PoC Overview
Environment
Microsoft Power Platform, Power BI
Engagement type
Funded Assessment/PoC
Duration
3 weeks
Scope
Centralized automation intelligence control tower consolidating telemetry from Power Platform, Intake Forms, Value Metrics, and related automation tooling into a single, canonical data model that surfaces leadership-grade KPIs on ROI, adoption, governance, and efficiency.
Customer snapshot
Industry
Healthcare
Headquarters
New Jersey
The business challenge
- No unified view into ROI, adoption, governance, or compliance across automation investments.
- Metrics manually updated; dashboards siloed and inconsistent, creating reporting debt and leadership blind spots.
- Secure secret storage unavailable; unreliable connectors (e.g., Jira migration) impede data ingestion.
- Leadership lacks a single source of truth to guide prioritization and investment decisions from automation ecosystem fragmented across Power Platform, Intake Forms, Jira, and IA/AI tools — no single pane of glass for visibility.
Impact of the problem
Manual reporting effort
Inconsistent insights
Delayed decision-making
PoC objective
- Deliver a working vertical slice of the Automation ROI and governance control tower.
- Validate feasibility of ingestion pipelines and the canonical data model.
- Surface 4–6 high-impact KPIs covering adoption, value leakage, and governance exceptions in a Power BI dashboard.
- Produce a clear phase-2 roadmap covering Jira, Robocorp, and GenAI Orchestrator extension.
The Sonata solution – what we did
Solution overview
Sonata designed and built a centralized intelligence layer – the automation ROI and governance control tower – that consolidates telemetry from Power Platform, Intake Forms, and Value Metrics into a harmonized canonical data model. The solution automates KPI computation and surfaces leadership-grade metrics through a Power BI dashboard, replacing manual, fragmented reporting with enterprise-grade automation intelligence.
Crucially, the solution elevates automation from a siloed execution layer to an investment governance platform, enabling leadership to track, optimize, and reallocate automation spend based on measurable ROI and performance insights.
Phases of implementation
Week 1
Foundations and access: Scope and KPI finalization, environment setup, data profiling, canonical schema v1, and ingestion stubs.
Week 2
Pipelines, KPI engine & semantic model: Full ingestion logic, KPI computation engine, relationship mapping and DAX measures, and dashboard v1.
Week 3
Dashboard, insights, UAT and roadmap: Final visuals, insights generation, refinements, demo, and Phase-2 rollout planning.
Microsoft technologies used
Microsoft Power Platform
Power BI
Dataverse/App settings
Current scope vs future enhancements
Current scope
- Ingestion pipelines for PP telemetry, Intake Forms, and Value Metrics
- Canonical schema for opportunity, adoption, value, and governance
- 4-6 high-impact KPIs
- Power BI dashboard with drilldowns and automated refresh
Future enhancements
- Robocorp/GenAI Orchestrator integration
- Full governance / DLP analytics rollout
- Jira Cloud (until connector stabilizes)
- Token-level GenAI usage tracking
Microsoft + Sonata credibility
30+ years
of partnership
Microsoft AI Business Solutions Inner Circle member
Microsoft Frontier Firm Partner
How we did it
The engagement was structured as a 3-week funded PoC under the Microsoft + Sonata funded assessment program. The approach prioritized rapid value demonstration through a vertical slice: ingestion → canonical model → KPI engine → dashboard. Interim controls (Dataverse / App Settings) were used for secure credential management to unblock delivery while production-grade secret store integration is planned for Phase 2.
Key differentiators
- End-to-end measurement engine: Not merely a dashboarding layer, the solution encompasses ingestion, governance controls, KPI logic, and a semantic model.
- Secure-by-design integration: Interim Dataverse/App Settings approach ensures credentials are secure.
- Leadership KPIs: Value leakage, adoption maturity index, governance exception rate, and ROI impact – metrics that speak to executive investment decisions.
- Canonical data model: Harmonizes multiple disparate sources, eliminates duplication, and provides a scalable foundation for Phase-2 expansion.
Results and benefits
| Metric | Improvement |
|---|---|
| Reporting method | Automated KPI engine & canonical data model enabling creation of a unified Power BI dashboard. |
| Visibility into ROI | Agent helped consolidate telemetry into KPIs thereby improving ROI visibility from 0% to 100% of tracked automations. |
| Governance exceptions | Enabled control tower to surface exception thus improving detection accuracy by ~90% and proactive remediation |
| Leadership decision-making | Faster insights leading to reduced reporting lag by up to ~70%. |
