Information is the most valuable thing there is. It’s why companies hire consultants just to share what they know, it’s how stock brokers and financial advisers are able to make a living without actually creating anything, and it’s why the big data and predictive analytics industry is projected to reach $50 billion by 2017. Most important of all, it’s how Biff Tannen was able to amass a fortune through gambling in Back to the Future Part II after borrowing the time machine and giving a younger version of himself a sports almanac from the future.
Big data is popular because it presents an opportunity to make the sports almanac from Back to the Future Part II real. Getting reliable information about the future is an especially enticing proposition because it’s something that people naturally are bad at. Biological evolution favored those who were able to recognize patterns that would lead their tribe to food or accurately predict what would happen if they tickled a bear, but the infinitely branching nature of consequence makes more sophisticated predictions almost impossible. Anyone who’s ever put money on a sporting event knows that outcomes, even binary ones, are only obvious after they’ve happened, no matter how sure you were that the Steelers would cover the spread.
Human pragmatism is planted firmly in the present, which also makes people bad at re-imagining context. Finding tonight’s dinner has taken precedence over almost everything else for thousands of years. When asked what the problems of the future will be, people will nearly always describe some variation of today’s problems. The challenge of modern man really lies in identifying what might be just around the corner.
What new problems will government face and what new jobs will be created to address them? The crystal ball in the basement office of Public CIO headquarters revealed how the coming decades will transform the public-sector IT workforce.
Predictive Data Engineer
As valuable as information is, the value of a discovery is diminished almost to zero if there’s no follow-up action. Information is only valuable if it’s acted on. Predictive data engineers will ensure their organizations don’t squander data opportunities. Through this office, agencies will inspire behavioral change informed by data through a technique known as gamification.
Today’s data analytics platforms are like a beautiful car dashboard that displays measurements with extreme precision, but when the driver turns the steering wheel, there’s a six-month delay before anything happens, said Rajat Paharia, founder of gamification company Bunchball.
The talent of the predictive data engineer won’t just be in reading and analyzing data, Paharia said, but also in knowing how to work the buttons and levers of the organization to make change happen. Gamification, Paharia said, is the steering wheel that will allow an organization to change course immediately.
“What gamification does is it motivates behavior change using data as the raw material,” he explained. “It shouldn’t just be about looking at the data. It’s about taking it and using it to drive change to get you to a desired state or desired outcome.”
Too often, leaders try to change the system, when the focus should be on quickly changing the behavior of people, he said. If government wants more citizens to use a new passive data collection app or encourage more local startups to think about their communities, the predictive data engineer will use gamification to create incentives that are informed by data to make change happen fast.
“This role, I think, is about ingesting all this data but then using it in an automated, scalable, repeatable way,” Paharia said. “It can’t be teams of analysts looking at it and then figuring out what to do — that’s what computers are good at.”
Gamification becomes more powerful as more data points about individuals become available, Paharia said. If, for instance, it becomes clear that employees are driving to work during rush hour each day, this information can be used to create incentives for workers to drive at another time or to use a different route. If enough people are influenced, the impact is realized through reduced traffic, increased worker efficiency, reduced pollution, saved time and a happier community.
Autonomous Vehicle Manager
The dynamic of 21st-century roadways is changing. California, for instance, is preparing legislation for self-driving cars that could be in place as soon as 2015. Although it may take decades before autonomous vehicles hit the highways in large number alongside traditional vehicles, work is under way to make the technology roadworthy.
Edwin Olson is an assistant professor of computer science and engineering at the University of Michigan. His team placed fourth in a DARPA Urban Challenge, and his leadership of a winning team in a 2010 robotics competition led to the Department of Defense awarding him $750,000 for his work with autonomous robots. He’s now collaborating with the university and Ford on self-driving vehicle technology.
“We have a fleet of six 2014 Fusion hybrid cars that are driving autonomously on Ford proving ground areas and those will be driving in more places soon,” he said.
Just as governments today see little difference between trains and the rails they ride on, autonomous vehicles will one day be considered part of a city’s infrastructure.
How and when society will reach a world that is mostly self-driving cars will depend on society’s priorities, Olson said. Today’s fatality rate is one per 100 million miles driven, which is very good and will be “exceptionally difficult” to replicate with self-driving cars, said Olson. If society decides speed isn’t that important, then it becomes much easier to put self-driving cars on the road, he said. If convenience and quality of life gained by those who can’t drive becomes a higher priority, then a concession of more dangerous roads could also expedite adoption, he added.
One government role of the near future will be in fostering autonomous vehicle adoption, which could be accomplished by offering incentives like special magnetized highway lanes dedicated to self-driving cars, Olson said.
“It’s possible that cars might ultimately end up with a sort of flight-traffic control system that aircraft use, in which case there might be municipal managers doing things,” Olson said, although he considers it a long shot. Instead, he expects software will assume that role. “Even in the aviation space, that could be largely automated and if autonomous cars are accepted socially, then I would guess that automating a lot of that high-level management would also be socially acceptable.”
But even if software is put in charge of managing autonomous traffic flow, there would still be a human worker to adjust various thresholds for speed, safety and congestion levels, and to handle exceptions like rerouting traffic around construction. A highly automated system enabled by the Internet of Things would allow a city’s air- and land-based drones to co-exist with the city’s autonomous and manually driven vehicles, although human oversight would be necessary for when things go wrong, at least until true artificial intelligence is developed.
An autonomous vehicle manager would be responsible for identifying unknown objects in the city’s mapping interface and issuing citations to drivers who aren’t equipped with the appropriate autonomous vehicle stickers or to alert vehicle drivers who aren’t equipped with the proper sensors.
With work by Google and several large automakers advancing the self-driving vehicle space, many seem ready for self-driving cars to come soon, but Olson warns people to temper their expectations. The biggest challenge is getting an intelligent system to understand “weird scenarios,” he said. To a human, most weird scenarios aren’t weird at all, but easily explained through intuition. When a ball rolls into the street, a human driver will stop because he knows a child may soon run after it. They can hard-code those kinds of things into autonomous systems, Olson said, but the problem is that there are millions of one-off scenarios that machines continue to struggle with, but pose no challenge at all to the human brain.