# Data Strategy Lifecycle

> Creates and manages enterprise data strategy from minimum viable data governance through a mature data management Center of Excellence. Use when developing data strategy, data management strategy, MVDG, data governance roadmap, data capability roadmap, or CoE transition plans.

- Skill: `bkjohn2018/data-strategy-lifecycle` (Agent Skill)
- Install (CLI): `npx skillmds@latest add bkjohn2018/data-strategy-lifecycle`
- Raw SKILL.md: https://api.skillmd.com/api/skills/bkjohn2018/data-strategy-lifecycle/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: bkjohn2018 (https://skillmd.com/u/bkjohn2018)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/bkjohn2018/data-strategy-lifecycle

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# Data Strategy Lifecycle

Use this skill as the orchestrator for data strategy work. Anchor recommendations in DAMA-DMBOK concepts and call specialized skills for deeper deliverables.

## When to Use

- Creating or refreshing an enterprise data strategy.
- Moving from minimum viable data governance to repeatable data management.
- Sequencing data governance, quality, metadata, architecture, standards, analytics, and CoE capabilities.
- Building executive-ready strategy, roadmap, investment, or maturity materials.
- Managing the strategy lifecycle after initial publication.

## DMBOK Strategy Anchors

Frame the strategy around these capability areas:

1. Data governance
2. Data architecture
3. Data modeling and design
4. Data storage and operations
5. Data security
6. Data integration and interoperability
7. Reference and master data
8. Data warehousing and BI
9. Metadata management
10. Data quality management
11. Data management maturity

## Lifecycle Stages

1. **MVDG launch**
   - Define sponsor, business drivers, critical domains, decision rights, issue intake, glossary starter, and minimum standards.

2. **Foundation build**
   - Assess maturity, define target operating model, assign owners and stewards, establish standards lifecycle, and launch priority controls.

3. **Scale and standardize**
   - Expand standards, metadata, lineage, quality monitoring, metric governance, reference data, access controls, and reusable data product requirements.

4. **Managed data function**
   - Manage a capability portfolio, funding model, roadmap, scorecard, change control, and cross-domain operating cadence.

5. **Center of Excellence**
   - Operate a CoE with service catalog, advisory reviews, enablement, communities of practice, reusable patterns, and continuous maturity improvement.

## Workflow

1. Clarify strategic drivers, business outcomes, regulatory or control pressures, and executive sponsorship.
2. Assess current state by DMBOK capability, maturity, pain point, risk, and business value.
3. Define strategy principles, target outcomes, target operating model, and capability ambition.
4. Sequence roadmap themes from MVDG to CoE using value, risk, feasibility, and dependency logic.
5. Define investment themes, roles, decision rights, governance forums, and delivery cadence.
6. Build scorecards for value, adoption, risk reduction, control effectiveness, and maturity.
7. Establish lifecycle management: review cadence, change triggers, refresh process, evidence, and owner accountability.

## Related Skills

- Use `data-management-foundations` for DMBOK capability framing.
- Use `data-governance-mvdg-launch` for the first governance release.
- Use `data-management-maturity-assessment` for maturity scoring.
- Use `data-capability-roadmapping` for sequencing and dependency planning.
- Use `data-coe-operating-model` for the CoE target state.
- Use `data-strategy-scorecard` for measurement.
- Use `governance-project-delivery` to manage the strategy workstream.
- Use `data-standards-management`, `metadata-and-lineage`, `data-quality-controls`, `metric-governance`, and `data-security-and-privacy-controls` for pillar-level implementation.

## Output Template

```markdown
# Data Strategy: [Organization or Domain]

## Executive intent
- Strategic driver:
- Business outcomes:
- Sponsor:
- Scope:

## Current-state summary
| Capability | Current maturity | Pain point | Risk | Opportunity |
|---|---:|---|---|---|

## Strategy principles
- ...

## Target state
- Operating model:
- Governance model:
- Critical capabilities:
- CoE ambition:

## Roadmap
| Stage | Time horizon | Outcomes | Key initiatives | Success measures |
|---|---|---|---|---|
| MVDG launch |  |  |  |  |
| Foundation build |  |  |  |  |
| Scale and standardize |  |  |  |  |
| Managed data function |  |  |  |  |
| Center of Excellence |  |  |  |  |

## Investment themes
- People:
- Process:
- Data and architecture:
- Technology:
- Controls and assurance:

## Measurement and lifecycle
- Scorecard:
- Review cadence:
- Refresh triggers:
- Accountable owner:
```

## Quality Checklist

- Strategy links business outcomes to data management capabilities.
- DMBOK capability coverage is explicit and tailored.
- Roadmap distinguishes MVDG, foundation, scale, managed function, and CoE stages.
- Ownership, decision rights, funding, and adoption measures are defined.
- Success measures include business value, risk reduction, adoption, quality, and maturity.