Establish asset, treatment, and work data standards, standard prioritization factors, metadata standards, and comprehensive governance programs.
Improvements in this Area aim at specifying data requirements to align with agency business needs, standardizing data models so that information from different systems can be integrated and aggregated for analysis and reporting, and formalizing roles and processes to ensure alignment and coordination across different stakeholders.Open the full set of Response Templates for Area A
Credible, reliable data begins with well-defined and understood standards. Inventory, condition, and performance data are the most important components of a data-informed TAM program. Location referencing and design standards are also essential to support integration across life-cycle systems and with other data (such as roadway use, or environmental data).Explore Section A.1
Standardized data on asset treatments and work allows agencies to coordinate improvement planning across funding programs, understand asset maintenance and rehabilitation costs, compile a unified work history for an asset and build meaningful models of the performance of different treatments.Explore Section A.2
Standardized prioritization factors and analysis parameters are critical to support high-level asset management decision-making and resource allocation.
When standardized across the agency asset portfolio, these support alignment of investments with the agency mission, goals, and objectives and transparency in decision-making.Explore Section A.3
Standard formats and processes for documenting data element definitions and calculations, as well as dataset level information, ensure that data are well understood and useful to TAM staff, IT staff, and data users.
Accurate, accessible metadata enables users to identify data sources and elements available across the enterprise and understand their limitations.Explore Section A.4
Formal policies and procedures, oversight structures, roles and processes are critical for data standards development and adoption.
Data governance helps to ensure that data collected and maintained are well understood, used appropriately, and are effectively and efficiently collected and leveraged across the enterprise.Explore Section A.5
This project illustrates the criticality of stewardship and formal oversight for data standards within an organization. The case study reveals the necessity to engage across all levels of the organization to ensure that there is investment to provide a comprehensive, sustainable governance structure established by policy. This case study demonstrates how a specific DOT could advance governance elements from practice level 1 or 2 up to practice level 3, by implementing improvements for stewardship roles and governance structures, data management maturity self-assessments, and data and integration through process mapping.Explore Case Study