B0147: Data Quality Improvement Roadmap Framework

Context

Context: planning a data quality improvement roadmap creates recurring decisions where teams interpret error rate, data freshness, rework hours and source system lineage, validation rules, data ownership map differently. Without a shared frame, the accuracy versus delivery speed choice becomes implicit and accountability weakens. A decision log preserves learning and improves the next cycle.

Options

• Option A: Maintain the current approach to minimize disruption, accepting slower gains and limited learning.
• Option B: Pilot changes in phases, validate results against agreed metrics, and scale after thresholds are met.
• Option C: Redesign the approach end to end for larger gains, accepting higher execution risk and effort.

Decision

Decision: Choose Option B. Run a staged rollout that validates error rate, data freshness, rework hours against thresholds and pauses if source system lineage, validation rules, data ownership map change materially. Assign owners, document constraints, and set a review checkpoint to avoid drift.

Rationale

Rationale: Option B balances accuracy versus delivery speed while preserving flexibility if conditions shift. It allows the team to test source system lineage, validation rules, data ownership map and protect against the main risk of misjudging error rate, data freshness, rework hours. Phasing improves buy in because progress is visible and accountability is explicit.

Risks

• Weak data quality can obscure changes in error rate, data freshness, rework hours and delay corrective action.
• Execution drag may prolong exposure to the downside of accuracy versus delivery speed and reduce expected benefits.

Next

Next: Confirm ownership, finalize baselines for error rate, data freshness, rework hours, and document source system lineage, validation rules, data ownership map in a shared log. Schedule the first review, define stop conditions, and communicate the plan to affected teams.