For some reason, the idea hit me on a chilly afternoon in northern Virginia while I was heading out to a DC United match with my oldest son. The boy loves his sports, for sure, and I started thinking about how much awareness he had already acquired on the topic. For months, when he got his precious iPad time, he would immediately go to my sports app, theScore, and start memorizing. He probably still knows the score of last year’s Tennessee-Vanderbilt football game as I type this. (Note: I just checked, he does.)

My five year-old’s ability to find sports scores on an iPad contrasts rather sharply with my inability to rapidly sift through mountains of intelligence data from my desk in the Pentagon. This leads to an intriguing question: How might the sports information model be applied to intelligence dissemination? I recognize there are all sorts of hurdles that would need to be overcome, but suspend disbelief and bear with me.

Sports Information Delivery …Who Won the Game?

Consider the 24/7 sports information machine for a moment. Pick any device — smart phone, tablet, laptop, watch — and there are dozens of ways to find a score in a cricket match on the other side of the planet. Right there on ESPN…Moors Sports Club 574, Badureliya Sports Club 221 & 151/6 in Hambantota, Sri Lanka. I found it in under 60 seconds despite having never before looked up a cricket score. Put another way, I accessed obscure data from an unknown source from across the globe in near real-time without any specialized knowledge.

That’s a simple example, so let’s dig deeper. With apologies to cricket fans, I’ll switch to football and call up that Tennessee-Vandy game on my phone. I get score, team records, standings, quarter-by-quarter breakdown, scoring plays, top performers, team statistics, location, attendance, and closing odds. That’s on the Matchup tab. If I swipe over to Stats, I have full statistics for both teams, and on the Plays tab I’ve got a description of every play in the game. This is a generic scores app on my phone, so it represents the lower end of what’s available. If I open a web browser and head to ESPN, I get a lengthy recap, 18 photos, 32 videos, and 125 archived comments.

I can do the same for almost any game in the past decade, and if you’re an average sports fan, none of this is surprising. You expect access to all this.

Still, we’re only scratching the surface. If you want to follow a particular game, Gamecast applications let you access the same info in near real-time, plus interactive graphics and integrated social media feeds. You may be able to get live audio and video, too.

Can’t devote your full attention to the game? No worries, with a few taps you can set up automated notifications for a slew of criteria: game start, scoring changes, significant events, end of quarter/period/half, final score, and more. On a more enduring basis, you can follow your favorite leagues, teams, players, and on and on.

For in-depth analysis, you’re not stuck with whoever has the beat in your local paper. You can easily access the full breadth and depth of reporting from traditional outlets and new media. With social media empowering the average fan to send a good read to their friends in a matter of seconds, the cream of the crop rises to the top, as does an understanding of what the sports fan collective is interested in.

Taken as a whole, this represents a 24/7 flood of data funneled down to what you want, when you want, and wherever you want it in near-real time via dizzying customization options. Yet it is so intuitive and easy that a five year-old can grasp it.

Nor is any of this particularly unique to sports. This is the reality of the modern information landscape.

A Vision for the Intelligence Community

So with an annual budget hovering in the $50 billion range ($50.4B in FY15, $53.9B requested in PB16), plus more spent by the services, why can’t our intelligence community (IC) provide the same? Is it possible for the IC to approximate ESPN’s intuitive interface in its own data management systems? What might intelligence look like using the sports information model?

Let’s start with the possibilities around an open-architecture device-agnostic approach. National-level political analyst with a desktop? Why yes, he can see how many times Vladimir Putin sneezed last week. Marine platoon with tablets moving into a natural disaster area on an Osprey? Video from the Reaper monitoring the landing zone shows the site remains clear, but a Triton with a wider view reveals a concentration of refugees five miles to the northeast that is heading in that direction. SOF team with Android handsets lying in wait outside a target airfield? A COMINT analyst sitting back in DC just chatted that the airfield has flown its final sorties for the day and is calling its patrols inside the wire for the night. F-35 pilot approaching hostile airspace while glancing at a multi-function display (MFD)? A Rivet Joint Raven just posted a message that the SA-20/GARGOYLE at 12 o’clock survived that strike twenty minutes ago, because its engagement radar is back up.

While there are a lot of possibilities to collect, combine, analyze, and disseminate data in the sports statistics and reporting realm, it is dwarfed by the magnitude of what is generated by the IC every day. This poses a greater challenge and potentially a greater reward. So how to filter out what you need? The personalized user interface is the key. The political analyst whose focus is senior Russian leadership has downloaded programs that give him quick access to detailed biographical information and set up push notifications for new reporting. The Marine platoon commander has loaded her tablet with apps that allow her to tap into regional imagery/video feeds, access social media updates, and coordinate with other units in the disaster zone. The SOF team commander has turned on updates for anything and everything on that airfield, plus he’s logged into his mission’s private discussion forum where he picked up that pivotal COMINT update. And the F-35 pilot has made sure her MFD was synched into the relevant threat streams for her mission, to include that troublesome SA-20, while filtering out the noise.

Also consider the metadata generated in this framework. If twelve thousand people are following the not-dead SA-20 site (it went viral, if you will), the IC knows it is important and can vector additional assets to cover it. As the speed of modern warfare accelerates, the ability to autonomously optimize collection assets may represent a critical operational advantage.

These examples are all similar, with a user employing something akin to a Gamecast to follow a specific entity. However, just as there are sports fans who only want last night’s results, plenty of operators and analysts just want a general idea of what’s going on in their theater or field of expertise. This may look like a magazine-style news aggregator (feedly, flipboard), a discovery app (digg, reddit), or a sports scoreboard. All, of course, customizable to make sure you only get the information you are interested in and in a format you like. In the IC, you could plug into “highlight” feeds on the Ukraine conflict, anti-tank missiles, global economic trends … let your imagination run wild.

I could keep making up examples, but I’ll let you fill in the blanks. In fact, I hope you’re thinking, “I wish I would have been able to access <DATA> on my <DEVICE> when I was at <LOCATION> and trying to <TASK>.” Frankly, that’s the entire point. The people in the Pentagon who make decisions about these kinds of tools have minimal insight into what you would actually use them for. We have ideas (some might even be right) but it should be a matter of enabling innovation at the local level.

Along those lines (if you really want to push the envelope), imagine loosening the reins of centralized control and giving units a dedicated information support specialist tasked with coding and fielding specialized apps which tap into this architecture. Units would figure out what worked and what didn’t, and share that information across the force, much as they do already with tactics, lessons learned, and best practices.

Into the Future

Is the thought of such a system naïve? Maybe, but many efforts within the Department of Defense, such as the Army’s Nett Warrior and MACE projects, are already moving in this direction. I don’t mean to trivialize the hurdles, but I’m frustrated by them just the same. Cross-network classification issues, proprietary network architectures, mobile security challenges, cyber threats, and stovepiped information channels (just to name a few) are all real. They represent major obstacles that would need to be overcome, but they should not be used as excuses to accept the status quo.

What the sports information architecture shows us is that this technology exists right now. There is nothing cosmic about it. Our cultural, institutional, and regulatory limitations are holding us back. This is why we need visions … so we know what problems need to be solved and why. An intuitive, customizable interface to quickly and easily access intelligence on any device, in any place, at any time … perhaps while sipping a frosty beverage at a cricket match in Sri Lanka. That’s something worth fighting for.

By the way, DC United won the game my son and I went to, beating Montreal 1-0 on a lovely tap by Jairo Arrieta. The boy and I stood up to cheer, and my phone buzzed with the scoring notification before we sat down.

The author has fifteen years of experience at multiple levels in the intelligence community as an Air Force intelligence officer and Air Force civilian analyst. His thoughts are his own and in no way imply or represent an official position of the U.S. Air Force, the intelligence community, or any other government entity.