Same governance system. Four different daily workflows.
Tuesday you saw the automation code. Today you see how real team members use it. Each role has different dashboards, different alerts, different wins. All working toward one goal: compliant, quality data.
Team Workflows
See how different roles use the same system to transform their daily work.Click each role below
Before Automation
With Automation
Dashboard Metrics
Impact By The Numbers
"I finally have time to be strategic instead of just putting out fires."
— Data Governance Manager, 11 years enterprise data
How Roles Work Together on One Violation
Watch how the system coordinates across roles to detect, analyze, and fix a high-risk violation in 18 minutes.
Customer PII exposed in analytics database
✨ Scroll here to watch the workflow
Practice-Wide Impact
| Metric | Before | After | Improvement |
|---|---|---|---|
| Time to Detect Violations | 2-4 weeks (quarterly audits) | < 5 minutes (real-time scanning) | 99.7% faster |
| Policy Compliance Rate | 67% (manual spot checks) | 94% (continuous monitoring) | +27 percentage points |
| Team Hours on Governance | 15.5 hours/day (3 people) | 2 hours/day (same 3 people) | 87% reduction |
| Data Quality Score | 78/100 (inconsistent) | 92/100 (automated validation) | +14 points |
Getting Your Team On Board
Analysts think AI will replace their expertise
Show them the AI flags issues, but they make the final call. Run parallel for 2 weeks: AI + manual. AI catches 40% more issues than manual alone.
Analysts see it as a force multiplier, not a replacement. They focus on complex edge cases instead of repetitive scanning.
Managers worried about false positives overwhelming team
Start with high-confidence alerts only (95%+ accuracy). Gradually lower threshold as team builds trust. Show precision metrics: 99.2% accuracy after 30 days.
Team learns AI is more accurate than manual checks. False positive rate drops to 0.8% within 60 days.
Ops concerned about auto-remediation breaking production
Begin with 'recommend only' mode for 4 weeks. AI suggests fixes, humans approve. Track success rate: 98% of AI recommendations are approved.
After proving accuracy, enable auto-remediation for low-risk fixes. Ops focuses on critical escalations only.
Executives worried about upfront cost vs unclear ROI
Calculate time savings: 13.5 hours/day saved × $75/hour avg = $1,012/day = $22K/month. Show 30-day payback period.
ROI becomes obvious. Executives see governance as revenue enabler (faster compliance = faster deals).
Security team skeptical of AI accessing sensitive data
Run security audit: AI operates in read-only mode, all actions logged, SOC2 compliant. Offer parallel security review for 2 weeks.
Security approves after seeing audit logs and encryption standards. AI becomes trusted member of security stack.
Investment & ROI
Typical payback in 30-45 days through time savings and reduced compliance risk
Pricing
ROI Calculator
Proven Results
From Demo to Live in 4 Weeks
From demo to production in just 3 weeks
- Audit current governance processes and pain points
- Connect data sources (databases, warehouses, lakes)
- Configure initial policy rules based on your requirements
- Import existing metadata and lineage documentation
- Customize role-specific dashboards and alerts
- Train each role on their workflow (3hr sessions per role)
- Set up Slack/Teams integrations for notifications
- Configure auto-remediation rules (recommend-only mode)
- Run pilot with 5 users per role (20 people total)
- Monitor AI accuracy, gather feedback, adjust configs
- Compare AI findings vs manual audits (parallel run)
- Fine-tune alert thresholds based on false positive rate
- Roll out to all users across organization
- Enable auto-remediation for approved low-risk fixes
- Establish baseline metrics (compliance rate, time saved)
- Schedule 30-day review to measure ROI
Enterprise deployments may take 6-8 weeks for complex integrations and multi-region setups
2026 Randeep Bhatia. All Rights Reserved.
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