Data Ontologies for Data-Driven Decision-Making: Research Approach and Findings (2026)
Chapter: 5 Conclusions and Suggested Research
5. Chapter 5 – Conclusions and Suggested Research
5.1. Conclusions
This NCHRP research explored the development and application of data ontology to maximize the use of legacy system data in transportation agencies. The key findings from the literature, survey, interviews, and gap analysis indicate a growing need for semantic interoperability between legacy and emerging data systems to support effective and efficient decision-making across business units. The findings emphasize the need for a structured approach to developing and applying data ontologies, as well as promoting the broader business and technical benefits that data ontologies offer. Using the gathered information, NCHRP 23-27 created a conceptual framework and a guide, serving as a road map for data ontology development. This resource is for transportation professionals seeking improved strategies to leverage legacy systems’ data to improve data-driven decision-making.
Notwithstanding this research’s contributions to the effective governance and management of agency data – i.e., treating agency data as an asset- practitioners and users of the research products must understand the implementation limitations and requirements. For example, a transportation agency may need to institute organizational, operational, or tactical changes to adapt to the new program. Building an agency culture and capacity that can influence the change approach is essential. Ignoring this part of the business process can have a detrimental impact on the strategy to implement ontologies, train the workforce, practice effective communication, improve workflows, determine processes, acquire technology, and other critical factors that support organizational change and advancement.
In conclusion, this research highlights 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 improvement will enable transportation agencies to progress based on their needs and maturity level while exploring further research.
5.2. Suggested Research
This section recommends potential research ideas that could further enhance the value and impact of the current study. The following list highlights key areas for future research on the development and application of ontologies in transportation agencies, with the goal of enhancing data sharing and integration, as well as overall efficiency in data-driven decision-making.
- Return on Investment in Data Ontology Practices: Data ontology is an emerging concept in the transportation domain, so it is expected to receive resistance from decision-makers and practitioners. Addressing this challenge requires objective data and information to communicate with stakeholders. As such, metrics should be developed to evaluate the effectiveness and return on investment (ROI) of ontology adoption. The effort will include developing assessment methods and tools to track and quantify the long-term ROI of ontology adoption.
- Stakeholder Collaboration and Document Practices: Research strategies to engage stakeholders (DOTs, FHWA, AASHTO) in ontology standardization efforts. Study best practices for collaborative ontology development, open knowledge graphs, and linked data initiatives and examine ways to easily share and harmonize ontologies across transportation agencies.
- Capacity Building: A significant gap exists in knowledge and skills regarding data ontology in the transportation domain. Research is needed to disseminate this and other research products supporting ontology adoption. The research will develop strategies to support adoption and enable the development, implementation, and practical application of ontologies, thereby improving decision-making. The research will be designed to explore avenues such as pilot studies, peer exchanges, and training for all levels of management, including leadership and senior management, mid-level managers, and field staff, across the organization.
