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Applied Curriculum — Chapters 9–17

Chapter 9 — Document and Content Management

Applied objective: Manage synthetic contracts, policies, invoices, product documents, and service content; assign metadata and controlled vocabulary; design retention/disposition rules; simulate an e-discovery request.

  • Primary lab mode: Content lifecycle / retention
  • Produces: content inventory, metadata profile, retention schedule, discovery response
  • Technology / medium: Drive/Docs + metadata registers; no unnecessary ECM platform
  • DMBOK gate: read and blueprint Chapter 9 first

Chapter 10 — Reference and Master Data

Applied objective: Reconcile three conflicting customer sources; define match and survivorship rules; produce a trusted customer view; separate master entities from controlled reference lists; manage code changes/hierarchies.

  • Primary lab mode: Match / master / control
  • Produces: match results, survivorship decisions, master table/view, reference tables
  • Technology / medium: SQL + PostgreSQL + Python/pandas
  • DMBOK gate: read and blueprint Chapter 10 first
  • Cross-chapter potential: strong interleaving chapter

Chapter 11 — Data Warehousing and Business Intelligence

Applied objective: Design a small star schema; load facts/dimensions from operational data; calculate business measures; build a dashboard; demonstrate why operational and analytical structures serve different purposes.

  • Primary lab mode: Operational → analytical
  • Produces: star schema, load SQL, KPI definitions, dashboard
  • Technology / medium: PostgreSQL + SQL + Metabase
  • DMBOK gate: read and blueprint Chapter 11 first
  • Boundary: analytics as a Data Management application, not analytics engineering specialization

Chapter 12 — Metadata Management

Applied objective: Catalog the lab assets already built: business definitions, technical metadata, owners/stewards, classifications, lineage, and source/target context. Distinguish glossary, dictionary, and catalog using real Meridian assets.

  • Primary lab mode: Catalog / define / trace
  • Produces: glossary, data dictionary, catalog register, lineage maps
  • Technology / medium: Sheets/Docs first; later optional OpenMetadata
  • DMBOK gate: read and blueprint Chapter 12 first
  • Career-platform bridge: after manual concepts are understood, serious Purview Data Governance practice can begin through the technology roadmap

Chapter 13 — Data Quality Management

Applied objective: Inject duplicates, nulls, invalid references, stale values, impossible quantities, and inconsistent codes. Profile; define quality rules/dimensions; measure; find root causes; repair; add preventive checks; compare before/after.

  • Primary lab mode: Profile / break / repair / prevent
  • Produces: DQ rulebook, profiling results, issue log, remediation SQL/Python, scorecard
  • Technology / medium: SQL + Python/pandas + PostgreSQL; optional OpenMetadata later
  • DMBOK gate: read and blueprint Chapter 13 first
  • Career-platform bridge: add Data Quality concepts to Purview/platform practice only after the chapter foundation exists

Chapter 14 — Big Data and Data Science

Applied objective: Generate a larger click/event dataset in JSON/Parquet; query it with DuckDB/Python; compare management characteristics with relational transactional data; focus on recognition and governance implications, not ML training.

  • Primary lab mode: Scale / format awareness
  • Produces: event dataset, format comparison, query notebook, management note
  • Technology / medium: DuckDB + Python/pandas; concise by design
  • DMBOK gate: read and blueprint Chapter 14 first
  • Boundary: no ML bootcamp, Spark specialization, or Data Science career drift

Chapter 15 — Data Management Maturity Assessment

Applied objective: Assess Meridian’s original and improved states using evidence created across labs; identify capability gaps, strengths, dependencies, and prioritized next actions.

  • Primary lab mode: Assess / evidence / roadmap
  • Produces: maturity assessment, evidence matrix, improvement roadmap
  • Technology / medium: Sheets/Docs + evidence repository
  • DMBOK gate: read and blueprint Chapter 15 first

Chapter 16 — Data Management Organization and Role

Applied objective: Map actual lab activities to owners, stewards, architects, DB/data roles, consumers, and decision bodies. Work incidents that ask who decides, who performs, who approves, and who is consulted.

  • Primary lab mode: Role / accountability
  • Produces: role catalog, responsibility matrix, incident answers
  • Technology / medium: Docs/Sheets; cross-domain synthesis
  • DMBOK gate: read and blueprint Chapter 16 first

Chapter 17 — Data Management and Organizational Change

Applied objective: Roll out a new customer master, glossary, quality rules, and governance process. Identify stakeholders, resistance, sponsorship, communications, training, adoption measures, and transition risks.

  • Primary lab mode: Adopt / transition
  • Produces: change plan, stakeholder map, communications/training plan, adoption measures
  • Technology / medium: Docs/Sheets; human/change simulation
  • DMBOK gate: read and blueprint Chapter 17 first
  • Boundary: programming is not the point of this chapter

Shared carry-forward rule

These chapters should draw from the accumulated Meridian system—prior schemas, governance decisions, definitions, lineage, quality evidence, incidents, and earlier lab artifacts—so the final experience resembles an evolving enterprise rather than seventeen disconnected tutorials.