Information Modeling: Building the foundations for Industrial AI

In today’s interconnected and data-driven world, the way we model and manage information has evolved into an important discipline that underpins advanced industrial operations. From the smallest data packets to complex digital representations of entire factories, information modeling is the foundation upon which digital transformation and AI-driven innovation are built.

However, many organizations today have fragmented approaches to information modeling that can hinder automation, decision-making, and a unified operational view. Challenges such as data silos, integration complexities, and the sheer volume of heterogeneous data get in the way of effective and scalable information modeling.

As an example, a business goal for many industrial organizations is yield maximization. To achieve this goal, organizations should have the ability to access and integrate data from a variety of data sources. Real time operational data is generated at the edge by industrial equipment. Additional data, such as quality inspection records, production schedules and maintenance logs come from MES, ERP, and supply chain systems. This data may be stored in cloud or edge databases. Scalable and high-performance storage solutions are key to managing the volume, velocity, and variety of industrial data while enabling security, availability, and real-time access. Finally, organizations want to be able to interpret data accurately to derive value from their analytics systems. Standardized semantics and consistent context can aid in this goal by helping to ensure data meaning and relationships are clear across systems.

To enable data to be meaningfully and efficiently leveraged across industrial environments, organizations desire a unified approach to information modeling that includes data interoperability, scalable storage, and robust semantic frameworks.

Layers of Information Modeling

Information modeling is a multifaceted discipline, with three key layers encompassing serialization, storage, and semantics, each playing a pivotal role in industrial operations.

Serialization

Serialization encodes data into a structured format, allowing it to be stored, transmitted, and exchanged consistently. In industrial operations, where data flows between sensors, control systems, and enterprise applications, serialization enables interoperability by standardizing formats. Technologies like JSON-LD, XML, Protobuf, and Avro structure data for easier exchange, while CloudEvents leverage these formats for event-based communication. This helps ensure near real-time data sharing between edge devices and cloud platforms, improving efficiency and maintaining semantic integrity across systems.

Storage

Storage underpins the information modeling stack by providing the infrastructure to capture, organize, and maintain data for future use. In industrial operations, this often involves data warehouses and data lakehouses, which support structured and semi-structured data, as well as streaming data platforms and time-series databases, which enable low-latency data ingestion and provide immediate availability for analytics and operational decision-making. Open table formats like Delta Lake and Iceberg play an important role in modern storage strategies, enabling versioning, schema evolution, and efficient querying at scale. Additionally, cloud-native storage solutions like Eventhouse and distributed file systems can provide scalability, security, and accessibility for diverse industrial workloads. Together, these storage paradigms form the backbone of a robust data ecosystem, empowering advanced analytics and decision-making.

Semantics and Context

While serialization and storage provide structure, semantics and context give data meaning and situational relevance. Semantic approaches, like ontologies, describe the relationships, attributes, and behaviors of entities within a system, allowing data to be interpreted and acted upon effectively. In industrial operations, ontologies play a fundamental role in representing the interdependence between machines, processes, and products. Standards such as OPC UA with its Information Models, and the World Wide Web Consortium (W3C)’s RDF and Web of Things (WoT) enable consistent semantic representations of industrial systems, helping ensure interoperability across different devices and platforms.

By embedding semantic understanding into data, organizations create a shared language that bridges previously siloed data and enhances decision-making. For example, an ontology might map sensors to equipment, linking data from disparate sources into a unified framework. This approach fosters improved consistency, better integration, and more informed decisions across industrial operations, supporting automation and optimization.

Unifying Information Modeling Layers

For industrial operations, the unification of serialization, storage, and semantics through edge and data platform integrations can help create an information modeling ecosystem more easily. By bridging the gaps between these layers, organizations can unlock transformative benefits including:

• Improved Semantic Context Across Systems: Ontologies can serve as the backbone for delivering consistent semantic context across diverse industrial systems. By unifying serialization and storage with semantics, organizations can help ensure that data from edge devices, control systems, and enterprise platforms is enriched with meaning and relevance. This consistency enables a shared understanding of entities, processes, and their interrelations, driving more accurate and actionable insights. For AI, this structured semantic layer is fundamental—it enhances data interoperability, improves model explainability, and enables more effective reasoning and decision-making. With a well-defined ontology, AI systems can better interpret industrial data, reduce ambiguity, and generate insights that align with domain-specific knowledge.
• Advanced Decision-Making and Automation: Embedding semantic context into real-time data streams empowers advanced decision-making and automation. Unified layers enable edge systems to process contextually relevant data and trigger actions autonomously. For example, an ontology can map a temperature sensor to a specific heat exchanger, linking its readings to operational thresholds and maintenance schedules. If the sensor detects a temperature spike beyond safe limits, the system can automatically adjust cooling operations and notify operators with precise equipment context, minimizing downtime and preventing damage. Additionally, by incorporating human-in-the-loop safety mechanisms, the system can help ensure that important decisions—such as emergency shutdowns or hazard mitigations—are escalated to human operators when necessary, helping to reduce risks and improve workplace safety.
• Semantic-Driven Analytics and Predictive Insights: Unified information layers combine structured storage with rich semantic metadata, forming a foundation for advanced analytics and decision-making. Predictive models, machine learning, and autonomous agents benefit from contextual relationships, improving accuracy and insights. For instance, an ontology can map sensor data from a manufacturing robot to its specific components, detailing the operational context such as temperature, vibration, and workload patterns. Autonomous agents can use this detailed ontology to identify subtle anomalies that precede machine failure, allowing for highly accurate failure prediction and timely, automated maintenance scheduling. Generative AI (GenAI) builds on this by simulating potential outcomes and generating actionable recommendations, such as optimizing parts replacement schedules based on predicted wear and tear patterns.

Azure’s adaptive cloud approach helps empower customers to unify their information modeling layers by more easily integrating edge, on-premises, and cloud environments with a consistent, scalable platform. Azure Arc extends Azure’s powerful data and management capabilities to hybrid and multi-cloud scenarios, enabling unified governance, security, and semantic consistency across diverse infrastructures. Azure IoT Operations, enabled by Arc, provides a mechanism to collect edge data, normalize it, and enable bi-directional communication with cloud endpoints. This includes Real-Time Intelligence and other end-to-end analytics platform capabilities in Microsoft Fabric, which helps integrate data storage and semantics to create a unified data plane. As organizations evolve, we will continue to advance capabilities in enabling the creation of scalable digital twins through a data-first approach, with ontologies serving as a foundational element. This approach enables digital twins to?not only be dynamic and interoperable but also deeply enriched with semantic context for accurate modeling and simulation.

In Summary

As AI continues to redefine the industrial landscape, the importance of information modeling has never been greater. From serialization and storage to semantics and context, each layer plays an important role in building the foundation for intelligent operations. By embracing these principles and investing in an approach that supports a unified information model, organizations can help unlock the full potential of digital twins. This enables smarter, more efficient, and resilient industrial systems. Learn more about how your organization can leverage AI to accelerate business outcomes here: Industrial Transformation with AI.