02 — Exam Map & High-Yield Targets
← Guided Learning · Chapter 1 Home · Next: Visual Atlas →
This is a preparation map, not a prediction of proprietary exam questions. Chapter 1 maps primarily to Data Management Process (2%), but many of its concepts recur inside higher-weight domains.
Priority 1 — Must Know Cold
| Target | What you need to know | How it could appear | Source |
|---|---|---|---|
| Data Management definition | Broad plans/policies/programs/practices that deliver, control, protect and enhance data/information value across lifecycles | definition / scope / distinction | pp. 19–20 |
| Business driver | Obtain value from data assets | best-reason question | p. 20 |
| Data as asset | Intangible, reusable, copyable, durable, shareable and not consumed by use | asset/valuation reasoning | pp. 22–26 |
| Data requires context | Representation must be interpreted; Metadata records meaning, relationships, origin, movement and use | ambiguous-value scenario | pp. 20–22, 29–30 |
| Cross-functional responsibility | Business and IT share responsibility; many skill types are required | role/ownership | pp. 19, 24–25, 29 |
| Requirements drive IT | Technology serves business-aligned data requirements | tool-first vs requirements-first | p. 25, pp. 33–34 |
| Lifecycle vs lineage vs SDLC | life stages vs data path vs system-development lifecycle | distinction | pp. 30–32 |
| DAMA Wheel | 11 KAs with Governance central | recognition | pp. 37–38 |
| Context Diagram | Inputs/Suppliers → P-C-D-O Activities → Deliverables/Consumers, with Participants/Tools/Techniques/Metrics | diagram classification | pp. 39–41 |
| P-C-D-O | Plan, Control, Develop, Operate | recall/classification | pp. 39–40 |
| 11 Knowledge Areas | the DAMA functional map | orientation/classification | pp. 47–48 |
Priority 2 — Must Understand
- Data vs information: intertwined and often used interchangeably; practical distinctions can still help communication.
- Data valuation: no universal price formula; reason consistently about cost, benefit, risk, replacement and opportunity.
- Data Quality: fitness for stakeholder/business purpose, not merely technical availability.
- Metadata as a managed asset: needed to understand/manage data and itself requires management.
- Enterprise perspective: data crosses departmental verticals and local representations can conflict.
- Different data types: different formats, content, sensitivity, storage and access patterns can require different lifecycle controls.
- Data as value and risk: quality, misuse, privacy, security, regulation, ethics and information gaps can all create risk.
- Leadership commitment: enterprise coordination and cultural change require sustained sponsorship.
- Data strategy: begins with business strategy and the data needed to support it.
- Data Management Program Strategy: establishes the management capabilities that make the data strategy workable.
Priority 3 — Must Be Able to Distinguish
| Confusion pair | Deciding distinction |
|---|---|
| Data Management vs Data Governance | Governance directs/oversees; Data Management is the broader discipline |
| Data Management vs IT Management | data asset/lifecycle vs technology platform/service |
| Data vs Metadata | business/content value vs context about data |
| Lifecycle vs Lineage | stages/events across life vs a specific data path |
| Lifecycle vs SDLC | data life vs system-development process |
| Data Strategy vs Program Strategy | business-use direction vs management enablement |
| Charter vs Scope vs Roadmap | mandate vs planning boundary/accountability vs implementation path |
| SAM vs AIM | four broad domains vs 9-cell Business/Information/IT × Strategy/Tactics/Operations |
| Wheel vs Hexagon vs Context Diagram | what areas exist vs what factors surround work vs how one area operates |
| Aiken Pyramid vs DAMA Wheel | progression/dependencies vs Knowledge Area taxonomy |
| Suppliers vs Participants vs Consumers | provide inputs vs perform/approve work vs benefit from outputs |
| Inputs vs Deliverables | what activities receive vs what activities produce |
Priority 4 — Must Be Able to Apply
- Business/IT alignment: reject tool-first implementation when requirements are undefined.
- Quality requirements: define fitness with consumers; do not treat cleanup as the complete solution.
- Metadata problem: unknown meaning/owner/origin/lineage points to Metadata.
- Enterprise consistency: conflicting customer/product definitions require cross-domain coordination.
- Lifecycle control: quality, Metadata, security and risk span creation through disposal.
- Prioritization: focus effort on critical data and reduce ROT.
- Framework selection: choose based on the relationship the question asks you to see.
- Planning artifact: mandate → Charter; horizon/accountability → Scope; projects/milestones → Roadmap.
Useful context / recognition level
- Poor Data Quality creates direct and hidden costs such as rework, workarounds, inefficiency, conflict, dissatisfaction, missed opportunity, compliance exposure and reputational harm.
- Data can be classified by type, content/domain, format, protection level, storage or access.
- An information gap is the difference between what is known and what is needed for an effective decision.
- Knowledge Area chapters generally use: Introduction; Activities; Tools; Techniques; Implementation Guidelines; Relation to Data Governance; Metrics.
- Chapter 1 also orients to Ethics, Big Data/Data Science, Maturity, Organization/Roles and Organizational Change.
- Frameworks are adaptable lenses, not rigid implementation prescriptions.
Lower-priority detail
Do not let these displace practice on definitions, distinctions, frameworks, lifecycle, strategy and Knowledge Areas:
- publication details of cited works;
- exact wording of metaphors such as “new oil”;
- historical dollar estimates of poor-quality cost;
- exact geometry/color of every framework figure;
- every example taxonomy in Section 2.5.10; or
- individual regulatory examples used only illustratively.
Named lists and sequences worth selective memorization
| Item | Level | Why |
|---|---|---|
| P-C-D-O | High | short and reused in Context Diagrams |
| Charter / Scope / Roadmap | High | easy to test by artifact clue |
| 11 Knowledge Areas | High orientation value | map for Chapters 3–13 |
| Wheel / Hexagon / Context Diagram | High recognition value | each serves a distinct purpose |
| Five principle buckets | Study aid | helps reconstruct the principle set |
| 13 challenges | Medium | understand themes/application more than exact order |
| Aiken phases | Medium | understand progression plus out-of-order caveat |
Self-test
1. A company buys an integration platform before defining latency, quality, lineage or sharing requirements. Which principle is most directly violated?
Answer: Data Management requirements must drive Information Technology decisions.
Why: Technology was selected before the data requirements it was supposed to serve were established.
Source: Sections 2.4 and 2.5.12, p. 25 and pp. 33–34.
2. An analyst asks where a metric came from and what transformations occurred before the dashboard. Lifecycle or lineage?
Answer: Lineage.
Why: The question asks for the route and transformations of a particular data set.
Source: Section 2.5.9, pp. 30–32.
3. A sponsor wants overall vision, business case, goals, principles, measures, risks and operating model. Which artifact?
Answer: Data Management Charter.
Why: Those are mandate/operating-intent elements, not planning-horizon boundaries or project milestones.
Source: Section 2.6, pp. 34–35.
4. A team wants inputs, suppliers, activities, deliverables, consumers, participants, tools, techniques and metrics for one Knowledge Area. Which visual?
Answer: Knowledge Area Context Diagram.
Source: Section 3.3, pp. 39–41.
5. Executives want the four-way relationship among Business Strategy, IT Strategy, Organizational Infrastructure/Processes and IT Infrastructure/Processes. Which framework?
Answer: Strategic Alignment Model (SAM).
Source: Section 3.1, p. 36.
Fast lookup map
| Need to verify | Chapter 1 |
|---|---|
| Definition / driver / goals | pp. 19–20 |
| Representation/context/Metadata | pp. 20–22 |
| Data vs information | p. 22 |
| Data as asset | pp. 22–23 |
| Principles | pp. 23–25 |
| Challenges | pp. 25–34 |
| Strategy / Charter / Scope / Roadmap | pp. 34–35 |
| Frameworks overview | pp. 35–36 |
| SAM | p. 36 |
| AIM | pp. 36–37 |
| Wheel / Hexagon / Context Diagram | pp. 37–41 |
| Aiken Pyramid | pp. 41–42 |
| Dependency/function/evolved views | pp. 42–46 |
| 11 Knowledge Areas / DMBOK structure | pp. 45–49 |