{ Draft }

{ Pieces of the Puzzle:

Changing the Tools

The goal: to improve the Learnability and UX of the tools we use, not only for beginners but for each step along the continuum from beginners to power users to “blue-collar coders” to analytics engineers and data scientists.

Tool makers: improve the overall UX of their tool

Organizations, and communities of organizations: build scaffolding (?) around tools to make them easier to use. Example:

  • Instead of trying to improve the overall UX of the pandas data science library, an organization might create a simple, lightweight library on top of pandas that makes it much easier to use pandas to address a few specific user or department needs, plus some template Jupyter notebooks and cheat sheets that target those needs.

Tool makers, organizations, communities of organizations: create:

  • Curated Code recipes
    • Tool makers: consider modifying IDE’s/tools so easy to navigate “cookbooks”
  • Better documentation, cheat sheets, etc., organized to make it easier to understand how you move up the learning curve

Soft Skills (need better name)

  • Helping an Organization Become More Data-Driven
  • Improving the UX of Data
  • Making Trade-Offs / Being Pragmatic
  • Data Cleanup Vs Analysis
  • Developer’s Tools: Version Control, Etc.

Bigger Steps

  • Coding UX Research
  • You Don’t Need to Be a Chemist To Bake Good Cookies: focusing more on what mental models people need to develop as they work their way up the learning curve
  • Why do __I__need to build a model of Shopify? Figuring out better ways of sharing higher level / more abstracted work
  • Data Modeling ala Jarvis (tool building scene in first Ironman)
  • Recipes and Visual – why should I have to remember crap? (Instead of constantly Googling)

Not Sure Where to Put

  • The problem of self-selection bias
    • “Posttraumatic Locker Syndrome”
  • Everyone a tool maker – e.g., encouraging people to create views earlier as well as becoming a more sophisticated consumer of views
  • Smoothing the learning curve helps folks one step above – e.g., data scientists are now more likely to produce useful products

NOTE: the idea of Smoothing the Learning Curve comes from research by Makers All, the parent organization for Data Chefs

Create a table? Tools, Mental Model, Infrastructure }