Katie Bauer!

A confusing aspect of DS work is that your input is data and your output is... often also data

But not all types of data are the same. I've been trying to think of a way of explaining it to non-DS, and the mental model I'm currently working with has three main layers: records, summaries and metrics

Records are the foundation. They're extremely literal data--event logs of prod systems, snapshots of user states and server configurations, etc. Data scientists may use them in their work and advise on what records are needed, they aren't directly responsible for creating them

You CAN do all your analysis by querying records but it's not very efficient, particularly as the size of your dataset grows. This is where summaries come in.

Summaries get called a lot of things (facts, aggregates, rollups) and they're also pretty literal. They're often counts of particular types of records, used to precompute the results common queries and ensure the relevant slice dimensions and join keys are available

A well-curated set of summaries gives you a lot of analytical power. You've got a lot of computational heavy lifting done, and you can dynamically combine different summaries to answer a lot of business questions.

You can even use them to cheaply interrogate your assumptions about a system. Think about how often you expect a user to take action X on a given day, then use your summaries to see if it's higher or lower.

If the number you find doesn't match your expectations--congrats, you may have just found a user problem and have a new data-driven roadmap item that you can brag to your friends about