The Data Supply Chain: Maximizing Value Throughout the Cycle
summary
Defines a concrete data supply chain: question→source→prep→analysis→presentation→insight→close loop and argues analysts must own every step, not just extraction and modeling. Recommends Hadoop as a back-end scale solution while keeping existing front-end tools (Power BI, Tableau, Qlik) unchanged, warns 99% of big data is crap, and stresses logging, governance, and postmortems with stakeholders. Shows one merchant-services case that moves to Hadoop successfully and emphasizes structuring self-service for user skill levels to make ROI real.
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chapters (12)
- 0:00Data supply chain intro
- 3:36Lifecycle of data supply chain
- 6:44Data warehouse limitations
- 10:40Unstructured data handling
- 15:40Corporate data hierarchy
- 21:43Managing data sources
- 26:16Scaling data infrastructure case study
- 28:21Analytics preparation demands
- 31:55Visualization and storytelling
- 35:43Closing data outcomes loop
- 37:53Challenges and ownership
- 48:23Q&A
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