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data-stewardship-framework

Establish enterprise data stewardship programs with roles, RACI matrices, and quality remediation workflows.

Updated Aug 23, 2026
Search Tags:#data-quality#data-governance#mdm#maturity-model#data-stewardship#metadata-management
One-click install
npx skills add https://github.com/kotarosan-dev/02_rd --skill data-stewardship-framework
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-stewardship-framework
Source: https://github.com/kotarosan-dev/02_rd/tree/main/Books/2026/02/20260208_%E3%83%87%E3%83%BC%E3%82%BF%20%E3%82%B9%E3%83%81%E3%83%A5%E3%83%AF%E3%83%BC%E3%83%89%E3%82%B7%E3%83%83%E3%83%97%20%E3%83%87%E3%83%BC%E3%82%BF%E3%83%9E%E3%83%8D%E3%82%B8%E3%83%A1%E3%83%B3%E3%83%88%EF%BC%86%E3%82%AC%E3%83%90%E3%83%8A%E3%83%B3%E3%82%B9%E3%81%AE%E5%AE%9F%E8%B7%B5%E3%82%AC%E3%82%A4%E3%83%89
Command: npx skills add https://github.com/kotarosan-dev/02_rd --skill data-stewardship-framework

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Organizations struggle to maintain reliable, well-defined data assets because responsibility, processes, and metadata are fragmented or undefined; this Skill provides a practical, phased framework to establish accountable stewardship, sane governance, and measurable improvements in data quality and metadata management.

Core Features & Use Cases

  • Governance design: Templates and guidance to create a three-layer governance model (executive sponsors, governance committee, stewardship council) and a permanent DGPO.
  • Role & responsibility definition: Clear criteria and RACI examples for business, technical, project, and operational data stewards and enterprise/lead steward roles.
  • KBE & metadata operations: Stepwise instructions to select Key Business Elements, collect and publish business definitions, map to physical assets, and enforce data quality rules.
  • Operational tooling & workflows: Advice to run profiling, issue logs, approval workflows, reference-data management, MDM identity rules, and maturity assessments with measurable KPIs.
  • Use case: Launch a small-start pilot in finance or marketing to demonstrate ROI by improving address quality, reducing downstream exceptions, and reporting maturity gains.

Quick Start

Draft a one-page governance brief naming an executive sponsor, establish a DGPO lead, choose 3 initial KBEs, and define one measurable quality rule to pilot in the chosen business area.

Frequently Asked Questions about data-stewardship-framework

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I establish a data stewardship program for enterprise data governance?▼

Enterprise data stewardship programs require a three-layer governance model, a permanent DGPO, and defined RACI matrices. This framework provides templates to assign accountability across business and technical steward roles to institutionalize data trust and operationalize remediation.

What is a RACI matrix for data stewardship and how do I define roles?▼

A RACI matrix for data stewardship defines accountability across business, technical, and operational data stewards. This framework offers clear criteria and examples to distinguish enterprise, lead, and project steward responsibilities within your governance structure.

How do I select Key Business Elements for metadata management and data quality rules?▼

Selecting Key Business Elements (KBEs) involves choosing critical data assets, collecting business definitions, and mapping them to physical assets. This framework provides stepwise instructions to enforce testable data quality rules on those KBEs.

Can I pilot a data governance framework in a specific business area like finance?▼

You can pilot data governance in specific areas like finance or marketing. The framework guides launching small-start pilots by choosing 3 initial KBEs and defining one measurable quality rule to demonstrate ROI through improved address quality and reduced exceptions.

What's the best way to operationalize data quality remediation workflows?▼

Operationalizing data quality remediation requires running profiling, maintaining issue logs, and executing approval workflows. This framework provides operational tooling advice to operationalize remediation end-to-end, from profiling outputs to measurable maturity-score reporting.

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