7.2.3 Preparing for Data Collection

7.2.3

Preparing for Data Collection


In order to get the most out of the data collection process, it is important for agencies to be thoughtful in the steps leading up to the actual collection of data. Three important steps to prepare for data collection include: coordinating with stakeholders, specifying exactly what data will be collected, and training staff to collect the data.


Once an organization has determined what data to collect and how to best to collect it, the next step is to prepare for data collection.

Step 1. Coordinate

On important step prior to collecting data is to coordinate with other stakeholders in the organization concerning the data collection effort. It may be possible, through such coordination, to identify opportunities for coordinating data collection activities to reduce costs. Alternatively, other stakeholders may identify needs for collecting related data to address other needs. Another possibility is that a different business unit in the organization has already collected data that may impact the data collection plan.

Step 2. Specify

In this step one must identify exactly what data will be collected, the means used to collect the data, and who will collect the data. If data collection is being outsourced, at this point it is necessary to establish contract specifications for data collection.

Also as part of this step one should establish the approach for quality assurance (QA)/quality control (QC). A QA/QC plan specifies the desired accuracy of the data to be collected, and describes the measures used to assure data are of the specific level of accuracy, review data quality as data are acquired, and address any data quality issues that arise. If data are collected using automated means, the plan should specify the approach for calibrating any measurement devices used for data collection. If data are collected through visual inspection the plan should detail training requirements.

Note that given data QA/QC is an area of particular concern for pavement condition data collection, given the expense involved in collecting this data and increased reliance on automated data collection techniques. The Federal performance management requirements described previously include a requirement for State DOTs to establish a QA/QC plan for pavement data collection.

Step 3. Contract

This step involves determining whether to outsource data collection and to contract for services if applicable. Decisions to outsource are typically made to tap into a vendor with specialized equipment and experience with a particular data collection technique, and to enable accomplishing a major collection effort within a compressed timeframe, which would not be possible using internal staff resources. Some agencies may implement a hybrid approach, hiring a contractor while using internal staff (or a separate independent contractor) for supervisory or QA functions.

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Step 4. Train

The last step prior to collecting data is to train the staff involved in data collection and review in how data collection should be performed, as well as in their specific roles and responsibilities. Training is important for any data collection effort, but is particularly important in cases where the collection effort relies on visual inspection (for inspecting bridges). In these cases, the training requirements for inspectors should be carefully established and implemented. Even where there are no formal requirements for inspectors, it can be highly valuable to assemble inspectors prior to the start of data collection to review the data to be collected, walk through the data collection process, and perform inspections in a test scenario to ensure consistent interpretation of condition assessment language and other areas where differences in human judgement may impact how data are collected.

Once these steps have been performed the next step is to collect data, following the approach established in Step 2 for data collection and QA/QC.

Utah DOT

Utah DOT started capturing LiDAR data for multiple assets in 2011. Several different business units within the agency provided funding for the effort, which has included collection of inventory data for bridges, walls, signs, signals, barriers, power poles, striping, curb cuts, drainage, shoulders and ATMS devices – as well as pavement condition and roadway geometrics. UDOT has leveraged this integrated pool of asset data for several different applications, including one which creates a draft cost estimate for asset installation for project scoping, based on existing inventory.