Future of the Metrics Layer with Drew Banin (dbt) and Nick Handel (Transform)

Hot takes on what we get wrong about the metrics layer and where it fits in the modern data stack

Define your metrics once and reference them everywhere so that if your metrics ever change, you get updated results everywhere you look at data.”

“It comes down to those two things: productivity and trust. Is it easy to produce the metric, and is it the right metric? And can you put it into whatever application you’re trying to serve?”

“We’re trying to make sure that when someone says ‘weekly active accounts’ or ‘MRR’ or ‘MRR split by manage versus self-service’, we all mean exactly the same thing.”

“That’s why the idea that we call this the ‘metrics layer’ makes sense. It is a single abstraction layer that everything can interface with so that you can get precise and consistent definitions in every single tool.

“Products change, tables change, everything changes. Even the definitions of these metrics evolve. But most businesses end up tracking the same North Star metrics from the very early days. If you can attach metadata to it, that is incredibly valuable.

“In practice, the people that have been around for the longest time have the most context and probably know more than any of our actual systems do.

“Many companies out there are not at the data science and machine learning part of their journeys yet. Things that make business intelligence and reporting better (more precise and more consistent) cover 90% of the problems that they’re trying to solve with data.

“With machine learning, you try and get as close to the raw data sets as possible. With analytical applications, you try and process that information into the clearest and best picture of the world.

“Basically, we needed some programmatic way to go and construct metrics. It’s a hugely valuable application for companies that do it, but very few companies have the infrastructure or build the tooling to do this. I think that that’s really unfortunate. And it’s probably the thing that I’m most excited about the metrics layer.

“The word ‘layer’ makes sense only insofar as it’s a layer of abstraction. But otherwise, I reject the terminology, although I can’t think of anything too much better than that.

“Basically, people have problems, and companies build technologies to solve problems. If people have problems and there is a valuable technology to build, then I think it’s worth taking a shot and trying to build that technology and voicing those opinions.

“Everyone wants features to be specific to their model. Nobody wants metrics to be specific to their team or their consumption. People want metrics to be consistent. People want features to be unique and whatever benefits their model.

“The difference between something taking a minute plus to come back and not coming back at all is negligible in a lot of cases. So, conceptually, I’m very aligned with the idea of caching metric data and being able to serve it up really quickly.

“There are a lot of problems in data that you can solve with technology, but some of the hardest and most important ones you must solve with conversations and people and alignment and sometimes whiteboards. Knowing which kind of problem you’re trying to solve at any given time is going to help you pick the right kind of solution.”

“I think the metrics layer is basically a semantic layer with an additional concept of a metric, which is super important. So I would just say, the metrics layer should be backed by a general-purpose semantic layer. The spec and the definition of that semantic layer and the abstractions is so unbelievably important.”



Co-founder of Atlan (atlan.com), the active metadata platform for modern data teams | Weekly newsletter for data leaders: metadataweekly.substack.com

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Co-founder of Atlan (atlan.com), the active metadata platform for modern data teams | Weekly newsletter for data leaders: metadataweekly.substack.com