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This is so great! You see exactly what we see and clearly you have shared similar experiences with dashboards not matching because of wrong table. (The good old "spent 3 weeks debugging an analysis using sales_data and then finally found that sales_data_v2 was built to solve it).

Yeah we do something very similar to dbt for taking restructuring the data into a single time-series table. We add things like identity resolution, diffing, incremental update and computing some cache columns.

Your Crystal Ball is SPOT ON!!! We get 3 kinds of data people. The ones who are like: "THIS WILL NEVER WORK", "Too bad I already built all this" or the "THIS IS THE FUTURE, HOW IS EVERYONE NOT USING IT".

I would love to chat and show you what we have (schedule a demo on our site and it will go to me and we can chat!)

Also, Teaser... When you standardize all of data and you create a consistent way to relating that standardized structure then analysis become very consistent. Imagine a world where your email attribution deep dive can be run by loading a template and point it to your "opened email" activity and your "order activity".... coming soon ... a Narrative Library.



> restructuring the data into a single time-series table. We add things like identity resolution, diffing, incremental update

So is this where the customer still has to do some work? Defining states and transforming their sources into a series of events with these states?


Yes, the customer would have to define their activities (e.g. 'page view', 'completed order', 'support ticket opened') and write sql snippets to define them.

https://docs.narrator.ai/docs/activity-transformations describes these scripts and links to a few examples


scheduled!




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