Implement: Tools, Processes & Quick Wins

Deploy governance tools, implement data quality monitoring, onboard stewards, and deliver quick wins to build momentum and credibility.

🎯 Objectives

✓ Select and deploy governance tooling (data catalog, quality monitoring, glossary)
✓ Implement data quality monitoring dashboards for priority domains
✓ Onboard and train data stewards in priority domains
✓ Execute 2-3 quick-win projects that demonstrate visible value
✓ Establish governance workflows (issue management, change requests)

Select Governance Tools

Evaluate and select tools for the governance tech stack: **Data Catalog** — Central inventory of all data assets with lineage, ownership, and classification. Options: Alation, Collibra, Apache Atlas, Microsoft Purview, AWS Glue Data Catalog, Atlan. **Data Quality Engine** — Automated profiling, rule execution, and monitoring. Options: Great Expectations (open source), Informatica Data Quality, Talend, dbt tests, Soda. **Business Glossary Tool** — Managed term definitions. Many catalogs include this (Collibra, Alation). Simpler options: Confluence/SharePoint with templates. **Metadata Management** — Automated metadata harvesting and lineage. Options: OpenMetadata, DataHub (open source), Informatica Enterprise Data Catalog. **Workflow/Issue Tracking** — Governance issue management. Options: Jira, ServiceNow, or built-in catalog workflows.

💡 Consultant Tips

● You do NOT need expensive tools to start — a spreadsheet-based glossary + Great Expectations + a shared wiki can work for Phase 1
● Don't let tool selection delay the program — tool selection takes months; governance should start immediately with simple tools
● If budget is limited, start with open-source: Apache Atlas (catalog), Great Expectations (DQ), OpenMetadata (metadata)
● Collibra and Alation are the market leaders for enterprise data governance platforms, but they come with $200K+ annual licenses
● Whatever tool you choose, ensure it integrates with the existing data platforms (Snowflake, Databricks, SQL Server, etc.)

Implement Data Quality Monitoring

Set up automated quality checks for priority domains: 1. **Profile** — Run data profiling on priority data assets to establish baselines (null rates, distinct counts, value distributions) 2. **Define Rules** — Translate business DQ rules into executable checks 3. **Automate** — Schedule DQ checks to run daily/weekly as part of data pipelines 4. **Dashboard** — Create a DQ dashboard showing scores by domain, trend lines, and rule violations 5. **Alert** — Set up notifications when quality drops below thresholds 6. **Remediate** — Establish a workflow for investigating and fixing violations

💡 Consultant Tips

● Start with a DQ scorecard that shows a single number per domain (e.g., 'Customer Data Quality: 87%')
● Make the dashboard visible to executives — what gets measured gets managed
● Don't aim for 100% quality — set realistic targets based on business impact (95% is usually sufficient)
● Track DQ trends over time to show improvement — this builds the governance business case

Onboard Data Stewards

Recruit and train data stewards for priority domains: 1. **Identify** — Find business-side people who already informally manage data quality in their area. They're natural stewards. 2. **Formalize** — Give them the official title, update their job description, and allocate 20-30% of their time to stewardship. 3. **Train** — Provide training on governance policies, tools, DQ concepts, and their specific responsibilities. 4. **Equip** — Give them access to the data catalog, DQ dashboards, and issue tracking tools. 5. **Support** — Assign a DGO liaison to each steward for the first 3 months. 6. **Recognize** — Publicly acknowledge stewardship work and include it in performance reviews.

💡 Consultant Tips

● Never make stewardship a full-time role initially — 20-30% of an existing role is more sustainable
● Choose stewards who are respected by their peers and passionate about data — skills can be taught, attitude cannot
● The first stewards are critical — their success or failure sets the tone for the entire program
● Create a Steward Community of Practice where stewards meet monthly to share challenges and best practices

Execute Quick Wins

Deliver 2-3 visible wins in the first 90 days to build credibility and momentum: **Quick Win Ideas:** - Clean up duplicate customer records in the CRM (show the before/after count) - Create a business glossary for the top 50 most-debated terms - Implement DQ dashboard for the CEO's favorite report - Resolve a long-standing data conflict between two departments - Automate a manual data reconciliation that wastes 20+ hours/month - Create a data lineage map for a critical regulatory report

💡 Consultant Tips

● Quick wins should be visible to executives and take less than 30 days each
● Choose wins that affect the people who are most skeptical about governance
● Document the before/after impact with specific numbers (saved X hours, prevented Y errors, reduced Z risk)
● Communicate wins broadly — send an email, present at a town hall, update the governance intranet

📦 Phase Deliverables

☐ Governance Tool Stack (deployed and configured)

☐ Data Quality Monitoring Dashboard (for priority domains)

☐ Data Steward Onboarding Kit (training materials, role cards, tool access)

☐ Stewardship Training Program (curriculum and schedule)

☐ Quick Win Results Report (2-3 completed projects with measured impact)

☐ Governance Issue Management Workflow (intake, triage, resolution, closure)

☐ Data Quality Scorecard (executive-level summary)

← Back to Data Governance Implementation