Glossary

A

Asset Hierarchy

An asset hierarchy is a structured representation of industrial equipment, systems, and facilities that organizes assets according to their physical or functional relationships. It helps organizations connect information across maintenance systems, historians, engineering documents, and operational applications.

Related: Asset contextualization, entity resolution, industrial knowledge graphs.

Asset Performance Management (APM)

Asset performance management is the use of data, analytics, and operational processes to improve the reliability, availability, and performance of industrial assets. APM combines equipment information, maintenance history, operating conditions, and predictive insights to support better asset decisions.

D

Data Contextualization

Data contextualization is the process of connecting raw data to the assets, processes, locations, events, and business information that give it meaning. In industrial environments, contextualization transforms disconnected operational and engineering information into structured, usable intelligence.

Related: IT/OT integration, entity resolution, industrial knowledge graphs.

Data Governance

Data governance is the framework of policies, processes, roles, and controls used to manage data quality, ownership, access, and usage. In industrial operations, data governance helps ensure that information remains consistent, traceable, and appropriate for operational decisions and AI applications.

Data Lineage

Data lineage describes the origin, movement, and transformation of data throughout its lifecycle. It allows organizations to understand where information came from, how it was modified, and which systems or processes depend on it.

DataOps

DataOps is an approach to managing data pipelines and workflows through automation, collaboration, testing, and continuous improvement. Industrial DataOps applies these practices to operational and engineering information to improve data quality, reliability, and accessibility.

Data Products

Data products are reusable, governed collections of data designed to serve specific business or operational needs. Industrial data products bring together relevant information from multiple systems and make it accessible for analytics, applications, engineering workflows, and AI.

Digital Twin

A digital twin is a digital representation of a physical asset, system, or process that uses relevant data to reflect its characteristics, condition, or behavior. Industrial digital twins can support monitoring, simulation, maintenance, and operational optimization.

Related: P&ID digitization, industrial knowledge graphs, operational data.

Distributed Data Architecture

A distributed data architecture enables data processing, storage, or access across multiple systems and locations rather than relying on a single centralized environment. It can help industrial organizations work with information across plants, edge environments, enterprise systems, and cloud platforms.

E

Engineering Data Contextualization

Engineering data contextualization connects information from engineering drawings, equipment specifications, technical documents, and design systems to operational and enterprise data. It helps organizations understand how physical assets, processes, and engineering information relate to one another.

Entity Resolution

Entity resolution is the process of identifying and linking records that refer to the same real-world object across different data sources. In industrial environments, it helps reconcile inconsistent equipment names, asset identifiers, well names, and location references.

Related: Asset hierarchy, data quality, master data management.

F

Federated Knowledge Graph

A federated knowledge graph connects entities and relationships across distributed data sources without requiring all underlying information to be moved into a single repository. It provides a connected view of industrial assets, engineering information, operational data, and business context.

H

Historian Data

Historian data is time-stamped operational information collected and stored by industrial data historians. It commonly includes measurements such as temperature, pressure, flow, vibration, and equipment status, enabling engineers to analyze historical operating conditions and trends.

I

Industrial AI

Industrial artificial intelligence is the application of AI technologies to industrial processes, assets, and operations. It uses operational, engineering, and enterprise information to support activities such as anomaly detection, predictive maintenance, process optimization, and engineering analysis.

Industrial Data Contextualization

Industrial data contextualization connects operational technology (OT), information technology (IT), and engineering technology (ET) data to the assets, processes, and business relationships that make it meaningful. It helps transform fragmented industrial information into a consistent foundation for analytics, automation, and AI.

Primary SEO term: industrial data contextualization.

Industrial Knowledge Graph

An industrial knowledge graph is a structured network of industrial assets, systems, processes, and information connected through defined relationships. It helps organizations understand how equipment, engineering documentation, operational measurements, and enterprise records relate to one another.

IT/OT Convergence

IT/OT convergence is the integration of information technology systems with operational technology systems used to monitor and control physical industrial processes. It enables organizations to connect enterprise applications with operational information while addressing security, reliability, and data management requirements.

M

Management of Change (MOC)

Management of Change is a structured process for evaluating, approving, documenting, and communicating changes to industrial equipment, processes, procedures, or operating conditions. MOC helps organizations identify and manage potential safety and operational impacts before implementing changes.

O

Operational Technology (OT)

Operational technology refers to hardware and software used to monitor, control, and automate physical equipment and industrial processes. Examples include programmable logic controllers (PLCs), distributed control systems (DCS), and supervisory control and data acquisition (SCADA) systems.

P

P&ID Digitization

Piping and instrumentation diagram (P&ID) digitization is the process of converting engineering drawings into structured digital information. It involves identifying equipment, instruments, piping, symbols, tags, and their relationships so that engineering information can be searched, validated, connected, and reused.

Related: Engineering intelligence, process safety management, digital twins.

Piping and Instrumentation Diagram (P&ID)

A piping and instrumentation diagram is a detailed engineering drawing that illustrates the equipment, piping, instrumentation, and control relationships within an industrial process. P&IDs are essential references for engineering, operations, maintenance, process safety, and plant modifications.

Predictive Maintenance

Predictive maintenance uses equipment condition data, historical performance, and analytical models to estimate when maintenance may be needed. It helps industrial organizations identify developing equipment issues and plan maintenance activities before unexpected failures occur.

Process Safety Information (PSI)

Process safety information is documented information about hazardous chemicals, process technology, and equipment used to support the safe operation of covered industrial processes. It provides a foundation for process hazard analysis, operating procedures, training, and management of change.

Process Safety Management (PSM)

Process safety management is a systematic approach to identifying, evaluating, and controlling hazards associated with industrial processes. In the United States, OSHA's PSM standard establishes requirements for covered processes involving specified quantities of highly hazardous chemicals.

R

Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation is an AI approach that retrieves relevant information from external sources and provides it as context to a generative AI model. In industrial applications, RAG can help AI systems answer questions using engineering documents, operational records, and other authorized information.

S

Semantic Data Model

A semantic data model defines the meaning of data and the relationships between entities, attributes, and concepts. In industrial environments, it helps establish a common understanding of assets, measurements, processes, and business information across different systems.

T

Time-Series Data

Time-series data consists of observations recorded at specific points in time. In industrial operations, it includes sensor measurements, equipment conditions, process variables, and other time-stamped information used for monitoring, analysis, and optimization.

Time-Series Data Cleansing

Time-series data cleansing is the process of identifying and addressing errors, gaps, duplicates, inconsistent timestamps, and other quality issues in time-stamped information. It improves the reliability of industrial data used for analytics, monitoring, and AI applications.