Keeping Up With Data #102

5 minutes for 5 hours’ worth of reading

Adam Votava
3 min readSep 30, 2022

We see a lot of images depicting the levels of analytics maturity — like the one above.

These are often variations on The Gartner Analytics Continuum. Typically with an extra stage or helpful explanation added (such as the hindsight/insight/foresight labels this time).

They also almost always come with a warning that one cannot jump ahead, skip the initial stages and explore the benefits of the most advanced maturity levels. But seeing the y-axis, many executives can’t resist.

In my experience, developing the analytic solutions from the top-right corner is not much more difficult than developing those from the bottom-left. But having the organisation adopting them is vastly different!

What I’ve learned is that when you are building data-powered solutions for people — sales reps, restaurant managers, facility managers, marketing managers, or executives — always start patiently from the left.

It is one of the ‘go slow to go fast’ situations.

This week’s reading list looks at problem solving skills, operating model for data and analytics, and lessons from 20 years since Moneyball.

  • Forget about algorithms and models — Learn how to solve problems first: This is yet another reminder about the importance of problem solving skills in data science. I always say that data science is about solving business problems with data and analytics. I also like to believe that my studies of general mathematics spent trying to understand complicated theorems and studying their proofs full of rigid chains of implications prepared me well for a data science career. Five problem solving steps outlined in the article are: understand the problem, breaking it down, starting with an example, executing, and reflecting. After over a decade spent by solving business problems with data and analytics, I do very much agree. (Ari Joury, PhD @ TDS)
  • An Operating Model for Data & Analytics: “An operating model for data & analytics is critical for aligning resources across the enterprise and balancing the needs for agility and governance.” And it is the role of chief data officer to nurture alignment between business, technology, and hybrid teams. From hard-core IT, through enterprise data, domain-based teams, all the way to senior business stakeholders. In the end, CDO’s role is not to compete with others, but to deliver business value through data and analytics. And aligning people is key to that. (Eckerson Group)
  • Moneyball 20 Years Later: A Progress Report On Data And Analytics In Professional Sports: Last twenty years have seen incredible growth of data and analytics in baseball and other sports. The amount of data grew exponentially, the analytical models got more sophisticated, and teams have started employing data scientists on a regular basis. And just like it is the case in business, even in sport is data analytics experiencing headwinds and degrees of resistance. Bringing data into sports (and business) is not just an add-on. It requires the teams to start thinking differently. And that doesn’t happen effortlessly overnight. (Forbes)

I’m listening to Don Shirley’s song The Warning as I’m finishing this blog. What a great way to end the week.

Enjoy the weekend!

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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Adam Votava

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