Keeping Up With Data — Week 40 Reading List

Source: https://medium.com/data-visualization-weekly/four-impressive-visual-data-stories-to-check-out-dataviz-weekly-697402eec7a1 by AnyChart

Fever hit me on Monday and my plans for the week got shattered like being punched in the mouth by Mike Tyson. Though I was ok the next morning, I haven’t been able to get into flow since. And I’ll be honest, I haven’t been very proactive in searching for insightful data articles. But it wasn’t needed this time. I just had to open a couple of links my friends and colleagues have shared with me. [Thanks!!!]

Here are the top articles of the week that made in into the 51st edition of #KeepingUpWithData!

  • Why Do Chief Data Officers Have Such Short Tenures? An article by Tom Davenport et al. brings great advice from P&G CDO Guy Peri: “1) Start with a clear connection to business strategy with tangible examples of how data analytics can drive business outcomes (topline, bottom line, cash, stewardship), and 2) lead with 1–2 forward thinking business partners to demonstrate what is possible. Those partners become the change agents across the organization.” I concur. (Tom Davenport)
  • On Building Effective Data Science Teams: In order to ‘demonstrate what is possible’ a data science team is often needed. What does it entail to make the team effective? In a nutshell, “keep [the data science] strategy simple to begin with, hire the right people at the right time, leverage the knowledge gathered from previous incarnations of the field, and develop a process that works best for your team and goals.” Hard to disagree at that level, right? But it — once again — shows that many things need to work in concert for a data science team to be effective. Plus there are some useful tips for data science team structure and project management in the article making it more practical. (Saurav Dhungana @ CraftData Labs)
  • Why Do Strategy, Anyway? Though not directly about data, there are takeaways for data strategy. Not having a strategy because world is just too complex to plan is not a good excuse. Not doing anything, not making any choices and waiting with a ‘fast follower’ attitude to be applied when “we have the data / systems are ready / basic processes are solved” is also a (data) strategy — because “strategy is what you do, not what you say”. Just not a very smart one in my opinion. The answer is in iteratively hypothesising, applying, testing, evaluating, and then hypothesising again. Because, as we read in the article, strategy is about learning. Continuous, iterative learning. (Roger Martin @ Medium)

One other link I received let to an article from which the image above is taken. It brings four incredible examples of visual data stories. The ones about sexual violence in Singapore and harvesting ice cores from melting glaciers are a piece of art. Fitting form for these significant topics.

Thanks for reading!

Please feel free to share your thoughts or reading tips in the comments.

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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