Guide to Calculating Ownership and Operating Costs of Department of Transportation Vehicles and Equipment: An Accounting Perspective (2020)

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51 The process noted above is depicted as a continuous loop because various components may change over time, such as the type of available cost data and audience data needs. In order to empower stakeholders to make well-informed decisions based on the best data available, it is important to periodically revisit this process. 10.1. Data Visualization Best Practices Organizations across numerous industries have developed new and creative ways to report their performance through data visualization techniques. Data scientists, creators of data visualization software, and others have worked to distill best practices in this arena. The following best practices have been synthesized from several literature sources and observations made through an informal review of industry practices and recent advancements. • Use Best Available Data. This seems obvious, but analytical presentations ultimately depend on the quality, relevance, and integrity of their content. • Show Multiple Variables. Displaying data from more than one variable tells a richer and more complete story than examining one variable at a time in isolation. This approach helps viewers gain a clear picture of what is going on and make more informed conclusions and decisions. • Show, Don’t Tell. Reserve the most real estate for large visuals and graphical representations of the data and use narrative and copy sparingly to make key points. • Make Smart Comparisons. Show comparisons, contrasts, and differences: these are the elements a viewer is intuitively attracted to and curious about. Benchmarking comparable data against previous years or against other districts can be an effective way to demonstrate performance. • Emphasize Most Important Data. Combining several fields of data into a single chart can tell a nuanced story but can also be confusing for the viewer. As a best practice, put the most important data on the X- or Y-axis and use signifiers like color, size, and shape for less important data. Readers typically process information in the following order: (1) overall shape of a diagram or chart, (2) colors, then (3) details like individual parts and text. • Explain Patterns and Correlations. Where patterns emerge in the data, be prepared to provide an explanation or hypothesis for the correlation. Explain relationships between variables so the audience gains a robust understanding of the data. For example, if there is a correlation between equipment maintenance cost and outdoor air temperature, it would be informative for the audience to understand why this correlation may be happening.

52 • Integrate Visual Elements. Integrate visual elements when appropriate to help the viewer confirm and enhance their understanding of the data. For data representing costs compared across asset types, consider using icons to represent the different asset types. • Ensure Graphics are Legible and Clear. Organize charts and graphs to align with how people usually read. For example, avoid listing text labels vertically—if needed, rotate the format so text can be read horizontally. Especially in comparison charts, it is important for the viewer to be able to visually compare the data. • Avoid Overloading Data. Avoid overlapping multiple colors, shapes, and patterns that the user needs to distinguish within the same visualization. • Make it Relatable for the Reader. Interpret data to tell a cohesive story that resonates with the intended audience. Use headers that directly speak to the reader’s interests to get their attention. For example, data comparing a service’s internal costs to contracted costs could be shown to a high-level decision maker with the title “Helping You Make More Effective Decisions.” • Tell the Whole Story. For accountability purposes, it is important to tell a complete story, not just a good story. For less than favorable data, it is best practice to report that data and explain what went wrong and how it is being addressed. Giving stakeholders a complete picture of performance engenders trust and creates a more interesting narrative if performance is not always perfect.

53 11. APPENDIX D. INTERVIEW SUMMARY TABLE In developing this Guide, the research team interviewed 15 fleet managers using the questionnaire shown in Appendix E. Table 13 summarizes the responses of these 15 interviewees. Table 13. Summary of interviewee responses Fleet Characteristic 1. Ownership Model Own fleet assets X X X X X X X X X X X X X X User departments own assets X Lease certain pieces of equipment from another agency/unit X X X X X X 2. Primary Source of Equipment Funds Budget appropriation within Maintenance Department X X Budget appropriation as separate line item X X X X X X X X X Sinking fund/enterprise X X X X Debt financing/lease agreement 3. Role of Central Fleet Group Produces and administers fleet policies X X X X X X X X X X X X X X X Performs specification development X X X X X X X X X X X X X X X Purchases equipment X X X X X X X X X X X X X X X Handles vehicle disposal/auctions X X X Has central equipment upfitting shop(s) X X X X X X X X X X X X X X X Has one or more directly managed shops X X X X X X X X X X X X X X

54 Fleet Characteristic Has strong/direct authority over field-based shops X X X X X X X X Has weak/indirect authority over field-based shops X X X X X Has no authority over field shops X X X Performs fleet management function (such as determining whether repairs are performed in-house or outsourced) X X X X X X X 4. Upfitting/Make-Ready Costs Aggregated and amortized X X X X X X X X X Included in first-year maintenance and repair X X Other X 5. Salvage Funds Disposition Returned to fleet operation X X X X X X X X X X X Goes to the agency’s general fund X X X Goes to another agency/state budget X 6. Shop Rate Has a shop rate X X X X X X X X X Shop rate does not include facilities, utilities, other X X X X Shop rate is fully burdened X X Non-productive mechanic time is tracked X X X X X X X X X X X X X