Summary

Description of the Research

Transportation agencies are considering tools and techniques to treat data as a business asset. This shift to performance-based management necessitates the use of cross-cutting analytics and data-driven decision-making. In addition, the advancement of technology has left agencies with many legacy systems, architectures, and an accumulation of separate systems. Agencies are taking meaningful steps to make legacy systems more amenable to cross-functional decisions by developing “data lake” or “data warehouse” approaches. This research aimed to create a conceptual framework and a guide for State Department of Transportation (DOT) executive leadership and senior managers, mid-level managers, field staff, and others. The purpose was to help them design strategies for creating and using data ontologies. Data ontology is a framework for characterizing and defining classes, attributes, and their relationships in a domain to provide a shared meaning across multiple users and support agile, efficient, data-driven decision-making.

Research Findings

The research reiterated the increasing need for advanced techniques to improve semantic interoperability between agency data systems. This included effectively managing the knowledge embedded in legacy systems and leveraging it for efficient, integrated decision-making across business functions. Data ontology stands out as one of the practical knowledge management techniques available to transportation managers and practitioners. Data ontology can help data owners and users capture, organize, share, and apply hidden knowledge in legacy systems, thereby improving decision-making. The research revealed different classifications of data ontologies, from informal to formal, based on the degree of formality, that can be used to extract concepts and characterize information in legacy systems.

Although it is an effective technique, data ontology is still a developing practice in transportation. The research highlighted several practices across private and public transportation systems, focusing on urban infrastructure planning and maintenance. Specifically, DOTs have conducted studies and developed tools and methodologies that serve as frameworks for integrating data programs, particularly those related to financial and accounting, asset management, and highway performance management, including safety. These examples provide real-world context for future DOT implementation of ontologies. Building an agency culture to support ontology across DOTs will require effective management to address cultural factors. To sustain a culture for deploying and advancing ontology use in data-driven decision-

making, there must be a long-term strategy, buy-in from leadership, a functional decision-making structure, effective workforce management strategies, and a way to communicate the rewards associated with the change.

Research Products

NCHRP Project 23-27, “Strategies for Developing and Using Data Ontologies for Data-Driven Decision-Making,” developed a conceptual framework and a guide, serving as a roadmap for data ontology development. This resource is for transportation professionals seeking improved strategies to leverage legacy systems’ data to improve data-driven decision-making. The published guide, NCHRP Research Report 1169: Data Ontologies for Data-Driven Decision-Making: Development and Use, contains the fundamental concepts to understand the building blocks of data ontology, a conceptual framework connecting four key strategies with actionable steps, and informs users on how to integrate the process across the broader agency. Since the concept of data ontology is still developing in the transportation domain, the guide draws on notable examples and best practices, ranging from research and proof-of-concept to practical implementation in business functions. The examples offer users valuable tips and how-to guides on implementing the guide in their business.

The guide is accompanied by PowerPoint presentation slides that can be customized and used to communicate with all levels of the transportation agency, supporting implementation efforts. This includes building capacity through training, developing knowledge, and sharing information, as well as gaining executive leadership and senior management support.

Conclusions and Recommendations

NCHRP 23-27 highlighted the value of data ontology and its application in legacy data transition, knowledge management, data governance and management, and data-driven decision-making. A successful implementation will be incremental yet offer both business and technical value to transportation agencies. Understanding the process as a journey of improvements will enable transportation managers and practitioners to progress based on their needs and maturity level while exploring further research to facilitate future implementation.

Some of the areas of research recommended include developing tools to help quantify the tangible benefits of ontology, such as improved operational efficiency, savings in time and money, and effective decision outcomes. Another area for exploration may include stakeholder collaboration and the documentation of best practices through DOTs, the Federal Highway Administration (FHWA), and the American Association of Highway and Transportation Officials (AASHTO) support,

to streamline and standardize efforts across agencies. Finally, NCHRP 23-27 recommended capacity building as a centerpiece for future implementation efforts. This effort will help bridge the significant gap in ontology knowledge and skills in the transportation domain.