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6 Effective transit planning, operations, maintenance, and analysis involve coordination among a wide array of internal and external partners to optimize system performance. As Peter Drucker has said, âcanât improve what you donât measureâ (Drucker 2006). Data are central to improving and optimizing service, becoming a major asset and investment to the data-driven organization. Transit agencies manage more data due to the advent of new technologies and harness a new ecosystem of tools to analyze the ever-increasing size of data sets. Yet, data processing, explo- ration, developing performance metrics, and communicating decisions are often scattered across many departments. Additionally, existing policies and processes often lack cohesion within their existing agency, and agencies operate with limited and shrinking resources, a changing tech- nological landscape, and shifting roles and expectations. To manage data assets, transit agencies need to become more data driven by â¢ Responding with the ability to transform data into information and decisions; â¢ Supporting interoperability to share information; â¢ Becoming effective data custodians to manage data discovery and access; and â¢ Becoming a trusted source to manage data quality and privacy. To that end, key data are increasingly being planned and managed to support the enterprise rather than just supporting a single project, which is often done. Data governance, therefore, is becoming increasingly important for organizations and overall systems of organizations that work together. Many organizations that adopt data governance practices recognize the need to undergo a cultural transformation, changing the ways individuals and systems handle and process data. This study focused on understanding the people, processes, and tools agencies use to adopt a data-driven culture. Project Objective The objective of this study was to understand how transit agencies manage, store, analyze, and govern the data they collect with a focus on the people, processes, and operating principles for managing data. This study refined the scope of data to service data, which include service planning data and archived operational data. This study did not consider real-time information except to identify systems used to collect the data for future archiving. Technical Approach Several methods were used to gather information for this synthesis. They include â¢ Literature review of relevant research information. â¢ Survey of the practice gathered from transit agency input from a variety of different types of organizations varying by institutional structure, size, and mode(s). â¢ Case examples that gathered information on the state of the practice. C H A P T E R 1 Introduction
Introduction 7 The literature review derived from numerous research paper repositories, conference pro- ceedings, and several TCRP reports. The search used several keywords, including (transit) data management, data governance, data curation, and data analytics. Chapter 2 presents the results of the review. Chapter 3 summarizes the survey results, which are provided in detail in Appendix B. The survey compiled responses from 28 agencies. The survey consisted of 62 questions, some of which were triggered by answers to a previous question. The questions were grouped into themes related to transit data management. The themes included â¢ General agency organization, â¢ Data collection technologies, â¢ Service data and performance metrics, â¢ Data governance, â¢ Data management, â¢ Data curation, â¢ Data tools, and â¢ Improvement needs. In addition, the first several questions (Questions 1 through 6) requested information on the respondentâs organization and contact information. The study includes eight case examples from a variety of agencies. The case examples are divided into three categories: Category 1âBuilding Blocks to Create a Data Management Ecosystem (Enterprise Approach) This series of case examples discusses the âbuilding blocksâ and processes that Kitsap Transit, AC Transit, and Metro Transit have undertaken to manage and use service data. The four case examples explore approaches to getting started, stakeholder involvement, architecture and tools, processes (including curationâquality and access), skills required to manage their system, and lessons learned. Category 2âTransit Data Governance This series of three case examples explores agencies that are implementing data governance. The survey requested information, and only a few organizations indicated that they were initiating data governance. Staff from the agencies furthest along, KCM, UTA, and AC Transit, were interviewed on their motivation, approach, stakeholder involvement, structure including champion, and lessons learned. Category 3âOpen Source Software: Multimodal Tools and Analysis Methods This case example explores open source software (OSS) tools and how transit agencies access and use them. The tools cover both data generation and curation, and transit planning. The survey respondents identified several OSS tools that agencies are using. The case example is composed of several interviews from organizations that use OSS and a vendor of a transit OSS planning and analytics tool. Report Organization The report is divided into five chapters and also has a list of abbreviations, references, end- notes, and Appendix A. Appendix B is a separate document and available on TRBâs website at www.trb.org by searching on TCRP Synthesis 153. Chapter 1 describes the report objectives, scope, and methodology. Chapter 2 summarizes the literature review that describes current practices described by transit practitioners, as well as for transportation and associations promoting best practices for data and information management.
8 The Transit Analyst Toolbox: Analysis and Approaches for Reporting, Communicating, and Examining Transit Data Chapter 3 presents results of the survey to transit agencies of varying sizes and institutional structures. Chapter 4 presents the case examples, including the three example categories: data manage- ment building blocks, data governance, and open source tools. Chapter 5 summarizes the report findings and recommends future research related to this study. Project Scope The study focused on the building blocks needed by transit agencies to develop an effective data-driven organization. The growth and explosion of data sources, uses, and systems drive the need for transit agencies to adopt best practice data management methods, systems, and processes comprising an IT toolbox. This study investigated the current practices adopted by transit agencies to build the toolbox and become data driven (i.e., effective, introspective, and agile). The project identified key areas to understand the composition of this toolbox. The key catego- ries and questions associated with the toolbox include â¢ Data collection methods and systems â What service data are collected and for what purpose (performance measures)? â What systems are used to collect the data? â¢ Data management and curation â What data are collected? Are they integrated? â How are the data processed and accessed? â Where are the data stored, and what methods are used to label and store the data? â Who performs the data curation and quality processes, and what skills do they need? Are multiple organizations involved in data management, who are they, and how is collaboration governed? â What are the major challenges in data collection, data quality, and management? â¢ Data provisioning processes â How are the data prepared and provisioned for the public (e.g., open data portals)? â What formats, methods, and tools are used to visualize and report the service-based performance measures? A data-driven organization goes beyond just the procedures and tools used to manage transit data. Through gathering answers to these questions from the literature, a survey, and interviews with several transit agencies, the synthesis explored the governance, roles, and methods agencies use to sustain quality transit service data over time.