Measurable results needed

However, as it often does in the public-safety sector, funding stands in the way of implementing data analytics, said David Jones, a former NENA president who now is vice president of Mission Critical Partners, a 911-sector consultancy. Agencies wishing to deploy data-analytics technology need to think like businesses do, he said.

“At the end of the day, let’s not be naïve to think that our mantra of, ‘[We’re] public safety,’ will be good enough—because we have learned that it is not,” Jones said. “What we need to focus on is presenting a good business case that investing in these data-analysis tools makes [financial] sense.

“We’ve heard about a lot of great solutions, but they all come at a cost.”

 It’s not enough to demonstrate that such tools provide actionable intelligence—such as those described previously by IBM’s Reade—the results also need to be measurable, Jones said.

“That’s a key point. … In these economic realities, we have to be able to measure the success—or not—of every investment that we make,” he said. “You have to focus on how you can create measurable outcomes.”

Denver County (Colo.) 911 didn’t have the money to pay for an expensive data-analysis tools designed specifically for the public-safety sector, but it still wanted to measure how its personnel was performing, said Carl Simpson, the center’s executive director.

“We wanted it to be automated, we wanted cool dashboards, but we’re not there yet [financially],” he said.

So, the center improvised by considering solutions used in other industries employing large call centers that potentially could be adapted for a PSAP. It uncovered the Blue Pumpkin work-force optimization suite, which was being used by an organ-transport call center.

“These guys had a reputation for being crisp,” Simpson said. “If someone died, they’d send someone to pick up the heart and take it somewhere else.”

According to Simpson, his center used the suite to crunch data from the previous three years—taken from the center’s call-logger, its CAD system and other sources—to predict the call volume the center is likely to experience on any given day of the year. As a result, the center started to reallocate personnel.

“We used to have an equal number of call-takers on every shift, but the data told us that the calls don’t come in like that,” Simpson said. “It made a big difference in our performance, without having to hire additional people.”

For example, where the center previously answered 80% of emergency calls on the first ring, it has improved that metric to 95%, according to Simpson. So, not only is this data-analytics deployment improved performance and cost efficiencies, in the sense that the center didn’t have to add headcount to achieve the improvement.

 “They were able to use that measurable result to justify that investment,” Jones said.