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Data like this allows us to be extremely customer focused and help direct investment for the business, such as what features we build on and when. We also use
by lawrjone 3y ago
Data like this allows us to be extremely customer focused and help direct investment for the business, such as what features we build on and when.
We also use the same data pipeline to power a lot of our data product features which customers pay us for.
So it’s extremely worthwhile as an investment for us. It’s also why we have about five people hired into data adjacent roles, as it’s so key to us running the business correctly.
- jimberlage 3y agoOut of curiosity, would running DBT that outputs to a reporting schema on a Postgres read replica work? Or as a startup do you already have too much data for that?
- lawrjone 3y agoIt would work for most of our business reporting, but BigQuery is so good at what it does and cheap that it doesn't make much sense to avoid it. We also have some parts of our dataset that are complicated to compute. An example is that we estimate the hours that people have spent working on incidents, with a method that we describe here: https://incident.io/guide/insights/workload https://incident.io/guide/insights/workload That requires us to do some expensive cross-joining of every action ever taken in an incident from messages sent to the channel to GitHub PRs being merged. We could make this incremental and optimise it for performance but using BigQuery by default means we don't need to worry yet and can leave the optimisations for when we're bigger and the engineering resource we'd dedicate wouldn't detract as much from customer-focused work.