At the Insights Association's NEXT conference today, a common theme involved debunking myths.

Andrea Jones-Rooy of New York University debunked some myths about data scientists.

  1. All data scientists are young, and all young people are data scientists. Age, of course, does not imply skills, and many older professionals have switched careers into data science.
  2. All early career people want the same thing. No, not all young people want the same things out of their careers and of course the pandemic is affecting some differently than others.
  3. “Data scientist” means they can do anything related to computers. Data scientists aren’t IT staff, webmasters, or social media experts.
  4. If a data scientist is saying something that doesn’t make sense, it just means you don’t know enough. “This is part of the process with any kind of cognitive diversity! Part of all of our jobs is to communicate what we are doing and why. Technologists are no exception. There is no shame in not understanding.” She pointed out that people would be more willing to confess not knowing something a chemist said than something a data scientist said, perhaps from a mistaken belief that we should know more about data science than we do.
  5. All data scientists are loners who want to work in isolation. “Many want to collaborate with experts; they seek and enjoy teamwork!”

In a later session, Roddy Knowles of Alpha debunked some myths about agile processes.

  1. Embracing test and learn requires a large amount of change. “The fear is that you have to get stakeholders aligned, get executive buy-in, organize meetings and develop plans.” In actuality, getting started is not difficult: just run a project. Doing so is the first step to creating “a culture of exploration, encouraging creativity and curiosity and testing.” As he said later, “Companies that are nimble and innovative focus on getting something out quickly, a simple prototype.”
  2. Test and learn only applies to software, product, and innovation teams. Think of it instead for anything that you want to learn more about. Emphasize execution over planning (you can’t plan for what you don’t know yet) and taking small bets “to de-risk the bigger bets.” Think of test and learn as risk reduction. “Start by thinking about test and learn not as a process or methodology but as a daily habit.”

If you haven’t consider either yet, how about starting with an agile, data-science project?