The National Academies

NCHRP 23-23 [Anticipated]

Data Governance Design and Implementation - Links Between Governance Approaches and Performance Effects in DOTs

  Project Data
Source: AASHTO Committee on Data Management and Analytics, California DOT
Funds: $350,000
Staff Responsibility: Sid Mohan
Fiscal Year: 2022

This project has been tentatively selected and a project statement (request for proposals) is expected to be available on this website. The problem statement below will be the starting point for a panel of experts to develop the project statement.

Cross-functional data governance design and implementation that have been optimized for different organizations is a recognized challenge for the transportation community. The experience with data governance approaches is varied among state departments of transportation (DOTs) and there is an ongoing need for greater understanding of the most effective approaches and for process improvement as feasible. Key trends such as the accelerating increase in the amount of data created/collected every year, and the growing variety of data from different sources, both internal and external to state DOTs, have exacerbated the traditional challenges of data quality, access, security, and suitability for state DOT requirements. The 2018 FHWA report, “Data Governance & Data Management: Case Studies of Select Transportation Agencies” highlighted that many of the agencies surveyed for the report did not have official data management or data governance policies.

The objective of this research is to advance the application of data governance to increase the value of data as an enterprise asset, while minimizing data-related costs and risks associated with poorly governed data, or low quality data. The proposed research will examine existing best practices for data governance and conduct further qualitative and quantitative research on the complexity of state DOT data ecosystems, including, but not limited to, existing governance approaches, data sources, data-related costs, data-related risks, data supply and value chains, enterprise data warehouses and data lakes, technology platforms, performance metrics, and user demands.

The outcomes of the proposed research will (1) fill gaps in our understanding and application of different data governance approaches in state DOTs; (2) identify key areas of data governance to gather data from state DOTs on the performance effects of different approaches across time; (3) optimize the design and implementation of data governance approaches for different organizational contexts; and (4) prepare an implementation document to deploy the data governance approaches.


Direction from the AASHTO Special Committee on Research & Innovation: Consider refining the scope of work to focus on enterprise data warehouses and data lakes.  The use of GIS and spatial data may be used to identify gaps in service; make use of enable data links.


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