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4 tips for effective implementation of Smart City solutions

Smart Cities are implementing new solutions offered by technologies, whose evolution is turning them into new fields of practice. One such new field is telematics, which incorporates telecommunications, vehicle and transport technologies, road safety, engineering, and computer science.

One successful application of telematics, achieved by the use of big data and analytics technology, is the use of data from sensors installed in commercial vehicles to gather, transmit and interpret real time information which helps companies as well as municipalities acquire accurate information about urban road infrastructure and many other things.

For example, sensors placed in trucks passing through cities can provide data about the weather, problematic intersections and traffic lights, traffic jams, potholes, and missing railroad crossing signs, as is currently the case in Toronto. Or, the presence of a sensor-equipped truck fleet in Mexico city allowed authorities to realize that something disastrous was happening when a major earthquake struck in September, even before the earthquake was reported, therefore greatly improving emergency response times.

Such solutions can improve efficiency in municipal planning practices, allowing the city to make adjustments that have an impact. However, more than technology is required for these solutions to work. Employees have to change their work practices and adapt their skillsets. More importantly, cities themselves have to improve their ability to respond and plan in order to take advantage of the abundance of data.

According to TechRepublic, the cities that will reap the greatest benefits from this new technology will be those that will take into account the following best practices:

A commitment to top-down management. Overcoming initial resistance to such initiatives and being determined to train employees in new skills, to replace older data collection and performance measurement tools, and to streamline existing processes.

Using before and after metrics. Not neglecting to do follow-ups to measure the results of new solutions, and comparing them with the metrics before the solutions were implemented.

Focusing on diagnostics. Existing GIS systems should be made more diagnostic-oriented by equipping them with dynamic feeds and data modeling that better equip staff to diagnose infrastructure hazards and events as they happen, and scheduled the required maintenance.

Taking people into account in system installs. Avoiding the common mistake of planning system installs without sufficient time and resources for training, which is essential to make staff confident and comfortable with the new technology before it goes live.

  1. A commitment to top-down management. Overcoming initial resistance to such initiatives and being determined to train employees in new skills, to replace older data collection and performance measurement tools, and to streamline existing processes.
  2. Using before and after metrics. Not neglecting to do follow-ups to measure the results of new solutions, and comparing them with the metrics before the solutions were implemented.
  3. Focusing on diagnostics. Existing GIS systems should be made more diagnostic-oriented by equipping them with dynamic feeds and data modeling that better equip staff to diagnose infrastructure hazards and events as they happen, and scheduled the required maintenance.
  4. Taking people into account in system installs. Avoiding the common mistake of planning system installs without sufficient time and resources for training, which is essential to make staff confident and comfortable with the new technology before it goes live.

The original article by Mary Shacklett can be found on TechRepublic.