Chapter 21: Industry Examples & End-to-End Walkthroughs
Real-world examples showing how different personas use the MATIH Platform across the complete data lifecycle — from ingestion to insights, experimentation to production. Each walkthrough follows a persona through every stage of their work, demonstrating how the platform's workbenches and services work together end-to-end.
How to Use This Chapter
Each industry section contains four persona-driven walkthroughs that take you through the full platform lifecycle:
| Persona | Primary Workbench | Focus |
|---|---|---|
| Data Scientist | ML Workbench + Data Workbench | Exploration, feature engineering, model development, experimentation |
| ML Engineer | ML Workbench + Pipeline Service | Training pipelines, model deployment, serving infrastructure, monitoring |
| BI Lead | BI Workbench + Semantic Layer | Metrics definition, dashboard design, report automation, self-service analytics |
| Executive Leadership | Agentic Workbench + BI Dashboards | Natural language insights, strategic analysis, KPI monitoring, decision support |
The Platform Lifecycle
Every walkthrough covers these eight stages, showing how each persona experiences them:
┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ 1. INGEST │───▶│ 2. DISCOVER │───▶│ 3. QUERY │───▶│ 4. ORCHESTR │
│ │ │ │ │ │ │ │
│ Connect │ │ Catalog │ │ SQL / NL │ │ Pipelines │
│ sources, │ │ browse, │ │ queries, │ │ DAGs, │
│ sync data, │ │ profile, │ │ federation, │ │ scheduling, │
│ file import │ │ lineage │ │ caching │ │ transforms │
└─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ 8. EXPERIM │◀───│ 7. FEEDBACK │◀───│ 6. PRODUCE │◀───│ 5. ANALYZE │
│ │ │ │ │ │ │ │
│ A/B tests, │ │ Drift │ │ Deploy │ │ Quality │
│ hypothesis, │ │ detection, │ │ models, │ │ checks, │
│ iteration │ │ alerts, │ │ publish │ │ profiling, │
│ cycles │ │ retraining │ │ dashboards │ │ exploration │
└─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘Industries Covered
Retail & E-Commerce
Customer analytics, demand forecasting, personalization, and omnichannel optimization. The most common starting point — every business has customers and transactions.
Example scenarios: Customer churn prediction, dynamic pricing, inventory optimization, marketing attribution, basket analysis.
Financial Services
Fraud detection, credit risk scoring, regulatory reporting, and portfolio analytics. High-stakes decisions with strict compliance requirements.
Example scenarios: Transaction fraud detection, credit default prediction, AML monitoring, portfolio risk dashboards, regulatory stress testing.
Healthcare & Life Sciences
Clinical trial analytics, patient outcome prediction, operational efficiency, and compliance-first data governance. Sensitive data with HIPAA requirements.
Example scenarios: Patient readmission prediction, clinical trial enrollment optimization, drug interaction analysis, hospital capacity planning, claims analytics.
Manufacturing & Supply Chain
Predictive maintenance, quality control, supply chain optimization, and operational intelligence. IoT sensor data meets enterprise analytics.
Example scenarios: Equipment failure prediction, defect detection, demand-supply matching, logistics optimization, energy consumption analysis.
SaaS & Technology
Product analytics, user behavior modeling, revenue forecasting, and growth optimization. Data-native companies leveraging every signal.
Example scenarios: User churn prediction, feature adoption analysis, revenue cohort modeling, A/B test analysis, infrastructure cost optimization.
Cross-Cutting Themes
Every industry walkthrough demonstrates these platform capabilities:
| Capability | Where It Appears |
|---|---|
| Multi-source federation | Querying across databases, data lakes, and SaaS sources in a single SQL statement |
| Semantic layer | Business-friendly metric definitions that abstract away table complexity |
| Data quality gates | Automated validation before data enters production pipelines |
| AI-assisted analysis | Natural language queries that generate SQL, visualizations, and insights |
| Governance & compliance | Row-level security, column masking, audit trails, retention policies |
| Real-time monitoring | Drift detection, anomaly alerts, SLA tracking across all data assets |
| Template-driven acceleration | Industry-specific ontologies, model architectures, and agent workflows |
Prerequisites
Before following these walkthroughs:
- A running MATIH instance — local or cloud-deployed (see Installation)
- Sample data loaded — each industry section specifies the datasets required
- Familiarity with the platform — complete the Quickstart Tutorials first if you are new
Related Chapters
- Quickstart Tutorials — 15-minute hands-on tutorials for each workbench
- User Personas — Detailed persona descriptions and capabilities
- Platform Capabilities — Full feature documentation
- Architecture — How the platform components fit together