines of “you can build whatever you want; it depends on what you want”. Which isn’t exactly what I was looking for either. So, I changed tack and asked sales people ‘what are the questions you need answers to?’ This time I got so many answers I felt like I was hit by a shotgun.

I’m so happy that today’s dashboards are telling stories. They are designed around people’s behaviours — and psychology is regularly used in the process. Also, that we can use insights, like the ones shared in the article by Maw Ferrari and don’t have to re-invent the wheel every time.

‘Not reinventing the wheel’ is this week’s topic — three articles sharing lessons learnt about CDOs, scaling DS and smoothing demand variability.

  • Nasty, brutish and short: The life of the modern CDO: Fantastic article about what does the CDO role entail, by a real-life CDO. What areas should a CDO cover and what is the importance of “acting as the ‘glue’ between the business and the data team”? What happens if you hire a CDO “to figure out what we should be doing with data”? And what if you overwhelm them with “business as usual”? The role is still new and many organisations (and also many CDOs) are building the plane while flying it. This article can be used as an ‘airborne early warning and control system’ by many of us. (LinkedIn)
  • Full Cycle Data Science (FCDS): Data scientist should have business impact. Having them spend time “where they don’t bring value, is the worst enemy of scaling a data-driven organisation”. A single data scientist, equipped with the right technology and supported by the right culture should be able to solve a problem single-handedly — end-to-end. Yes, including data cleaning! Removing the complexities of coordination between multiple people or teams increases the efficiency of a data science function. This approach favours data science generalists and has a strong pre-requisite: technology — so that a single person (not a super-hero) can cover the whole process; and culture — where they are empowered and inspired to do so. (Daniel Marcous @ TDS)
  • The Right Way to Mix and Match Your Customers: “The costs of demand variability can put you out of business.” Companies are aware of this and are therefore often looking for customers with stable demand. But that doesn’t necessarily make things better. It is important to value customers not just by absolute sales but by the variance too. And it shouldn’t be too strongly positively correlated to the demand of your other customers. So, just like a portfolio approach helps lower variance in investment or risk management, the same can be applied in supply chain. And because it is a “chain”, don’t only look at the customers but be mindful that you are someone’s customer too. (MIT Sloan)

Alas, my week was pretty ‘nasty, brutish and short’. But tomorrow I’m taking (or dragging??) the family to see Giro d’Italia as it visits Switzerland. Maybe we’ll catch a bidon since it’s possible again! Fingers crossed, and it will be one from Jan Hirt during his winning breakaway move 🚴‍♂️🇨🇿🤞

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)