Data Collection Program Review
Most DOTs have programs in place for collecting inventory, condition, and performance data. However, needs and requirements change over time, as technology advances and new data sources create opportunities to improve efficiencies.
Therefore, periodic review of data collection programs across assets is helpful to determine if adjustments are warranted. Key questions include:
- What data collection is happening now and how can those processes be automated?
- What information is available in other departments that could be brought into the data collection program?
Quality Management and Governance
A comprehensive data quality management plan (DQMP) enables a consistent collection process across assets and departments. The development of a DQMP can begin with individual assets and be expanded and integrated over time.
Governance processes should also be put into place to ensure that data collection and quality control measures remain aligned with business processes and needs.
Digital Transformation and Automation
As asset collection is standardized, manual and paper processes can be replaced by digital systems and automated processes. Agencies with several disparate collection and management tools can find opportunities for consolidation.
Data Sourcing and Collection Opportunities
Evaluate existing data sources before developing a new data collection program. If new collection is needed, consider whether outsourcing would be more sustainable than establishing a new, internal data collection program.
The following terms are used within this Section.
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.
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 for multiple asset categories (e.g. pavements, roadside assets, signage, marking, drainage) can be bulk-captured using mobile LiDAR vehicles. The data can be processed to extract feature inventory, asset condition, and detailed asset attribution.
Project-level data collection for pavements can be informed by destructive methods such as drilling/coring rigs or nondestructive deflection testing via falling weight deflectometers. Ride-quality can be measured using tools such as a high-speed profiler.