Read on Twitter
Been jammin' with @grantwinship about what separates great data teams. This is where we landed.
Great data teams realize they are in control of entity definitions.
What does this mean?
Great data teams realize they are in control of entity definitions.
What does this mean?
If I ask: "What does a record in the "customers" model represent?"
Most data teams: "We pull that data from our app"
Great data teams: "Someone who has purchased a product — they might come from our online store (our own app), or our retail locations (a POS system)"
Most data teams: "We pull that data from our app"
Great data teams: "Someone who has purchased a product — they might come from our online store (our own app), or our retail locations (a POS system)"
Or: "What does a record in the "users" model represent?"
Most data teams: "That's from our users table"
Great data teams: "It's our best guess of one 'real life' person. They don't need to have an account — we included anonymous users in this table."
Most data teams: "That's from our users table"
Great data teams: "It's our best guess of one 'real life' person. They don't need to have an account — we included anonymous users in this table."
Most data teams: group reports / dashboards / analyses by data source — stripe, segment, zendesk
Great data teams: group reports / dashboards / analyses by business area — marketing, product, finance, support
Great data teams: group reports / dashboards / analyses by business area — marketing, product, finance, support
How do we know this?
In my first data role, I let my source data define my data models — I made these mistakes myself!
We also see lots of companies we work with do the same thing. It feels like a subtle difference, but ends up being a huge paradigm shift.
In my first data role, I let my source data define my data models — I made these mistakes myself!
We also see lots of companies we work with do the same thing. It feels like a subtle difference, but ends up being a huge paradigm shift.
