Keeping Up With Data #61

Source: Wilson Wong @ The Startup

There is plenty to read about data this time of year. Trend prognoses for 2022 are being published like mushrooms after rain. Data industry is incredibly diverse and so are the trends for the year to come. One can also spend time going back and reflect on the trends for 2021 or sooner.

Though I’ve read many of the ‘trend’ articles, I’m not including any in the list below.

  • Why is “Data Scientist” such a controversial title? The article is a rebuttal to one of the talks at the Coalesce conference claiming that “data scientist” is a bad job title, because data teams do more than just experiment. This article defines science as a journey of learning. And that can be incredibly useful even in business context. However, companies haven’t yet learned how to fully leverage it. But because not every business problem we might want to solve with data and analytics has a known solution, having a data scientist — who is continuously learning and experimenting with possible solutions — in the team is important. If you ask me, data scientist is a great job title. And it is here to stay. (The Data Leader’s Survival Guide)
  • Unrepresentative big surveys significantly overestimated US vaccine uptake: Having data that are reflective of the problem at hand is superior to having large volumes of data. Case in point? This article shows how and why an online panel with 1,000 respondents per week can be more accurate than a large survey with 250,000 responses each week. The main point is that following survey research best practices provides reliable estimates and uncertainty quantification. I’d add that in my opinion asking “How representative of the real world the data is?” is the most important data question. (Nature)
  • The Most Important Role You’re Not Hiring for Your Data Team: The Information Architect: Information architecture is “the practice of deciding how to arrange the parts of something to be understandable.” While it has been associated mainly with websites, it is pertinent for data too. Organising dashboards to make it easy for users to navigate, find the answers (and make a decision) is indeed a very handy skill. An example might be a KPI dashboard for an executive. What is their goal? Their primary question? How to help them find the answer? “Selecting better data encodings, color palettes, and even improving other visual details, will only get you so far in improving the data experience for your users. You’ll be able to make much more impactful changes by focusing on the information architecture first.” (Lilach Manheim @ Nightingale)

Happy holidays!

In case you missed the last week’s issue of Keeping up with data

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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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