AI & Data Advisory Blueprint
Why Organizations Struggle to Start
🗄️Data Silos
Multiple source systems — ERP, CRM, IoT, SaaS — with no unified access layer or single version of truth.
⚠️Governance Gaps
No defined data ownership, quality standards, or compliance controls enforced across systems.
🤖AI Readiness Gap
Business wants AI outcomes but lacks structured, clean, labeled data to reliably support them.
👥Talent Uncertainty
Leadership doesn't know which roles to hire, in what order, or how to structure a data + AI team.
🌐Platform Confusion
Pressure to pick Fabric, Databricks, or Snowflake without a clear framework grounded in business needs.
Advisory Before Implementation
Organizations that invest in advisory groundwork — establishing vision, architecture, governance, and talent strategy before building — consistently outperform those that jump to implementation.
NCompas anchors every decision to a measurable business outcome, not technology enthusiasm. No platform is provisioned until the strategy is clear.
Strategy Precedes Technology
Every architectural choice maps to a business goal before a single environment is provisioned.
Governance is Non-Negotiable
Data and AI governance are designed in Phase 2, activated in Phase 3, and matured continuously.
Talent Runs Parallel to Platform
Hiring the right roles before the platform build prevents the most common delivery delay.
Start Narrow, Prove Fast
1–2 domains and 1–2 AI use cases demonstrated before scaling is the fastest path to continued investment.
Business Owns Data
Governance fails when treated as IT-only. Domain owners and stewards must take active accountability.
Engagement at a Glance
The Five-Phase Advisory Journey
Phase 1
3–5 wks · Pure Advisory
Baseline scoring, source inventory, AI opportunity matrix, gap analysis, regulatory review.
Phase 2
4–6 wks · Pure Advisory
Target architecture, governance framework, AI blueprint, org design, talent roadmap, business case.
Phase 3
8–12 wks · Architecture + Advisory
Medallion architecture, first data domain end-to-end, data quality framework, governance activation.
Phase 4
10–14 wks · Advisory + Co-Build
Platinum layer, MLOps, feature store, first AI use cases in production, AI guardrails activated.
Phase 5
3–6 mo. · Advisory Retainer
Full domain expansion, AI CoE, governance automation, capability transfer, executive reporting.
Discovery & Data Maturity Assessment
Executive Alignment Sessions
CIO, CDO, CFO, CISO interviews to capture strategic priorities and AI investment rationale.
Data Maturity Assessment
Scored baseline across data architecture, governance, analytics capability, and AI readiness.
Source System Inventory
Map all data sources by volume, format, ownership, and integration complexity.
AI Use Case Discovery Workshop
Score candidate use cases by business impact, data readiness, and feasibility.
Regulatory Compliance Review
GDPR, HIPAA, CCPA obligations mapped to current posture.
Stage 1 — Reactive
Data is an afterthought · Siloed systems · Manual reporting · No governance
Stage 2 — Structured
Departmental BI · Inconsistent definitions · Some controls in place
Stage 3 — Integrated
Central platform forming · Data quality processes · Emerging governance
Stage 4 — AI-Native
Governed lake · Self-service analytics · Active AI in production
Vision, Strategy & Roadmap Design
🏢 Target Architecture
Define end-state unified data platform — storage, processing, governance, consumption — at a logical level before any provisioning.
📅 12–18 Month Roadmap
Phased execution plan with milestones, dependencies, resource requirements, and KPIs.
📈 Investment Business Case
TCO analysis, ROI model, risk-adjusted value scenarios for Board/executive approval.
⚓ Platform Decision Framework
Scored decision matrix: build/buy/partner, licensing, ecosystem fit, talent availability, TCO.
🛡️ AI Governance Architecture
Ethics code, risk tier model (EU AI Act), accountability hierarchy, committee structure, guardrail requirements — before any model is built.
🗃️ Data Domain Prioritization
Identify 3–5 first-wave domains (Customer 360, Supply Chain, Finance) based on impact and data readiness.
👥 Organizational Design
Target data + AI team structure, reporting lines, CoE strategy, and interaction model with business units.
🧑💼 Talent Roadmap
Hire / develop / contract decisions per role; sequenced by phase with JD templates for priority hires.
Foundation Build — Governed Data Lake
Technical Build
Governance Activation
Establish
Classify
Automate
🥉 Bronze — Raw
Immutable ingested data · No quality enforcement · Full audit trail
🥈 Silver — Cleansed
Validated, deduped, standardized · Automated quality gates
🥇 Gold — Business-Ready
Curated metrics · Steward-validated · Certified KPIs for reporting
💎 Platinum — AI-Ready
Semantic models · Ontology · Feature stores · AI Agent context (Phase 4)
AI Enablement — First Use Cases in Production
Platinum Layer
Semantic models, business ontology, vector/embedding infrastructure for generative AI.
Feature Store
Centralized, versioned, reusable features across all ML models and AI use cases.
MLOps Infrastructure
Experiment tracking, model registry, versioning, performance dashboards, retraining pipelines.
AI Inventory
All AI systems registered with Owner, risk tier, purpose, and data lineage.
Risk Tier Classification
Prohibited
High-Risk
Limited
Minimal
Runtime Guardrails
PII detection · Prompt injection safeguards · Output validation · Content safety filters
Drift Monitoring
Automated alerts when model performance drifts outside acceptable bounds.
PoC Gate
KPI improvement demonstrated · Limited blast radius · Stakeholder sign-off · Data sources governed
Staging Gate
Bias testing passed · Security review complete · Model Owner assigned · Monitoring live · Rollback documented
Production Gate
Model Card documented · Governance Committee approval · Human-in-loop process defined · Rollback tested
Scale, Operate & Optimize — AI-Native Operations
🌐 Domain Expansion
All priority domains onboarded through proven Medallion Architecture; cross-domain data products for multi-domain use cases.
🤖 AI Portfolio Scaling
Advance from 2 to full portfolio — predictive, NLP, computer vision, GenAI, agentic workflows using AI Use Case Playbook.
🏆 AI Center of Excellence (CoE)
Charter, mandate, asset library, enablement programs, demand management process for new requests from business units.
🧑🏫 Capability Transfer
Runbooks, training, JD templates, onboarding guides. NCompas role graduates: co-lead → advisor → on-call → concluded.
Data Governance Framework
① Data Ownership & Stewardship
Every dataset has a named Data Owner (business) and Data Steward (technical). Defined Phase 2, assigned Phase 3.
② Data Classification
Sensitivity labels: Public / Internal / Confidential / Restricted. Automated classification from Phase 3.
③ Data Quality Management
Automated rules at each tier. Quality scores in dashboards. Business rules validated by domain stewards.
④ Data Lineage
End-to-end lineage from ingestion through AI-ready layer. Mandatory for all production datasets from Phase 3.
⑤ Access Control
Role-based access at platform level. Row/column-level controls for sensitive data. Consistent across all tools.
⑥ Compliance Enforcement
GDPR/HIPAA/CCPA. Automated retention, deletion, cross-border transfer policies. Audit logs maintained.
⑦ Metadata Management
Central data catalog as system of record for all data assets, linked to business glossary.
| Activity | CDO | Gov Lead | Steward |
|---|---|---|---|
| Define policy | A | R | C |
| Assign ownership | R | A | R |
| Apply labels | I | A | R |
| AI audits | A | R | I |
R=Responsible · A=Accountable · C=Consulted · I=Informed
AI Governance & Responsible AI
🚫Prohibited
Mass biometric surveillance, social scoring — not permitted.
⚠️High-Risk
HR decisions, credit scoring, medical triage — full governance + mandatory human oversight.
💬Limited-Risk
Customer chatbots, content generation — disclosure requirements + output monitoring.
✅Minimal-Risk
Spam filters, internal Q&A, report summaries — acceptable use policy + basic monitoring.
Use case registered in AI Inventory with Owner, purpose, data categories, and risk tier.
All data sources documented; PII and sensitive data categories identified.
Bias testing completed across relevant demographic or operational slices.
Model Card documented: data provenance, methodology, known limitations, performance thresholds.
Prompt injection & data exfiltration safeguards validated (for GenAI systems).
Drift monitoring and alerting configured and tested.
Human-in-the-loop process defined (if High-Risk tier).
AI Governance Committee review complete; approval formally documented.
NIST AI RMF · ISO/IEC 42001:2023
Implementation Stack
Microsoft Fabric · OneLake · Azure Databricks · Purview · Unity Catalog
🔷Microsoft Fabric
Unified SaaS analytics & AI platform
🗄️OneLake
Single-copy enterprise data backbone
⚡Azure Databricks
Large-scale ML & data engineering
🛡️Purview + Unity Catalog
Governance · Lineage · Access control
Microsoft Fabric & OneLake
Phase 3 — Foundation
Data Factory (pipelines) · Dataflows Gen2 · Lakehouse (Bronze/Silver) · Data Warehouse (Gold) · Purview Integration
Phase 4 — AI Enablement
Real-Time Intelligence (EventStream) · Azure AI Foundry Agents · Power BI + Copilot · Semantic Models
Phase 5 — Scale
Capacity Planning & FinOps · Domain Workspace Topology · OneLake Shortcuts (Federation)
| Layer | Fabric Component | Format | Governance |
|---|---|---|---|
| 🥉 Bronze | Lakehouse Files | Raw (CSV/JSON) | Purview scan |
| 🥈 Silver | Lakehouse Tables | Delta Lake | Labels + Quality |
| 🥇 Gold | Data Warehouse | Delta + SQL | RLS + Certified |
| 💎 Platinum | Semantic Model | Semantic + Vector | AI Foundry policies |
Azure Databricks & Unity Catalog
Lakeflow is Databricks' end-to-end data engineering platform — covering ingestion, transformation (Spark Declarative Pipelines), and orchestration. Production-ready pipelines build up to 25x faster than traditional approaches.
Lakeflow Connect
Managed ingestion from 100+ sources with automatic schema evolution.
Spark Declarative Pipelines
DLT (Delta Live Tables) for reliable, tested, auto-scaling transformation logic.
Jobs Orchestration
Workflow scheduling with dependency graph, alerts, retry, and SLA tracking.
MLflow
Experiment tracking, model registry, versioning, serving, and monitoring all in one platform.
- Fine-grained access control
Catalog / schema / table / row / column level security - Column-level data lineage
Tracks which notebooks, jobs, and models touched each column - AI asset governance
ML models, feature tables, experiments all governed in Unity Catalog - Delta Sharing
Cross-org and cross-cloud data sharing without copying data
Unity Catalog — Governance Layer
Recommended Catalog Structure
AI Guardrails — Technical Architecture
Input Guardrails
PII detection, prompt injection (OWASP LLM01/02), data classification enforcement before reaching the model.
Model Governance
Bias detection, fairness testing, Azure Responsible AI Dashboard, Model Cards required for all production models.
Output Guardrails
Content filtering, hallucination detection, citation grounding, custom domain validators.
Data Access in RAG
Unity Catalog row/column security in retrieval queries, data minimization policies in pipeline.
Audit Logging
Azure Monitor + Purview Insights: all AI interactions and decisions logged for compliance review.
Microsoft Purview
- Automated Data Map scanning
- Sensitivity labels (PII, Financial)
- DLP policy enforcement
- Column-level lineage
- Cross-platform governance
Azure AI Content Safety
- Prompt injection detection
- PII masking in LLM inputs
- Harmful content filtering
- Grounding / hallucination checks
Integrated Stack
Critical Success Factors & Risk Mitigations
Executive Sponsorship is Non-Negotiable
CIO or CDO must be the named accountable executive. AI governance without executive authority consistently fails.
Governance Before Scale
Every domain and AI use case passes governance gates before production — speed without governance creates technical and legal debt.
Talent Acquisition Runs in Parallel
Hire governance lead and senior data engineer in Phase 2 — not Phase 3 — to prevent the most common delivery delay.
Start Narrow, Prove Fast
1–2 domains + 1–2 AI use cases demonstrated before scaling. Fastest path to continued board investment.
Business Owns the Data
Governance fails as an IT function. Domain owners and stewards must take active accountability.
Shadow AI bypassing governance High Risk
Mitigation: Acceptable Use Policy from Phase 2; enforcement controls from Phase 4.
Data quality too poor for AI High Risk
Mitigation: Silver-layer quality gates enforced before Gold promotion.
Organizational change resistance Medium Risk
Mitigation: Executive sponsorship, early AI wins, champion network in business units.
Talent gaps delaying delivery High Risk
Mitigation: Hybrid model — NCompas covers specialist gaps while permanent roles are hired.
AI model bias in production Medium Risk
Mitigation: Mandatory bias testing gate; quarterly audits; drift monitoring from day one.
🚀 Ready to Start?
The journey to an AI-native organization begins with a structured, governed foundation. NCompas Technology Solutions is your advisory partner from discovery to full operational independence.