Data as a Product: Transforming Analytics for Business Success
summary
Claim: treat domain datasets as versioned outputs so four source data products (orders, products, clickstream, product-info) feed aggregated analytics products that deliver product recommendation models (historical or ML), APIs, SQL endpoints or streams. Implementation details: prefer columnar formats Parquet/Avro/CSV, provide starter kits, enforce observability (freshness alerts, monitors) and governance (least-privilege, GDPR). Outcome: deployed data products shorten time-to-market, surface conversion and CLV KPIs, trigger actions that generate new data and uncover hidden opportunities via the cycle of intelligence.
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chapters (9)
- 0:00Introduction to data products
- 1:37Data product characteristics
- 3:16E-commerce data use cases
- 5:46Overcoming data silos
- 8:58Practical implementation
- 12:54Key pillars of development
- 15:31Organizational benefits
- 16:58Lessons learned
- 18:53Analytics maturity model
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