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Lesson 5 — Ten Critical Success Factors

An operating model can exist perfectly on paper and still fail. Chapter 16 surrounds the structure with ten conditions for adoption and sustainability.

The ten factors

  1. Executive sponsorship — influential champion, authority, cross-leader engagement, long-term support.
  2. Clear vision — shared understanding of what Data Management is, why it matters, and what changes.
  3. Proactive change management — plan, manage, and sustain the people-side transition.
  4. Leadership alignment — leaders agree on the need, purpose, value, success definition, and messages.
  5. Communication — early, open, frequent, consistent, stakeholder-specific explanation of why/what/expected behavior.
  6. Stakeholder engagement — deliberately involve people who can influence or are affected.
  7. Orientation and training — provide the depth of knowledge and skill appropriate to each role.
  8. Adoption measurement — prove that behaviors, processes, risk response, projects, analytics, and trusted data are actually improving.
  9. Adherence to guiding principles — shared values/rules/constraints provide consistent reference points for decisions.
  10. Evolution not revolution — use lower-risk incremental improvement when possible.

High-confusion distinctions

Sponsor: one influential champion can advocate for and protect the change.

Leadership alignment: the leadership group agrees collectively and sends a coherent message.

A strong sponsor can exist while leadership alignment is poor.

Communication vs training

Communication: “Know why, what, and what behavior is expected.”

Training: “Know how to perform the role/process/tool.”

A memo cannot fix a skill deficit.

Launch completion vs adoption measurement

A committee meeting and an org chart existing do not prove success. Adoption measurement looks for meaningful change from the prior state and enabling effects on business processes, risk, projects, decision-making, analytics, or other outcomes.

Guiding principles

Distributed models need shared decision rules as much as centralized models do. Centralization alone does not create consistency.

Source anchor: Chapter 16, pp. 528–531.

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