OT/IT Glossary

Hardware and software that monitors and controls physical devices, equipment, and processes. Think PLCs, DCS, SCADA — the systems that run factories, plants, and infrastructure. Contrast with IT, which manages information and data systems.

Industrial computer that controls machinery and processes using ladder logic or structured text programming. PLCs are real-time, deterministic, and built for harsh environments. The workhorses of industrial automation.

Control system where controller functions are distributed across the plant rather than centralised. Common in oil & gas, chemicals, and large continuous processes. Different from PLC-based systems which are typically modular and discrete.

System that monitors and controls industrial processes across geographically distributed sites. SCADA collects data from remote RTUs and PLCs, displays it to operators, and can send commands. Think water treatment plants or power grids.

Touchscreen or panel display that operators use to monitor and control industrial processes. The "dashboard" of the plant floor. Modern HMIs often run on standard PC hardware with specialised software.

Device that monitors and controls remote equipment and transmits data to a central SCADA system. Used in field locations — substations, pumping stations — where conditions are harsh and communications are limited.

KPI measuring manufacturing productivity as a percentage of fully productive time. OEE = Availability × Performance × Quality. World-class OEE is ~85%. IIoT projects frequently target OEE improvement as a primary business case.

Time-series database designed for storing industrial process data. Purpose-built for high-frequency data from PLCs and sensors. OSIsoft PI (now AVEVA PI) and AspenTech IP.21 are common examples. The original "IIoT data store."

A named data variable in a control system — e.g., "Tank1.Temperature" or "Line3.MotorSpeed." Tags are the atomic unit of OT data. Historian and SCADA systems are typically licensed by tag count.

The most common PLC programming language, designed to resemble relay logic circuit diagrams. IT developers find it alien; OT engineers find it intuitive. Part of the IEC 61131-3 standard for PLC programming languages.

Lightweight publish-subscribe messaging protocol designed for constrained devices and unreliable networks. Devices publish data to a broker; subscribers receive relevant messages. Ideal for IIoT data collection from thousands of sensors.

Platform-independent, service-oriented protocol for industrial data exchange. The modern standard for secure, structured communication between PLCs, historians, MES, and cloud platforms. OPC-UA provides both data access and an information model.

Serial communication protocol developed in 1979 that is still ubiquitous in industrial environments. Simple, robust, and widely supported — but limited: no security, no data types, no discovery. The "legacy you can't escape."

Industrial Ethernet protocol developed by Rockwell Automation, widely used in North American manufacturing. Runs on standard Ethernet hardware but uses the CIP (Common Industrial Protocol) application layer. Dominant in discrete manufacturing.

MQTT-based specification that defines how devices should report data and state to a broker — including topic namespace, payload format (protobuf), and birth/death certificates. Designed to enable Unified Namespace architectures.

Central hub that receives published messages from devices and routes them to subscribers. The broker is stateless about business logic — it just routes messages. Common brokers: Mosquitto, EMQX, HiveMQ, AWS IoT Core.

Device or software that translates between industrial protocols (e.g., Modbus to MQTT, EtherNet/IP to OPC-UA). The bridge between legacy OT equipment and modern IIoT platforms. Kepware, Cogent DataHub, and Ignition are common.

Industrial network protocol for connecting field devices (sensors, actuators, valves) to controllers. PROFIBUS, DeviceNet, and FOUNDATION Fieldbus are examples. Being progressively replaced by industrial Ethernet variants.

Reference architecture for industrial control systems that separates operations into hierarchical levels (0=field devices, 1=control, 2=supervisory, 3=operations, 4=enterprise). Widely used for OT/IT segmentation but increasingly challenged by IIoT cloud connectivity.

International standard that defines the interface between enterprise (ERP) and manufacturing (MES) systems. Defines a functional hierarchy and data models for integrating business and production systems. Predecessor concept to UNS.

Architecture pattern where all data from all systems (PLCs, historians, ERP, cloud) is published to a single, centralised message broker. Any system can subscribe to any data. Popularised by Walker Reynolds; enabled by MQTT/Sparkplug B.

Processing data at or near the source (the factory floor) rather than sending all data to the cloud. Edge reduces latency, bandwidth costs, and cloud dependency. Edge nodes run applications locally — dashboards, ML models, protocol conversion.

Device that sits between OT equipment and higher-level systems (cloud or enterprise). Collects data from PLCs/sensors, applies logic or filtering, and forwards relevant data upward. Often runs protocol conversion and lightweight applications.

Virtual representation of a physical asset, process, or system that is updated with real-time data. Used for monitoring, simulation, predictive maintenance, and process optimisation. Ranges from simple dashboards to complex physics-based simulations.

Software system that manages and monitors production on the shop floor in real time. MES sits between ERP (business level) and control systems (machine level). Tracks work orders, quality, inventory, and labour on the production floor.

Business software (SAP, Oracle, Microsoft Dynamics) that manages core business processes — finance, HR, procurement, supply chain. IIoT bridges OT data to ERP to enable real-time visibility into production costs, inventory, and scheduling.

Database optimised for storing and querying data indexed by time. Essential for IIoT data where you have millions of readings per day from sensors. InfluxDB, TimescaleDB, and OSIsoft PI are common choices. Different from relational databases.

See "Historian" above. The term is used interchangeably. Emphasises the role of the system as a time-series record of process data — the operational "source of truth" for production history.

Protection of operational technology systems from cyber threats. Different from IT security: OT prioritises availability over confidentiality (downtime in a factory is more critical than a data breach in many contexts). Safety is also a primary concern.

Physical or logical separation between OT networks and external networks (including IT/internet). Traditionally used to protect critical infrastructure. IIoT connectivity often requires bridging the air gap — a significant security decision requiring careful risk assessment.

Network segment that sits between untrusted (internet/IT) and trusted (OT) networks. Systems that need to communicate across the IT/OT boundary (e.g., a data historian with cloud connectivity) are typically placed in a DMZ.

International standard series for cybersecurity of industrial automation and control systems (IACS). Covers policies, procedures, system design, and component requirements. The primary OT security framework; referenced in critical infrastructure regulations globally.

North American Electric Reliability Corporation Critical Infrastructure Protection standards. Mandatory cybersecurity requirements for bulk electric system operators in North America. Relevant if your OT environment includes power generation or transmission.

Security model based on "never trust, always verify" — no user or device is trusted by default, even inside the network perimeter. Increasingly being applied to OT environments as IIoT connectivity erodes traditional perimeter defences.

Using data (vibration, temperature, current draw, etc.) to predict equipment failure before it happens and schedule maintenance proactively. One of the highest-ROI IIoT use cases. Contrast with reactive maintenance (fix when broken) and preventive maintenance (fixed schedule).

Continuous monitoring of equipment health parameters to detect early signs of degradation. The data collection layer that enables predictive maintenance. Typically involves vibration sensors, thermal cameras, oil analysis, or ultrasonic detectors.

Levels of message delivery guarantee in MQTT. QoS 0: fire and forget (may be lost). QoS 1: at least once (may be duplicated). QoS 2: exactly once (guaranteed, slowest). For IIoT, QoS 1 is most common — guaranteed delivery with duplicate handling.

Two approaches to collecting OT data. Polling: the system requests data at regular intervals. Event-driven: the device publishes data when something changes. MQTT is event-driven; OPC-DA uses polling. Event-driven reduces bandwidth and latency for IIoT applications.

Configuration that limits reporting to changes above a threshold. E.g., a temperature sensor only reports if the value changes by more than 0.5°C. Reduces network and storage load from continuous high-frequency sampling.

Adding business meaning to raw data. Converting "Sensor_A: 94.3" to "Assembly_Line_3 > Motor_A > TemperatureC: 94.3." UNS architectures are largely about contextualization — structuring data so it's immediately useful to consumers.

A defined way for software systems to communicate with each other. REST APIs are the most common: they use HTTP requests to exchange data. IIoT platforms expose APIs so business applications can pull production data without direct database access.

Web API style that uses standard HTTP methods (GET, POST, PUT, DELETE) and returns JSON or XML. The dominant integration pattern for cloud services and modern platforms. IIoT dashboards and analytics tools typically connect via REST APIs.

JavaScript Object Notation — a lightweight, human-readable data format widely used in IT systems and IIoT platforms. MQTT payloads are often JSON. Understanding JSON is increasingly essential for OT engineers working on IIoT integration projects.

Packaging software and its dependencies into a portable unit (container) that runs consistently across environments. IIoT edge applications are increasingly deployed as Docker containers, enabling reliable updates and rollbacks on edge gateways.

IT practice of automatically testing and deploying software changes. Being adopted in OT for managing IIoT application deployments to edge devices — enabling reliable, tested updates rather than manual field interventions.

Infrastructure (compute/storage), Platform (managed services for developers), or Software (ready-to-use applications) delivered over the internet. IIoT architectures commonly use cloud platforms (AWS, Azure, GCP) for data storage, analytics, and dashboards.

Encrypted tunnel over a public network that allows secure remote access to private systems. Commonly used for remote OT access by maintenance engineers or OEM vendors. VPN access to OT systems is a key security control point.

Requiring two or more verification factors to authenticate a user — something you know (password), something you have (phone), or something you are (biometric). Essential for any remote access to OT/IIoT systems per IEC 62443 and most compliance frameworks.

The delay between an action and its response. Critical in IIoT: safety-critical control loops require sub-millisecond latency (always local). Dashboards and analytics tolerate seconds. Cloud connectivity introduces additional latency that must be factored into architecture decisions.

The volume of data or transactions a system can process per unit of time. Measured in messages/second, MB/s, or tags/second in IIoT contexts. High-throughput requirements drive broker and database selection for large IIoT deployments.