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Data scientists are a precise group.
They aren’t a magic wand that cut through any piece of data, but a specific skill-set that, when utilized, yield impactful insights. It’s your job as a data science manager to equip your team to do the job.
In this post we are deep-diving on Product Knowledge, the first of three areas data scientists become blocked.
What is Product Knowledge?
Product Knowledge is knowing the functionality and language of your product.
It’s the product intuition you build through actual use.
Generally, teams do a so-so job at this. It’s not top of mind for managers and usually relegated to training through osmosis (aka “...they’ll pick it up on the job”).
To train for Product Knowledge, data scientist managers should 1) require formal product training and 2) seek product usage.
1) Formal Training
Goal: Put your data team through the hard training.
What trainings do the certified power users go through? The admins? The sales team?
It’s not overkill for non-customer facing groups. I’ve implemented this as a necessity for my teams going forward.
2) Using the product
This one may seem obvious, but ask yourself (and your team): how much you use the product you’re analyzing?
Do you know your product’s work arounds and shortcuts?
Do you know which things are “annoying” in your own product?
If you could add any new feature, what would it be?
Is there a certification for your product, do you have it?
How would you get around your paywall?
If you’re successful, your data scientists will speak the language of your product. This holistic product knowledge (vs siloed) allows them to connect disparate ideas for analysis (a soft skill that's hard to train for).
This adds up to the ultimate goal: inspiration for insights that impact. You know, what you paid them to do.