🧪 Tracker Mirror — CHAPTER LABS
All current statuses are Not Started.
| Ch | DMBOK Area | Applied Experience | Lab Type | Primary Tools | Prerequisite Gate | Independent Completion Test |
|---|---|---|---|---|---|---|
| 1 | Data Management | Enterprise data inventory; value/risk; lifecycle; strategy/roadmap | Management + technical orientation | Sheets/Docs; diagrams; SQL observation | Chapter 1 source fully read | Explain why each lab artifact represents Data Management rather than only IT |
| 2 | Data Handling Ethics | Ethical data-use dilemmas and decision records | Decision / dilemma | Docs/Sheets | Read full Chapter 2 first | Reason from the DMBOK ethics material without forcing coding |
| 3 | Data Governance | Decision rights, roles, councils, policies, standards, issue workflow | Management artifact + scenario | Docs/Sheets; diagrams | Read full Chapter 3 first | Use operating model to resolve a real Meridian conflict |
| 4 | Data Architecture | Current-state and target-state architecture; information flow | Architecture / trace | diagrams.net; SQL metadata observation | Read full Chapter 4 first | Explain why architecture choices support business/data requirements |
| 5 | Data Modeling and Design | Conceptual→logical→physical model; implement schema | Technical build | PostgreSQL; DBeaver; diagrams.net; SQL | T1,T2,T4,T5 + full Ch.5 read | Rebuild core model and explain keys/relationships/design choices |
| 6 | Data Storage and Operations | Operate database; transactions; backup/recovery and operational controls | Technical operations | PostgreSQL; DBeaver; SQL | T4,T5 + full Ch.6 read | Demonstrate and explain operational behavior and recovery evidence |
| 7 | Data Security | Classification; roles; grants; views; CRUD/access analysis | Technical + control | PostgreSQL; DBeaver; SQL | T3,T4,T5 + full Ch.7 read | Show both allowed and denied access and tie it to requirements |
| 8 | Data Integration and Interoperability | Ingest CSV/JSON; stage; transform; reconcile; source-to-target lineage | Technical pipeline | SQL; PostgreSQL; Python/pandas | T1–T8 + full Ch.8 read | Trace one field end-to-end and explain transformations |
| 9 | Document and Content Management | Content inventory; metadata; retention; version; e-discovery scenario | Content / management | Drive/Docs/Sheets; Python optional | Full Ch.9 read | Find requested content and justify retention/version decisions |
| 10 | Reference and Master Data | Resolve customer duplicates; survivorship; golden/trusted record; controlled code sets | Technical + governance | SQL; PostgreSQL; Python/pandas | T1–T8 + full Ch.10 read | Distinguish master/reference data through actual reconciliation |
| 11 | Data Warehousing and BI | Build analytical model; facts/dimensions; load; dashboard and KPI trace | Technical build + BI | PostgreSQL; SQL; Metabase later | T1–T5 + full Ch.11 read | Trace dashboard metric back to source and explain analytic design |
| 12 | Metadata Management | Catalog actual Meridian assets; definitions; technical metadata; lineage | Catalog / trace | SQL metadata; DBeaver; OpenMetadata later | T3,T4,T5 + full Ch.12 read | Use metadata to answer what/where/who/how a real data asset is |
| 13 | Data Quality Management | Inject defects; profile; define rules; measure; root cause; remediate; prevent | Diagnostic / break-it | SQL; PostgreSQL; Python/pandas | T1–T8 + full Ch.13 read | Show before/after quality evidence and explain root cause |
| 14 | Big Data and Data Science | Larger event files; JSON/Parquet; management implications | Recognition + technical observation | DuckDB; Python/pandas | T6,T7,T10 + full Ch.14 read | Explain what changes operationally/managerially at larger scale |
| 15 | Data Management Maturity Assessment | Assess Meridian current state using accumulated lab evidence | Assessment / evidence | Sheets/Docs | Prior labs + full Ch.15 read | Support maturity conclusions with evidence rather than impressions |
| 16 | Data Management Organization and Role | Assign responsibilities; RACI-style matrix; role incidents | Role / organization | Docs/Sheets | Prior labs + full Ch.16 read | Correctly separate owner/steward/custodian/etc. using work already performed |
| 17 | Data Management and Organizational Change | Roll out a governed change; stakeholders; resistance; adoption evidence | Change / scenario | Docs/Sheets; workflow diagrams | Prior labs + full Ch.17 read | Show how a technically correct data change succeeds or fails through adoption |