A confusing aspect of DS work is that your input is data and your output is... often also data
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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.
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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
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But sometimes a summary isn't detailed enough. Maybe you need to filter for certain subsets of your user base, or chain their actions into a known user journey, or look at their actions over specific windows of time... This is when you need metrics.
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Metrics are where business logic lives. They're a way of formally encoding the relevant dimensions of a business problem you've decided to solve, and if you want to have an extensible, modular system for doing analysis, you will want to decouple business logic from summaries
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Once a summary of what happened in a system is generated, it doesn't change unless something happens to the underlying records the summaries are based on. Business logic, on the other hand, constantly evolves
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Company strategies are tweaked as the market evolves, users react and adapt their behavior to new product features, once-important projects get deprioritized... All of these things can impact the usefulness of your metrics
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Thanks to the modularity granted by separating summaries from metrics, it's much easier to change the business-logic-enforcing code to calculate your metrics. It's also a lot easier backfill historical values for new metric definitions, or two maintain a v1 and a v2 of a metric
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Creating useful metrics is a huge problem in its own right, but a design like this reduces some of the overhead (and potential tech debt) involved
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End of conversation
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