Data & Analytics

Data & AI Value Chain

Data & AI Value Chain is a case interview framework for data platform, analytics, ai/genai. Use for data platform, BI, and AI cases to map end-to-end value creation

When to use it

Use for data platform, BI, and AI cases to map end-to-end value creation

Where it fits

Best for data modernization, analytics platforms, and AI deployment cases

How to use it in a case

Work through value chain linearly: Business use case → data sources → ingestion → storage → transformation → governance → analytics/AI consumption → adoption → measurable value. For each stage, identify capabilities needed and gaps.

  1. Business Use Case. Define what business problem the data solves
  2. Data Sources. Identify internal and external data needed
  3. Ingestion. Collect data from sources reliably and at scale
  4. Storage. Store data in structured, scalable way (data lake, warehouse)
  5. Transformation. Clean, enrich, and prepare data for consumption
  6. Governance. Manage data quality, lineage, privacy, security, compliance
  7. Consumption. Build analytics, dashboards, models, and AI applications
  8. Adoption. Drive user adoption, change management, training
  9. Value Realization. Measure ROI, business impact, and iterate

Case types

Data Platform, Analytics, AI/GenAI.

Source: Industry best practice.

Related frameworks