Help Me, Help You: Supporting Your Data
summary
Framework for turning analysis into revenue: collect time series and historic state early, build pipelines and a source‑of‑truth data model that cuts development time in half, and provision a secure scalable dev platform (example: SageMaker for notebooks, Fivetran for syncs, APIs and web scrapers for sources like Salesforce/Shopify). Expect data scientists to spend ~80% on prep, model training can take tens of hours on weak hardware, andOperationalize with AIM adoption implementation maintenance and measure impact tied to dollars.
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chapters (7)
- 0:00Intro
- 0:54Your Tour Guide Kisa Brostrom - VP of Data at boodleAl
- 5:55Come Along on the Journey - Data Project Lifecycle
- 10:52Collect the Right Data
- 16:41Silo to Warehouse
- 24:04Setting Up Your Team for Success
- 31:34Don't Forget to A.I.M.
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