ten-steps-data-quality

What problem does it solve?

Many organizations struggle to improve data quality because efforts focus on tooling or ad-hoc fixes rather than on connecting quality work to concrete business needs, sustainable processes, and clear ownership. This Skill provides a practical, repeatable framework to design, justify, execute, and sustain data quality initiatives so improvements persist beyond one-off cleanups.

Core Features & Use Cases

• Business-driven scoping: Start from the highest-priority business need and map which data issues block that outcome.
• End-to-end 10-step workflow: Guidance from scoping and environment analysis through evaluation, root-cause analysis, remediation, monitoring, and change management.
• Impact justification: Techniques to collect episodes, quantify cost of low-quality data, and build ROI-based business cases for investment.
• Use cases include launching a master-data cleanup, embedding quality checks into SDLC/migrations, setting up ongoing QA dashboards, and building a governance-backed quality program.

Quick Start

Draft a concise data quality project plan that names the primary business need, selects 2–3 evaluation axes, outlines a short baseline assessment approach, quantifies impact with at least one episode, and proposes prioritized preventive and corrective actions.