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CHAPTER 2 Guide for Data Management This Guide explains how transportation agencies can use data management and governance to strengthen existing Performance Measurement and Target-Setting programs in the agency. It applies the research results presented in Volume I into practical guidance for transportation agencies. The case studies and examples examined in Volume III were used to produce the guidance. The Guide is organized under the following headings: 2.1. Establishing the Need for Data Management/Governance. Data governance is central to continuous improvement. Each private sector case study company noted that data management, data governance, and data accessibility have markedly improved their ability to meet their targets. For example, Corporation X's data governance framework is central to ensuring data of sufficient quality to feed its operational-financial model. ABC Corporation measures its deviations intently. And both ABC, MNC, and Corporation X use data transfer protocols with supply chain partners and governmental entities to collaboratively improve performance. In support of the need for data governance, this section describes the important relationship between data management and performance measurement and provides a maturity model to assist agencies in assessing their state of data governance. This section is intended to assist data managers in demonstrating the need for data management and governance and prepares them for implementing the strategies described later in the Guidance. The remainder of the sections assumes an agency is committed to improving their data management practices. 2.2. Establishing Goals for Data Management. Once an agency has committed to making improvements in their data management practices, a plan to achieve this should be formed. This section describes the steps and processes to planning for successful data management. 2.3. Assessing Current State of Data Programs. In this section, tools and techniques are described related to the first step of the journey--assessment of current data practices, tools, and processes. 2.4. Establish Data Governance Programs. This section offers guidance for executing and maintaining institutional data management principles based on knowledge gained in Section 2.3. 2.5. Technology for Data Management. This section suggests technological tools and techniques. 2.6. Linking Data to Planning, Performance Measures, and Target Processes. This section provides detail related to success factors in this area. II-28