High-Yield Targets 14–26
Target 14 — Adoption measurement
Know: measure adoption/delta and process, project, risk, innovation, analytics, and trusted-data effects. Deciding clue: structure has launched; question asks whether it is working. Trap: count meetings or boxes only. Source: p. 530.
Target 15 — Guiding principles
Know: shared values/rules/constraints used as decision reference points. Deciding clue: distributed groups interpret priorities inconsistently. Trap: assume centralization alone creates consistency. Source: pp. 530–531.
Target 16 — Evolution not revolution
Know: incremental change lowers risk and supports adoption/buy-in. Deciding clue: disruptive redesign proposed when staged improvement is possible. Trap: bigger transformation = more maturity. Source: p. 531.
Target 17 — Existing participants first
Know: inventory people already doing data work/analogous work before hiring; fill real gaps after. Deciding clue: informal roles or transferable skills already exist. Trap: rebuild from zero. Source: pp. 531–532.
Target 18 — Committee reuse
Know: existing committee is useful only if it can devote adequate attention and make needed decisions. Deciding clue: company resists another committee or already has a senior forum. Trap: reuse a forum that cannot prioritize data. Source: p. 532.
Target 19 — Stakeholder analysis
Know: identify affected/influential parties, reactions, goals, resources, blockers, influencers, support. Deciding clue: engagement or resource prioritization. Trap: treat stakeholders equally. Source: pp. 532–533.
Target 20 — Influence–interest map
Know: High/High Key Player; High/Low Meet Needs; Low/High Show Consideration; Low/Low Lower Priority. Deciding clue: stem explicitly gives influence and interest/impact. Trap: confuse interest with authority. Source: p. 533.
Target 21 — CDO
Know: senior enterprise data strategist/ambassador bridging business and technology; can own strategy while distributed teams execute. Deciding clue: enterprise data strategy, alignment, evangelism. Trap: CDO = project sponsor. Source: p. 534.
Target 22 — Data Governance vs Data Management
Know: DG establishes policy/guidance/accountability; DM implements/operates. Deciding clue: “Who sets the rules?” vs “Who executes?” Trap: treat them as substitutes. Source: pp. 534–535.
Target 23 — Data Quality interaction
Know: DQ can begin locally and become unified/COE as shared-data scope expands; align it with DMO. Deciding clue: quality work expands across LOBs, MDM, or shared data. Trap: force all DQ work into one centralized structure immediately. Source: p. 535.
Target 24 — Enterprise Architecture / ARB
Know: Data Architects can sit in EA or DMO; coordination via governance, ARB, or leader interface. Deciding clue: architecture standards or project approval. Trap: DMO must own all Data Architects. Source: pp. 535–536.
Target 25 — Global organization
Know: network/federated patterns become attractive when standards/accountability must coexist with regional laws, languages, systems, and semi-autonomy. Deciding clue: country/regional variation at scale. Trap: centralize every decision and remove local accommodation. Source: pp. 536–537.
Target 26 — Role discrimination
Know: use the exact work product: Steward=business terms/rules; DQ Analyst=fitness/root cause; Architect=architecture; Modeler=detailed models; Integration Architect=design; Specialist=implementation. Deciding clue: responsibility, not title prestige. Trap: choose by generic word “data.” Source: pp. 537–539.