It takes a combination of human and machine intelligence to capitalize on the data pouring in from smart systems like AMI. “These systems are automated, but they’re not autonomous,” said University of Pennsylvania Environmental Sustainability Director Dan Garofalo.

“So we need to have people in position who know what they are looking at and can interpret it very quickly.” According to Garofalo, the university spent the past 10 years installing smart sensors in buildings across its 302-acre campus, while also developing the computer systems to store and access the resulting data.

Computer scientists then have to harmonize the myriad data formats and use the resulting datasets to train machine learning systems, which continually refine their predictions as new data comes in.

Steve Davis, who worked as GE’s Digital Business Transformation Leader for 10 years, urged smart city pioneers to carefully evaluate what kinds of data they need most and what they are already collecting.

Patrick Cairo, emeritus member of IGEL’s advisory board, noted that as networks are being built to meet the needs of utility customers and companies, “They can also serve as a backbone for other services.” McCarty suggested that other utilities and city departments could piggyback on the infrastructure PWD is building to gather data from water meters across the city, just as cell phone base stations piggyback on everything from high rises to newsstand kiosks.

Equipped with Rubicon technology, garbage trucks in these three areas now collect and share data with both sanitation and sustainability departments in the cities.

Sensors in the trucks gather data that helps improve the efficiency of trash collection, while other data increases the amount of waste being diverted from landfills.