The dubious history of Ernst Haeckel and debunking of his biogenetic law aside, I still love the catchphrase he generated--"ontogeny recapitulates phylogeny." The embryo does in certain instances, reflect evolutionary history--pharyngeal gill slits anyone? But it isn't as reminiscent of Lamarckian ideology as our modern day understanding of the complexities of genetics reveal.
I recently heard the phrase attributed to nascent data teams. Think of the pluripotent stem cell for example. Many a data project begins with a small nimble team with an undifferentiated skill set. Angela Bassa, Director of Data Science at iRobot, shares that one of the earliest paths to "value" is by answering easy data queries with an architecture that takes care of itself.
Over time, a data team evolves and grows the ability to mature into anything. This leads to the specialization and differentiation of mature data teams. This sounds tremendously like how data projects evolve in the wild. When the journey is new, you may find yourself dabbling in a bit of the data engineering and data architecture bits regardless of focused expertise in formulating data questions.
The truth of the matter is, everyone has data. Data scientists worth their salt have the skill of formulating a data question and selecting the right statistical and visualization tools to formulate a solution. We strive for replicability, interpretability, and fit for purpose.
Full disclosure. When I am launching new data strategies with clients I focus on team building. One of the best pieces advice, I "borrowed" from Amazon CEO Jeff Bezos.
Perhaps you have heard of the 2 pizza rule.
"It's simple. The more people you pack into the meeting, the less productive the meeting will likely be. The solution?
Never have a meeting where two pizzas couldn't feed the entire group."--Business Insider
If you scour adds for data scientists, what you may notice is a focus on tools rather than asking if you have the imagination to envision what an answer to a data question might look like. In my opinion, the method should be second to the actual problem a team seeks to answer. It reminds me of a data professional that works within a large hospital system in my neighborhood. There are so many legacy systems in his department, they can barely communicate. The seasoned IT team has preferred ways of computing while the newer team members are more likely to be comfortable with the latest and greatest.
How many pizzas do you think it would take to feed those folks?
Listen to a recent post about managing data science teams--this post is a bit of a summary--the episode is worth your ears...
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In a world of "evidence-based" medicine I am a bigger fan of practice-based evidence.
Remember the quote by Upton Sinclair...
“It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”