‘IT is Mission’: How Data Is Revolutionizing Intelligence

Intelligence agencies must stop viewing information technology as a back-office support service and instead elevate it to its rightful place as a mission-critical capability, argues Sean Roche, associate deputy director of digital innovation for the CIA.

“Stop treating IT like a service. Stop treating IT with the word ‘customer.’ Stop treating IT like it’s part of the admin portion of your organization,” he told the Department of Defense Intelligence Information Systems (DoDIIS) worldwide conference Aug. 2. “IT is mission.”

The distinction “changes the way we go about funding and prioritizing programs,” he said. It also has significant implications for the kinds of skills and talent intelligence agencies will need in the future, and for how systems are built, managed and designed.

For much of the past two decades, networks were the critical assets in intelligence – the ability to interact and quickly communicate across secure networks helped deliver data to the tactical edge more and more quickly. The time lag between intelligence shrunk from days to hours, minutes, even seconds. But in a data-centric world, intelligence is crossing into new territory. It is increasingly possible to predict likely outcomes, allowing national security leaders to make better decisions more quickly.

Open-source data analysis is one of the valuable data streams helping in that process, flooding the IC with new insights that must be tagged, collated, correlated, verified and analyzed. The volumes exceed human capacity, requiring intelligent systems, informed by data and able to learn as they go, to sort through all the signals to identify patterns that can point to truths.

Streaming social media analytics has become as valuable today as “the information we get clandestinely,” Roche said. The old notion that only information stamped “top secret” is of real value in intelligence has long since expired, he said. “Mining that rich vein of open source data that’s increasing rapidly in real time, doing sentiment analysis on it, is going to be more and more important,” he said.

IT Grows in Stature
Time and again, DoDIIS speakers returned to this theme: IT as not just an enabler, but critical to mission success. The IT enterprise is not just a network, but the underpinning of the IC’s future contributions to national security.

Under Secretary of Defense for Intelligence Marcel Lettre, acknowledged that the Pentagon “no longer controls the pace of change, especially in IT,” but emphasized that “technology is the secret sauce in the Third Offset strategy.” The Third Offset is Defense Secretary Ashton Carter’s long-term strategic vision to ensure the United States maintains technological superiority through sustained investment in emerging technologies like data science and machine intelligence.

Providing the underlying framework to enable that technological superiority is the primary reason Director of National Intelligence James Clapper is still on the job, he told a packed DoDIIS auditorium. Ensuring that IC ITE, the Intelligence Community Information Technology Enterprise, was sufficiently mature that it could not be reversed is not so much about providing infrastructure as it is about enhancing mission effectiveness across the IC.

“Data is a community property,” he said. “Integration simply means bringing the best and most appropriate resources from across the community together and applying them to our most challenging intelligence problems.”

And Marine Lt. Gen. Vince Stewart, director of the Defense Intelligence Agency (DIA) said the “decisive advantage over our adversaries in the future” will not be kinetic weapons or ground maneuver skills, but rather “this cognitive fight that matters most.”

Cross-Disciplinary Teams
Data and the ability to rapidly extract meaning from it is the means by which leaders expect to gain that decisive advantage, but achieving that nirvana will take more than technical acumen. Innovators across the IC are also looking at new organizational models and the kinds of integrated skill sets analysts and technologists will need to bring to the IC in the future.

“We need people who actually understand the math and can help validate and do things,” said Michael McCAbe, chief of applied research in DIA’s Chief Technology Office. “We need programmers and data experts who can move data, groom it, secure it. And you need users who actually understand what [the technology is] doing. And it’s more than analysis. It’s decision making, too.”

Cross-disciplinary teams are looking at what future skill sets DIA will need, what a data science career field and career path will look like, and how the technical skills of IT specialists and the analytic skills of analysts will begin to merge over time, he said.

“The technological skill set new analysts will need is significantly more than in the 1970s,” McCabe continued. “We’re not really at the full answer yet, but where we’re trending is this: IT services will be IT services and analysts will be analysts, but somewhere in the middle, you’re going to see IT reach out to the analyst and the analyst reach out to IT. You’ll have analysts who can code and coders who know how to think like an analyst. And I think we’re going to hit probably four different user groups on that spectrum.”

Already, he said, this is happening. Where the breakthroughs are taking place, it’s because the walls between analysts and coders are coming down, and the collaboration is increasing.

“Analyst cells, divisions and branches will include teams with that full spectrum of skills,” McCabe said. DIA has assembled multidisciplinary innovation pods – analysts, programmers and human resources specialists to help conceive of the skill sets and training and career paths future analysts will need. The agency is also “trying to do experiments to inform what new data sets should look like in the future,” he said, a process that likewise demands both “technological and also analysis skills.”

Across the agency, he continued, “we’ve got a bunch of users who want to get better at analytics, want to get better at decision making, tracking and monitoring, statistics and metrics.”

Roche cited the same phenomenon at CIA: “What we’re finding is most successful is the data analyst sitting with a programmer, sitting with a mission specialist, and often with an operator.”

Indeed, this pattern is repeating itself wherever large data sets and the desire to unlock their secrets has emerged, said Stan Tyliszczak, chief engineer at General Dynamics Information Technology. “We’ve seen it in healthcare, medical research and public safety, not just Intelligence or DoD: : Data analytics is a mission function, not an IT support function,” he says. “Sure, IT managers can help set up a support function or acquire a software tool. But big data solutions come out of the mission side of an organization. Increasingly, we’re seeing the mission specialists becoming IT savvy, and the IT staff bringing a new perspective to mission analysis.”

For example, the National Institutes for Health (NIH) found it’s easier to teach medical researchers how to program than it is to teach programmers the necessary medical knowledge to extract information from a data pool. “If you look at NIH’s Health IT model, they have these cells and the researchers become IT specialists, and the people who have IT knowledge gradually learn more about health.”

So just as agencies are discovering they can extract new knowledge by aligning disparate data sources, they’re also discovering the best way to do it is by assembling people with disparate skill sets into teams and then setting them loose on these complex data problems.

The long-term implications are profound. Just a few years ago, IT was part of the CIA’s administrative directorate, Roche said. IT was a support service, just like human resources and accounting departments. “Today, we realize it is inextricably linked to our ability to respond.”

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