In 2021 I performed design visioning to achieve Asset Data Unification in the Maximo Application Suite, and drive a common terminology, asset data dictionary, and UX experience strategy for the Maximo suite applications integration use-cases.


We were a collection of applications loosely integrated and intently focused on addressing an aspect of optimizing an asset. We are now a unified solution that optimizes an asset using the data you have and makes that available everywhere across the asset lifecycle. We are a Maximo application suite.

The objective of this research and design aimed to identify a common Terminology that unifies the information model, user experience, and content designs, across the applications in the Maximo suite.

The research method used identified the conceptual and related variant terms and defined the scope and definitions required to achieve unification for the Maximo suite across the Maximo suite Design, Product Management, and Development teams.

The terminology

  • Defined a single source of truth that aligns the application teams.
  • Aligned IBM, industry, and dictionary definitions of terms
  • Derived input requirements to a conceptual data model.

Maximo Application Suite

Maximo application suite provides intelligent asset management through a set of applications for asset monitoring, asset management, predictive maintenance, and reliability planning.

The suite is a single, integrated cloud-based platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles, and reduce operational downtime and costs.

The suite includes Manage, Monitor, Health, Predict, and Mobile applications.

Learn more about the IBM Maximo Application Suite on


The Maximo suite terminology word cloud.



A manageable object that is either deployed or intended to be deployed in an operational environment.

Assets are often used when referring to both large assets like facilities and buildings, down to more granular assets like a pump or a compressor. In the Maximo suite, the term is more strictly used to represent managed assets and parts that are individually identified and managed.


  • Rotating asset. An asset that may be replaced for repair or refurbishment.
  • Fixed asset. An asset that remains in its installed location throughout its lifespan.
  • Point asset. An asset where maintenance does not depend on its length or measure.
  • Linear asset. An asset that is maintained in segments, such as a road, pipeline, or railroad track.
  • Asset template. A record that specifies common asset information that is shared by multiple assets.

Related terms

  • Item. Part of an asset, as a consumable or replacement part, manages an inventory but is not strictly monitored.


A place where assets are operated, stored, or repaired.

Locations are used in several different ways. While a location intuitively is considered a geographic point, like a BEDFORD site, it can also be interpreted as a functional location, like a BOILER ROOM or one of its BOILERs.

Functional locations are hierarchical, often following system-subsystem decomposition, like a plant, building, process, equipment, down to the related asset. Geographic locations may take different shapes, like a point position, a linear location like a power line, or an area like a production plant.


  • Functional location. A location type that indicates the equipment or process location of an asset.
  • Physical location. A location type that indicates the geographic location of an asset.
  • System location. A location type that indicates the top tier in a location hierarchy.
  • Process or Equipment location. A location type that indicates the mid-tier in a location hierarchy.
  • Operating location. A location type that indicates the presence of operating assets as opposed to a storage or repair location.

Related items

  • System. Systems are logical location hierarchies that represent functional areas.
  • Site. A business location where assets are co-located.

Work order

A record that contains information about work that must be performed.


  • Service request – A request from a user for work on an asset.
  • Ticket – A record, such as a service request, incident, or problem report, that can be routed and assigned a status.
  • Inspection – A record that schedules, executes, and documents asset inspection activities, using a checklist.


A device is a physical object with sensors, software, and communication technologies that enable it to connect and exchange data with other devices or systems over the internet.

Devices range across complex SCADA systems for asset control, to simple units that use sensors to measure asset operational metrics. Devices may use full MQTT IoT protocol networking, or simpler wireless protocols to connect using a dedicated gateway.


  • Thing. As in IoT Internet of THINGS.
  • Managed device. Devices that contain a device management agent.
  • Unmanaged device – Devices without a device management agent.
  • Gateway – Gateways are specialized devices that have the combined capabilities of a device and serve as access points for other devices.

Related terms

  • Device Type. Device type is a definition of devices that share characteristics or behaviors.
  • Smart Device. A device that connects to other devices or networks via different protocols such as Bluetooth, Zigbee, NFC, Wi-Fi, LiFi, and 5G.


A record that is used to record measurements.


  • Continuous meter. Meter readings that increment over time.
  • Gauge meter. Meter readings fluctuate over time.
  • Characteristic meter. Meter readings from a list of possible values.

Related terms

  • Tag, IO point – A tag represents a number or text data in a control system.
  • Sensor. Measures signals from the physical environment.
  • Metric. Time-series device data as the raw sensor or computed function values.


Data is a generalization of a time-series of data value samples.

Data may be Integers, Numbers, Boolean, Strings, Enumerated values, and Arrays.
Data is periodic and has time-stamps.
Data representing numeric samples may have a unit of measure.


  • Raw data. Single data value with time-stamp, or group of data values with common time-stamp.
  • Computed data. Time-series of computed output data values from related input data.
  • Aggregated data. Statistically aggregated output data values from related input data using statistical functions like Max, Min, Mean, Count, Sum, First, and Last. Often by a defined grain.
  • Predicted data. Predictions on future data values based on AI models and related historical data.
  • Failure data. Raw or prediction data that indicates an absolute or relative time of failure.

Data is aggregated to a grain. Grain may be time-based, data-source-based, or custom-based.

  • Time-based grains. Defined by the time period used when applying a statistical method, such as Monte, Hour, Day, Week, Month, or Year.
  • Data-source grains. Defined by a scope of data sources and by a time-train, like the average of all pumps of a model.
  • Custom grains. Non-common time-period grains, such as shifts, work hours, work days, or seasons.


A data value that deviates significantly from normal operational value patterns or is outside of the predicted value range.

An anomaly is a suspect to an abnormal behavior in the system. The detection of an anomaly may raise an alert. Several approaches may be taken when defining the deviation. A deviation may be identified as

  • Raw data is outside of the predicted confidence band
  • The distance of raw data outside of the predicted confidence band. The distance may be defined statistically using a sigma value (aka standard deviation) from the confidence band. A larger distance may indicate higher anomaly severity.
  • The velocity of raw data values departing the predicted confidence band. Higher velocity may indicate higher anomaly severity.
  • The end-to-end time window with values outside of the predicted confidence band. A time window may reduce the # of individual anomalies.
  • The heat map with clusters of values outside of the predicted confidence band. A distribution of anomalies in a time window will present a deeper understanding of the end-to-end dynamics of the anomaly.

Related terms

  • Prediction. The use of historical data and statistical or machine learning techniques to forecast future values.


Notification of a data condition.

An alert is a mechanism for notifying and recognizing an abnormal state, sharing the urgency of such a state with others, tracking the state and an incident, and the actions taken to mitigate the impact of that abnormal state.

Terminology references

Other terminology sources in

  • Product knowledge centers
  • Reference architectures
  • Standard |

Glossary in Maximo knowledge center

Concepts in Maximo Monitor knowledge center

IBM IoT Reference Architecture

w3c Web of Things

Semantic Sensor Network Ontology (SSNO)

Alliance for IoT Innovation (AIOTI) Domain Model

Smart Applications Reference Ontology (SAREF)