Utah DOT (UDOT) found that many asset owners were collecting data but there was no single repository for that information. This was creating data management challenges and an inability to quantify and enterprise-wide asset inventory and value to improve data driven decisions. To address this, UDOT determined it would collect a statewide LiDAR survey of its entire roadway system every two years.
Subarea: Inventory, Condition, and Performance Collection (B.1)
Element: Coverage (B.1.a, B.2.a, B.3.a), Automation (B.1.b, B.2.b, B.3.b)
UDOT engaged a service provider to collect mobile LiDAR data of its entire roadway system. During the initial phases of data collection, UDOT encountered various challenges with asset coding and the sheer scope of the data collection. UDOT addressed these challenges individually.
Step 2a: Developing a Data DictionaryThe data collection vendor did not have sufficient documentation to properly code all the assets being collected. UDOT prepared a Data Dictionary on asset coding to drive consistent, higher quality data extraction. This required bringing together individuals from across the organization to agree upon a single set of attributes for each asset.
Step 2b: Establishing Asset TiersWith an overwhelming amount of enterprise-wide data, UDOT focused TAM decision-making, by establishing asset tiers. Tier 1 assets, such as pavements and bridges, were targeted for data-informed decision-making.
Step 3: Maintain Asset DatabaseUDOT is now working to complement the enterprise wide asset data collections using mobile applications in a newly procured asset management system. The new mobile tools replace pen and paper tracking, allowing UDOT to understand the real cost to maintain assets over their life cycle. These tools will also assist in maintaining a more up to date asset database by providing live asset updates instead of waiting for statewide collection every two years.
Standardization and ease-of-use for asset data
LeadershipExecutive endorsement and vision from onset, Division level engagement
ExpertiseTechnical expertise to troubleshoot and optimize workflows, Asset performance subject expertise to identify data requirements
CoordinationCross-functional teams to make asset priority decisions
ChangeNew data management practices shifting to single repository for data access