Data Ontologies for Data-Driven Decision-Making: Research Approach and Findings (2026)
Chapter: 1 Introduction
1. Chapter 1 – Introduction
1.1. Background
In an environment with heightened performance-based management, cross-cutting analytics, and transparent decision-making, it’s proving more critical than ever to balance organizational knowledge with the need to harness quality and timely data and information across functional areas, programs, and modes. As DOTs collect more and more data, they face persistent challenges in converting that data into a business asset and, instead, often resort to ad hoc methods that produce unreliable results and different interpretations. This is because information systems are typically structured according to a “silo”-based architecture, which is populated by several independent, distributed data sources, each serving a specific application.
In addition, technological advancements and regulatory requirements have left agencies with many legacy systems, architectures, and an accumulation of separate systems. Although limited in many aspects, these legacy systems are still highly relevant because they are well understood by long-standing experts who have worked with the data. Notwithstanding their relevance and value in decision-making, legacy systems are costly to maintain and do not easily integrate with other organizational data and newer data systems, leading to data silos and loss of valuable information.
1.2. Research Objective
The primary objective of this research was to develop a conceptual framework and a guide for state DOT executive leadership and senior managers, mid-level managers, field staff, and others to design strategies for creating and using data ontologies to support agile and efficient data-driven decision-making. The purpose of the guide is to provide resources for users to:
- Understand the fundamental concepts of data ontology
- Understand the challenges of streamlining data ontologies in business
- Identify tools for ontology development
- Develop data ontologies to support legacy data use
- Identify strategies to integrate data ontologies into agency business
1.3. Report Organization
The remainder of this report is organized into the following chapters:
- Chapter 2: Research Approach – This chapter outlines the research approach, including the phases and the individual tasks executed to achieve the research objective.
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Chapter 3: Research Findings —This chapter highlights the research findings, including the following:
- Literature Review
- Targeted Surveys and In-depth Interviews
- Case Studies
- Ontology Development Methodology
- Chapter 4: Summary and Future Research—This chapter provides a summary of the research findings and outlines suggested future research to support the adoption and implementation of this research.
- Appendices – This section contains the detailed findings from the research
