Key Concepts

Glossary of Key Concepts

Analysis Parameters:

Key inputs to agency asset management or investment optimization analysis, such as asset deterioration rates, treatment condition reset values, treatment unit costs, or analysis time horizons.

Asset Breakdown Structure:

A hierarchical model of the agency’s assets, with high level categories (such as “traffic assets” and sub-categories (such as “traffic signals”).

Asset Information Model:

As defined by Building Information Modeling (BIM) standards (ISO 19650), a model that compiles the data and information related to or required for the operation of an asset.

Asset Life-Cycle:

The asset life cycle is broken down into phases representing key milestones in the development of the asset – starting with planning, then development, then delivery, and ultimately operation and maintenance.

Automated File Validation:

Specific software created for the purpose of “running” or “processing” project digital files to validate and quality assure information located within.

Automated Work Ordering:

An automated process that generates maintenance work orders, typically based on asset use, age, maintenance or work logs, inspection results, observed defects, or condition ratings.

Batch Processing/Transfer:

Mature data integration workflows are often supported by batch processing. Batch processing allows for certain data transformation tasks to be performed according to a routine, frequently without human intervention.

Big Data:

Data that is too large and complex to be dealt with through traditional data processing applications and methods.

Business Intelligence:

Systems, applications, and process that change raw data into useful business information.

Change Management or Change Control:

Processes in place to review, evaluate, and coordinate changes to data products, applications, and systems to minimize impacts to users and reduce any change-related errors.

Cloud Storage:

Cloud storage has emerged as a useful tool to address rapidly growing data storage needs. In the cloud storage model, physical data storage is managed by external service providers (e.g. Amazon Web Services or Microsoft Azure). Cloud storage often provides lower cost and less maintenance but requires additional data access and security considerations that must be addressed.

Community of Interest:

A group of stakeholders with a common interest in a type of data or other topic area. In contrast to a Community of Practice – where members have similar job functions, Community of Interest members may come from different parts of the organization and have different goals.

Component Breakdown:

Models that divide complex assets into individual parts of the larger whole, such as dividing a bridge into the deck, superstructure, and substructure.

Computer Maintenance Management System:

Computerized maintenance management system (CMMS) is software that manages an organization's maintenance operations.

Cross Asset Metrics:

Metrics allowing for measurement and comparison of outcomes across asset programs, typically established based on the agency goals and performance objectives. Examples can include benefit, value, need backlog, safety, operational performance, etc.

Cross-Asset Resource Allocation:

A resource allocation technique where potential investment strategies across multiple asset and/or program areas are evaluated to identify an investment program which best meets overarching agency priorities.

Data Attribute:

A specific piece of the data model, describing a data entity. A data element contains a specific fact important to the business (e.g. Bridge ID, Sign Type, Pavement Roughness, or Install Date).

Data Catalog:

A listing of available data resources complied to facilitate discovery and understanding.

Data Collection Plan:

An initiative or program planning document that outlines how a data collection program will be executed and improved to meet identified business needs. This should attempt to make the best use of current resources, leverage capital investment and technology, and be guided by documented business cases and value for data collection.

Data Dictionary:

A table documenting individual data elements in a dataset containing information such as data element name, description, and type.

Data Exchange Protocol:

Standard rules for data transfer between project design, delivery, and asset life-cycle management systems and/or process participants.

Data Governance:

The accountability for the management of an organization’s data assets to achieve its business purposes and compliance with any relevant legislation, regulation, and business practice.

Data Lake:

A data lake is a single repository of different databases in native form, typically used for data exploration, rather than routine analysis. Consider the end use and such details as storage, security, agility and end user sophistication when implementing a data lake.

Data Mart:

A scaled down version of a data warehouse, meeting a particular analytical, reporting, or decision-support need.

Data Quality Management Plan:

A Data Quality Management Plan (DQMP) is a documented management system that details the quality objectives and controls to be applied during the various phases of asset data collection. Its purpose is to ensure quality in all work processes, products, and outputs, and to support continuous quality improvement. Management sponsorship and governance is critical to ensuring the success of the plan.

Data Science:

The use of scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Data Stewardship:

The formal, specifically assigned and entrusted accountability for business (as opposed to information technology) responsibilities ensuring effective control and use of data and information assets.

Data Transformation:

The process of converting data from one format to another, often required to support data integration workflows, particularly when different technologies are employed by different users or stakeholders over the asset life-cycle.

Decision Science:

Quantitative techniques used to inform decision-making by identifying optimal choices based on available information. Decision science seeks to make plain the scientific issues and value judgments underlying these decisions, and to identify tradeoffs that might accompany any particular action or inaction.

Disaster Recovery:

A disaster recovery plan documents a set of policies and procedures that support the recovery of data and data infrastructure in the event of a natural or human-made disaster. In the context of asset management, the disaster recovery plan should consider necessary access to data related to “life line” assets such as evacuation routes, utilities and communications.

Evidence-Based Design and Construction:

Use of a scientific methodology and statistical techniques to evaluate project design decisions and construction practices in order to achieve the best possible outcomes. Useful to TAM programs in identifying changes to design standards and processes supporting improved asset management and operations outcomes.


An information technology security system that monitors and controls incoming and outgoing network traffic, screening what is and is not let through based on predetermined security rules. It is essentially a barrier between trusted sources and untrusted sources. Adjustments may be required in firewall security protocols to account for new means of access (such as mobile or third-party access to agency systems).

Investment Optimization:

Analysis techniques applied to select ideal TAM investments for a given analysis horizon, objective, and set of constraints.

Investment Prioritization:

Screening and ranking techniques used to establish TAM investment priorities.

Investment Prioritization Factors:

Factors allowing individual projects or other asset management investment opportunities to be evaluated against program goals or performance objectives for purposes of investment optimization or prioritization (see examples above).

Linear Referencing System:

A linear referencing system is a method of spatial referencing the locations of physical features along a linear element. The features are described in terms of measurements from a fixed point, such as a mile marker or station along a road. Each feature is located by either a point (e.g. a signpost) or a line (e.g. a no-passing zone). A well governed LRS helps ensure spatial relationships between assets held in different databases can be viewed and analyzed.

Location Referencing System:

A set of data and procedures for managing locations of geographic objects using one or more methods for specifying location. For TAM this often includes a linear referencing system that specifies location as the distance along the roadway from a reference point (such as a county boundary or intersection).


Data providing information about other data. This information can be technical (e.g. field names and formats) or business-oriented (e.g. data definitions).

Multi-Objective Decision Analysis:

A decision-making process utilized to make the best decision against a complex set of competing criteria and priorities. When used by DOTs, multi-objective decision analysis (MODA) is typically used in capital project selection.

Utilizing an established objective hierarchy and defined value function (based on agency goals and objectives), the DOT completes detailed, project-level data collection and analysis to score potential projects and identify those with the highest returns on investment. These projects are then prioritized in programming of available funds.

Non-Asset Data:

Non-asset data is data that is contextual to the asset but not directly about the asset. For example, the soil type in the area of a buried utility pipe is not data explicitly about the asset but is highly relevant to how the asset will perform.

Performance Targets:

Based on performance measurements intended to provide evidence or give indication of an assets level of service or performance, performance targets can be directly imposed by regulators or set based on strategic objectives of an organization. Targets can be established to meet a minimum level of service committed to the users of that asset (e.g. smoothness of pavement) or aspirational if an organization is trying to enhance the level of service to encourage use or thwart competition (e.g. lower congestion levels on managed lanes).

Preventive Maintenance:

Programs or activities employing network level, long-term strategy that enhances asset performance or extends asset life through a set of proactive, cost-effective practices.

Project Data Extraction Automation:

Digital project files (whether current or legacy) contain asset and non-asset information within.

Project Data Templates:

Pre-populated project files that include asset types and standard asset information. Using these templates to begin projects enables better quality management and consistent delivery of asset information.

Project information Model:

As defined by the ISO 19650 standard, a model developed during project design and construction that begins as a design intent model, and then evolves to be a virtual construction model.

Project Scoping Templates:

Developed for common project types, these can be pre-populated with TAM analysis outcomes and asset inventory and condition information as the basis for field project scoping. These templates provide efficiencies in scoping activities and encourage investment decisions aligned with TAM priorities.

Routine Maintenance:

Routine maintenance are maintenance tasks that are planned in advance. These can be recurring or one-off scheduled preventative care.

Service Request:

Assets requiring repair due to damage or wear are identified via a service request. These requests can originate from inside or outside an agency.

Single Sign On:

Technology facilitating ease of data access across different enterprise applications and network resources, through an authentication process allowing access to multiple applications with one set of login credentials. This eliminates the need for users to maintain different user names and passwords for different systems.

Two-Way Data Exchange:

Bi-directional reading and/or writing of data between two databases.

Work Accomplishments:

The type and quantity of completed work on assets (e.g. inspections, repairs, or replacements); may include other information such as date completed, whether the work was performed by state forces or contract, resources used, and cost.

Work Order:

Work orders are authorized maintenance tasks. These can result from approved service requests or via planned preventative or routine maintenance schedules.