Data Capability Roadmapping

Data Capability Roadmapping

Use this skill to convert strategy and maturity gaps into a sequenced, fundable, and measurable roadmap.

When to Use

• Building a roadmap for data strategy execution.
• Sequencing initiatives from MVDG to mature data management.
• Prioritizing data governance, quality, metadata, lineage, standards, architecture, BI, MDM, or security capabilities.
• Creating investment themes, quarterly plans, or delivery waves.

Roadmap Inputs

• Business drivers and strategic outcomes.
• Current-state maturity and target maturity.
• Critical data domains, products, systems, and reports.
• Risk, audit, regulatory, or control pressures.
• Stakeholder readiness and operating capacity.
• Technology, architecture, and integration dependencies.
• Funding, staffing, and change constraints.

Prioritization Criteria

Score or rank initiatives using:

1. Business value
2. Risk reduction
3. Regulatory or audit importance
4. Dependency enablement
5. Feasibility
6. Cost and capacity
7. Adoption readiness
8. Time to impact
9. Reusability
10. Maturity uplift

Roadmap Horizons

1. 0-90 days: Launch and stabilize

   • MVDG scope, owners, issue intake, glossary starter, critical data risks, quick-win standards.

2. 3-6 months: Foundation

   • Maturity assessment, operating model, priority quality controls, metadata expectations, standards lifecycle, domain stewardship.

3. 6-12 months: Scale

   • Metadata and lineage coverage, metric governance, data product standards, integration controls, reference/master data improvements.

4. 12-18 months: Manage

   • Portfolio management, scorecards, funding model, advanced stewardship, certification, reusable patterns, change control.

5. 18+ months: Optimize and CoE

   • CoE services, automation, communities of practice, continuous improvement, advisory reviews, enterprise maturity reassessment.

Workflow

1. Confirm strategy outcomes, scope, constraints, and roadmap horizon.
2. Group work into capability themes aligned to DMBOK areas.
3. Identify dependencies, minimum viable releases, and adoption risks.
4. Prioritize using value, risk, feasibility, and maturity uplift.
5. Sequence initiatives into waves with owners, outcomes, measures, and acceptance criteria.
6. Define governance checkpoints, funding decisions, and change control.
7. Review roadmap quarterly and adjust based on evidence and stakeholder feedback.

Related Skills

• Use data-strategy-lifecycle for the strategy context.
• Use data-management-maturity-assessment for gap and target maturity inputs.
• Use governance-project-delivery for delivery backlog, risks, and milestones.
• Use data-strategy-scorecard for roadmap measures.
• Use data-coe-operating-model for late-stage operating model design.

Output Template

# Data Capability Roadmap

## Roadmap intent
- Strategic outcome:
- Scope:
- Time horizon:
- Constraints:

## Capability themes
| Theme | DMBOK area | Current gap | Target outcome | Priority |
|---|---|---|---|---|

## Roadmap waves
| Wave | Time horizon | Initiatives | Dependencies | Owner | Success measure |
|---|---|---|---|---|---|
| Launch and stabilize | 0-90 days |  |  |  |  |
| Foundation | 3-6 months |  |  |  |  |
| Scale | 6-12 months |  |  |  |  |
| Manage | 12-18 months |  |  |  |  |
| Optimize and CoE | 18+ months |  |  |  |  |

## Dependency and risk view
- Critical dependencies:
- Capacity constraints:
- Adoption risks:
- Decisions needed:

## Quarterly review
- Progress evidence:
- Changes approved:
- Measures updated:
- Next decisions:

Quality Checklist

• Roadmap is outcome-based, not only an activity list.
• Sequencing reflects dependencies, capacity, and adoption readiness.
• Each wave has measurable outcomes and named ownership.
• Early waves create enough governance to reduce risk without overbuilding.
• Quarterly review and change control are explicit.