The challenge
Heavy industries — energy, manufacturing, utilities — have accumulated decades of operational data trapped in silos: SCADA systems, process historians, corporate ERPs, PLCs and MES systems that do not talk to each other. The result is that operations teams make decisions with incomplete information, equipment failures are detected late, and maintenance is managed reactively rather than predictively.
Monom faced the challenge of building a platform capable of integrating this heterogeneous mix of industrial data sources, contextualising it and turning it into actionable intelligence — without requiring operations teams to be data scientists, or companies to replace their existing infrastructure.
Our solution
We developed Monom’s Industrial Data Fabric, a unified platform that integrates, governs and activates operational data from industrial plants, bridging the gap between OT (Operational Technology) systems, IT systems and AI capabilities.
Universal data integration
The platform connects with all the common data sources found in industrial environments:
- OT systems: SCADA, process historians, PLCs, field sensors
- Business systems: ERP, MES, maintenance management systems (CMMS)
- Unstructured data: technical reports, work orders, incident logs
Universal connectors automate the ingestion, cleaning, normalisation and validation of data, eliminating the manual data preparation work that consumes the majority of analytics teams’ time.
Context and governance
- Contextual enrichment — Data from different sources is automatically related and contextualised, enabling cross-source analysis that would be impossible with the original silos
- P&ID integration — Data visualisation overlaid on piping and instrumentation diagrams, the native language of plant engineers
- Centralised governance — Granular role-based access control, complete data traceability and audit compliance
- Real-time anomaly detection — Automatic alerts when data deviates from normal operating patterns
Intelligence and automation
- Interactive dashboards and time-series analysis tailored to industrial operations
- Self-service analytics — Operations teams explore data without depending on the IT department
- AI agent deployment — Automation of operational workflows through no-code configurable AI models
- Predictive maintenance (APM) — Integrated module for failure prediction and asset lifecycle optimisation
No-code architecture for operations
A key differentiator of the platform is that plant engineers can configure data flows, dashboards and predictive models without writing code. This eliminates the dependency on data science profiles for recurring operational tasks and dramatically accelerates the time-to-value of each new use case.
Key technical decisions
| Decision | Reason |
|---|---|
| Data Fabric vs. Data Warehouse architecture | Industrial data changes continuously; fabric allows real-time federation without moving all data to a central repository |
| No-code for configuration | Plant engineers understand the process but don’t program; no-code gives them autonomy without depending on IT |
| Real-time processing | In industry, an anomaly detected minutes late can mean equipment failure; minimal latency is critical |
| Native P&ID integration | Engineers think in process diagram terms, not database tables; P&ID visualisation reduces the learning curve |
| Deployment without replacing infrastructure | Industrial plants cannot afford big-bang migrations; the platform overlays on existing systems |
Results
- Major energy companies such as Enel, Naturgy and Repsol rely on the platform for their industrial operations
- IT/OT silo elimination — Data from SCADA, ERP and MES unified in a single contextualised view
- Early fault detection through multivariate real-time analysis of historical and live data
- Reduction in unplanned downtime through predictive maintenance based on real plant data
- Operational autonomy — Plant teams configure and adjust their own analyses without IT involvement
- Audit compliance guaranteed by complete data traceability and role-based access control
Tech stack
- Industrial Data Fabric (proprietary architecture)
- Universal OT/IT connectors (SCADA, Historian, ERP, MES, PLCs)
- Real-time anomaly detection engine
- Integrated P&ID visualisation
- AI/ML engine for predictive maintenance (APM)
- No-code workflow and model configuration
- Role-based access control (RBAC)
How we work
Every project follows our artisan process, adapted to each client's specific needs.
Discovery & Requirements
Deep dive into business, users and objectives. Ideation workshops, market research and MVP scope definition.
Design & Architecture
Wireframes, interactive prototypes and technical architecture. Client validation before writing code.
Development & Testing
2-week sprints with demos. CI/CD, code review and continuous testing. Feedback every iteration.
Delivery & Evolution
Production deploy, monitoring and support. Post-launch metrics and continuous improvement roadmap.