Lesson 9 — Implementation, Governance, Privacy, and Metrics
Big Data is still data. Velocity is not permission to skip controls.
Readiness before scale
Assess: - business relevance; - business readiness to use results; - economic viability and ownership cost; - prototype evidence; - procurement/platform options; - skills/resourcing/talent.
A model can be accurate and useful yet still fail operational readiness if nobody can run the platform or change the business process.
Roles highlighted in Chapter 14
- Big Data Platform Architect: infrastructure, OS, filesystems, services.
- Ingestion Architect: source analysis, mappings, source-to-platform support.
- Metadata Specialist: Metadata interfaces, architecture, content.
- Analytic Design Lead: end-user analytical design and result communication.
- Data Scientist: statistical/model expertise and technical application to requirements.
Governance ring
Governance decisions surround the whole lifecycle: - sourcing; - sharing/contracts; - Metadata; - enrichment; - access/publication; - security/privacy; - Data Quality; - visualization standards.
Recombination risk
Separate sources can appear safe yet together reveal sensitive or identifiable information. Narrow model outputs/visualizations can create the same problem for small groups.
Metrics: activity is not value
Track technical usage, loading/scanning, service/query health and tangible learning/value such as: - useful models or validated patterns; - revenue opportunities; - reduced costs; - avoided threats; - new business initiatives; - adoption and decision change.
A technically busy Data Science program can still fail if benefits do not justify development and operating cost.
Final chapter rule
A strong Chapter 14 answer keeps the full chain intact: business purpose → trustworthy/contextualized source → quality/alignment → sound model evidence → appropriate communication/architecture → governed deployment → monitored value.
Source: pp. 495–501.