Data to Support Transportation Agency Business Needs: A Self-Assessment Guide (2015)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

3 Transportation Data: The Invisible Asset Transportation agencies are responsible for managing various physical assets, such as roads, bridges, and traffic signals. These assets are created and maintained to provide the traveling public with safe, efficient, and convenient travel options. Because of the importance of these assets for sup- porting the agency mission, transportation agencies strive to ensure that assets are designed to serve traveler needs and are effectively managed throughout their life cycles to provide maximum value. One asset owned by virtually all transportation agencies—yet often overlooked—is data. Agencies are investing thousands of dollars each year to collect, store, and manage data. Agencies can derive substantial value from their data investments—for example, to target high crash loca- tions for investigation, inform travelers of detours or congestion, efficiently route snowplows, identify urgent bridge replacement needs, and monitor key indicators of agency performance. However, when data is not treated like an asset, agencies do not derive full value from data investments. Agencies may be data rich, but have difficulties transforming their data into usable information. Treating data like an asset means • Inventory: understanding what data you have and how it is being used; • Valuation: making sure that investments in data are paying off in terms of improved agency decisions, improved customer service, reduced risks, and enhanced accountability; • Life Cycle Management: recognizing that data requires care and feeding throughout its life cycle to be useful and usable; and • Accountability: making sure there are people within the organization who are responsible and accountable for managing data to maximize its value. Lack of coordinated, agency-wide planning and orchestration of data collection, management, and presentation can lead to both inefficiencies and missed opportunities for transportation agencies: • Data may be collected, but not updated on a regular cycle, leading to a very short shelf-life. • Data may be collected but not well used because of lack of sufficient quality, convenient access, or documentation. • Data may be collected but not well used because the data cannot easily be integrated with other data to produce meaningful information. • Data may be duplicated across different business units of an agency, resulting in higher than necessary costs for database administration and storage—and potentially leading to conflict- ing information on different agency reports. • Some data may continue to be collected that once served a purpose but is no longer adding value—while pressing needs for actionable information in other areas go unmet. • Staff resources may be overstressed by the need for fire drill-like responses to meet time- critical information requests—without tools to facilitate or automate the process. C H A P T E R 1 Introduction

4 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Recognizing the benefits that transportation agencies can realize through improved data gov- ernance and management, the AASHTO Standing Committee on Planning (SCOP) has adopted a set of core principles for transportation data.1 These principles articulate the different dimen- sions of what it means to manage transportation data as an asset: • Principle 1—VALUABLE: Data is an asset—Data is a core business asset that has value and is managed accordingly. • Principle 2—AVAILABLE: Data is open, accessible, transparent, and shared—Access to data is critical to performing duties and functions and data must be open and usable for diverse applications and open to all. • Principle 3—RELIABLE: Data quality and extent is fit for various applications—Data quality is acceptable and meets the needs for which it is intended. • Principle 4—AUTHORIZED: Data is secure and compliant with regulations—Data is trust- worthy and safeguarded from unauthorized access, whether malicious, fraudulent, or erroneous. • Principle 5—CLEAR: There is a common vocabulary and data definition—Data dictionaries are developed and metadata are established to maximize consistency and transparency of data across systems. • Principle 6—EFFICIENT: Data is not duplicated—Data is collected once and used many times for many purposes. • Principle 7—ACCOUNTABLE: Decisions maximize the benefit of data—Timely, relevant, high-quality data is essential to maximize the utility of data for decision making. Transportation agencies adopting these principles—and putting them into action—should realize steady improvements to data value and an increased return on their data investments. Purpose of the Guide This Guide builds on the work completed in a prior scoping study conducted under NCHRP 8-36, Task 100. Completed in 2011, the object of this earlier study was to propose a framework and conceptual design to serve as the preliminary thinking for developing a resource to help transportation agencies assess the adequacy, direction, and management of their data programs. The Guide was developed to refine and operationalize the conceptual framework outlined in the scoping study report and can be used by transportation agencies to operationalize the AASHTO data principles and strengthen management of their data assets to realize greater value. The Guide is intended to help agencies answer the following questions: • Do we have the right data to make good decisions and meet reporting requirements? – What data do we need and why? • Is our current data good enough? – What level of accuracy, timeliness, completeness, and so forth is needed? • Are we getting full value from the data that we have? – Can users access, integrate, and analyze it? • Are we making the best use of our data collection and management resources? – Are we being efficient about how we collect and manage the data? • What do we need to improve? – Spot improvements (e.g., more data, different data, address specific usability issues) – Systemic improvements (e.g., improved governance, technical analysis, processes, skill sets, automation) 1 http://planning.transportation.org/Pages/Data.aspx

Introduction 5 The Guide features two types of assessment tools that can be used to examine current needs and practices at the agency: • Data Value Assessment (Data User Perspective)—assesses the degree to which data users believe that existing data sets are providing value and meeting their information needs, and • Data Management Maturity Assessment (Agency Perspective)—assesses the current level of agency capabilities for managing data assets to maximize their value. These tools can be applied separately or in combination to build an action plan to identify priority improvements for strengthening data programs based on an evaluation of risks, costs, and benefits to the agency. If these tools are applied in combination, the resulting action plan will be strongest because the plan will reflect both user and broader agency perspectives. Table 1 summarizes how the two tools address the core questions listed above. There is no presumption that agencies should strive for data perfection across the board. The emphasis is on improving data for decision making and making more effective use of existing data. The Data Value Assessment can identify spot improvements to data availability, quality, and usability concerns of data users. Such spot improvements might include collecting additional data, cleaning up existing data sets, and providing new data visualization tools to make data more usable. The Data Management Maturity Assessment can identify systemic improvements to data gov- ernance, data architecture and integration, and data quality management that can enhance data value sustainably. Systemic improvements improve agency data management capabilities rather than targeting a specific data availability or quality issue. Such improvements might include reviewing agency databases for duplication or inconsistency, defining data stewardship roles, establishing data standards, or implementing data cleansing tools. In some cases, the actions identified in the data value assessment will suggest the need for systemic improvements—for example, to address a data quality problem, one could search for and correct anomalies (a spot improvement). However, a more systemic approach would involve putting processes in place to define quality standards and validation procedures. Although the two assessments reflect different perspectives, they are intended to work together. As agency data management capabilities are strengthened, one would anticipate that user satisfaction with data would improve. Table 1. Data user and agency perspectives on key questions. Data Value: User Perspecve Data Management: Agency Perspecve 1. Right Data? User sasfacon with data availability Agency’s understanding of user needs 2. Good Enough Data? User sasfacon with data quality Agency’s support for quality assurance 3. Full Value from Data? User sasfacon with data usability Agency’s efforts to integrate and provide access to data in usable forms 4. Best Use of Resources? User perspecves on priories for improvement, given available resources Agency’s efforts to promote data re-use and coordinated data collecon across departments 5. Improvement Needs? Spot improvements to close priority data gaps Systemic improvements to improve agency data management capabilies

6 Data to Support Transportation Agency Business Needs: A Self-Assessment Guide Although user perspectives are critical to consider, agencies cannot meet all of the needs expressed. The purpose of the data assessment is not to create a list that is impossible to deliver. Rather, the assessment is a tool for improving an agency’s ability to make informed judg- ments about data investments and improve the effectiveness of the investments that are made. The assessment process can help agencies determine data priorities and optimize future data resources and investments. Intended Audiences This Guide and the accompanying assessment tools were developed based on input from 12 state DOTs and are primarily intended for the DOT audience. However, three metropolitan planning organizations (MPOs) were consulted during the early stages of development, and much of the guidance is sufficiently general to enable adaptation for MPOs, transit agencies, and other public-sector transportation agencies. Within transportation agencies, the primary audiences for this Guide are • Senior (division and district-level) managers interested in seeing the agency implement a systematic process for considering, evaluating, and prioritizing data improvement needs; • Staff with data management responsibilities wanting to review their data products and ser- vices systematically and develop an improvement plan; and • Business line managers (e.g., bureau chiefs) interested in making more effective use of data and wanting to systematically identify current data gaps and develop a strategy to fill these gaps. Structure of the Guide Chapter 2 presents an overview of the Data Self-Assessment Process and options available for scaling the process to fit agency priorities and resource availability. Chapter 3 provides guidance for the Prepare phase of the self-assessment process in which the agency mobilizes staff to direct and facilitate the assessments and decides on an initial set of focus areas to assess. Chapter 4 provides guidance for the Assess phase of the self-assessment process in which the agency conducts a series of workshops involving representatives of different business areas and/ or data management areas. These workshops result in assessment ratings, defined gaps, and lists of candidate improvements to address the gaps. Chapter 5 provides guidance for the Improve and Monitor phase of the self-assessment pro- cess, in which the agency reviews the results of the Assess phase and develops a coordinated plan of data improvements. This phase also includes a quarterly process to monitor the status of improvements that are in planning and implementation stages. Appendix A provides a glossary of terms used in this Guide. Appendix B provides templates for compiling an inventory of agency data programs. Appendix C provides a detailed description of the data management assessment elements. Appendix D provides a data improvement catalog that includes examples and references.