Utility Master Plan

Location: Rapid City, S.D.

Client: City of Rapid City

Completion Date: 2007

The City of Rapid City is experiencing a high rate of growth that presents challenges related to utility infrastructure planning. In order to facilitate systematic growth and planning, the city enlisted the help of Burns & McDonnell to complete an information management-based utility master plan that will develop the information technology (IT) and utility system tools necessary to allow staff to make informed decisions.

The initial step of the utility system master plan includes an IT needs assessment and implementation plan. Before talking with the city staff, a Web-based questionnaire was deployed to collect information from city employees regarding major job functions and the hardware, software and data used to complete these job functions. The information collected from the questionnaire was compiled and analyzed for workflow, software, hardware and data redundancies, then used as the foundation for interviews with each city department, including Finance, Public Works, Police and Fire, Growth Management, and IT. The interviews were used to add details to the workflows described in the Web questionnaire and to collect example data required for staff to complete their jobs. These workflows were documented within a three-step process and analyzed for inefficiencies and redundancies.

Upon completion of the workflow analysis phase, the workflows are prioritized, with the help of city staff, to determine which workflows would be re-engineered. The prioritization is based on which workflows are quintessential to the city’s day-to-day business as well as the workflows that have the greatest time and budget savings opportunities. The prioritized list of workflows to be re-engineered is the target for the IT implementation plan. With the endpoint of the implementation plan identified, the methods, budget and schedule necessary to accomplish each task will be developed.

  • IT/GIS assessment
  • IT/GIS planning
  • Source data assessment
  • Data model design