Debating Big Data for Intelligence

I’m always afraid of engaging in a “battle of wits” only half-armed.  So I usually choose my debate opponents judiciously.

Unfortunately, I recently had a contest thrust upon me with a superior foe: my friend Mark Lowenthal, Ph.D. from Harvard, an intelligence community graybeard (literally!) and former Assistant Director of Central Intelligence (ADCI) for Analysis and Production, Vice Chairman of the National Intelligence Council – and as if that weren’t enough, a past national Jeopardy! “Tournament of Champions” winner.

As we both sit on the AFCEA Intelligence Committee and have also collaborated on a few small projects, Mark and I have had occasion to explore one another’s biases and beliefs about the role of technology in the business of intelligence. We’ve had several voluble but collegial debates about that topic, in long-winded email threads and over grubby lunches. Now, the debate has spilled onto the pages of SIGNAL Magazine, which serves as something of a house journal for the defense and intelligence extended communities.

SIGNAL Editor Bob Ackerman suggested a “Point/Counterpoint” short debate on the topic: “Is Big Data the Way Ahead for Intelligence?” Our pieces are side-by-side in the new October issue, and are available here on the magazine’s site.

Mark did an excellent job of marshalling the skeptic’s view on Big Data, under the not-so-equivocal title, Another Overhyped Fad.”  Below you will find an early draft of my own piece, an edited version of which is published under the title A Longtime Tool of the Community”:

Visit the National Cryptologic Museum in Ft. Meade, Maryland, and you’ll see three large-machine displays, labeled HARVEST and TRACTOR, TELLMAN and RISSMAN, and the mighty Cray XMP-24. They’re credited with helping win the Cold War, from the 1950s through the end of the 1980s. In fact, they are pioneering big-data computers.

Here’s a secret: the Intelligence Community has necessarily been a pioneer in “big data” since inception – both our modern IC and the science of big data were conceived during the decade after the Second World War. The IC and big-data science have always intertwined because of their shared goal: producing and refining information describing the world around us, for important and utilitarian purposes

What do modern intelligence agencies run on? They are internal combustion engines burning pipelines of data, and the more fuel they burn the better their mileage. Analysts and decisionmakers are the drivers of these vast engines, but to keep them from hoofing it, we need big data.

Let’s stipulate that today’s big-data mantra is overhyped. Too many technology vendors are busily rebranding storage or analytics as “big data systems” under the gun from their marketing departments. That caricature is, rightly, derided by both IT cognoscenti and non-techie analysts.

I personally get the disdain for machines, as I had the archetypal humanities background and was once a leather-elbow-patched tweed-jacketed Kremlinologist, reading newspapers and HUMINT for my data. I stared into space a lot, pondering the Chernenko-Gorbachev transition. Yet as Silicon Valley’s information revolution transformed modern business, media, and social behavior across the globe, I learned to keep up – and so has the IC. 

Twitter may be new, but the IC is no Johnny-come-lately in big data on foreign targets.  US Government funding of computing research in the 1940s and ‘50s stretched from World War II’s radar/countermeasures battles to the elemental ELINT and SIGINT research at Stanford and MIT, leading to the U-2 and OXCART (ELINT/IMINT platforms) and the Sunnyvale roots of NRO.

In all this effort to analyze massive observational traces and electronic signatures, big data was the goal and the bounty.

War planning and peacetime collection were built on collection of ever-more-massive amounts of foreign data from technical platforms – telling the US what the Soviets could and couldn’t do, and therefore where we should and shouldn’t fly, or aim, or collect. And all along, the development of analog and then digital computers to answer those questions, from Vannevar Bush through George Bush, was fortified by massive government investment in big-data technology for military and intelligence applications.

In today’s parlance big data typically encompasses just three linked computerized tasks: storing collected foreign data (think Amazon’s cloud), finding and retrieving relevant foreign data (Bing or Google), and analyzing connections or patterns among the relevant foreign data (powerful web-analytic tools).

Word CloudThose three Ft. Meade museum displays demonstrate how NSA and the IC pioneered those “modern” big data tasks.  Storage is represented by TELLMAN/RISSMAN, running from the 1960’s throughout the Cold War using innovation from Intel. Search/retrieval were the hallmark of HARVEST/TRACTOR, built by IBM and StorageTek in the late 1950s. Repetitive what-if analytic runs boomed in 1983 when Cray delivered a supercomputer to a customer site for the first time ever.

The benefit of IC early adoption of big data wasn’t only to cryptology – although decrypting enemy secrets would be impossible without it. More broadly, computational big-data horsepower was in use constantly during the Cold War and after, producing intelligence that guided US defense policy and treaty negotiations or verification. Individual analysts formulated requirements for tasked big-data collection with the same intent as when they tasked HUMINT collection: to fill gaps in our knowledge of hidden or emerging patterns of adversary activities.

That’s the sense-making pattern that leads from data to information, to intelligence and knowledge. Humans are good at it, one by one. Murray Feshbach, a little-known Census Bureau demographic researcher, made astonishing contributions to the IC’s understanding of the crumbling Soviet economy and its sociopolitical implications by studying reams of infant-mortality statistics, and noticing patterns of missing data. Humans can provide that insight, brilliantly, but at the speed of hand-eye coordination.

Machines make a passable rote attempt, but at blistering speed, and they don’t balk at repetitive mindnumbing data volume. Amid the data, patterns emerge. Today’s Feshbachs want an Excel spreadsheet or Hadoop table at hand, so they’re not limited to the data they can reasonably carry in their mind’s eye.

To cite a recent joint research paper from Microsoft Research and MIT, “Big Data is notable not because of its size, but because of its relationality to other data.  Due to efforts to mine and aggregate data, Big Data is fundamentally networked.  Its value comes from the patterns that can be derived by making connections between pieces of data, about an individual, about individuals in relation to others, about groups of people, or simply about the structure of information itself.” That reads like a subset of core requirements for IC analysis, whether social or military, tactical or strategic.

The synergy of human and machine for knowledge work is much like modern agricultural advances – why would a farmer today want to trudge behind an ox-pulled plow? There’s no zero-sum choice to be made between technology and analysts, and the relationship between CIOs and managers of analysts needs to be nurtured, not cleaved apart.

What’s the return for big-data spending? Outside the IC, I challenge humanities researchers to go a day without a search engine. The IC record’s just as clear. ISR, targeting and warning are better because of big data; data-enabled machine translation of foreign sources opens the world; correlation of anomalies amid large-scale financial data pinpoint otherwise unseen hands behind global events. Why, in retrospect, the Iraq WMD conclusion was a result of remarkably-small-data manipulation.

Humans will never lose their edge in analyses requiring creativity, smart hunches, and understanding of unique individuals or groups. If that’s all we need to understand the 21st century, then put down your smartphone. But as long as humans learn by observation, and by counting or categorizing those observations, I say crank the machines for all their robotic worth.

Make sure to read both sides, and feel free to argue your own perspective in a comment on the SIGNAL site.

Tearing the Roof off a 2-Terabyte House

I was home last night playing with the new Kinect, integrating it with Twitter, Facebook, and Zune. Particularly because of the last service, I was glad that I got the Xbox 360 model with the 250-gigabyte (gb) hard disk drive. It holds a lot more music, or photos, and of course primarily games and game data.

So we wind up with goofy scenes like my wife zooming along yesterday in Kinect Adventures’ River Rush – not only my photo (right) but in-game photos taken by the Kinect Sensor, sitting there below the TV monitor.

Later as I was waving my hands at the TV screen, swiping magically through the air to sweep through Zune’s albums and songs as if pawing through a shelf of actual LP’s, I absent-mindedly started totting up the data-storage capacity of devices and drives in my household.  Here’s a rough accounting:

  • One Zune music-player, 120gb;
  • 2 old iPods 30gb + 80gb;
  • an iPad 3G at 16gb;
  • one HP netbook 160gb;
  • an aging iMac G5 with 160gb;
  • three Windows laptops of 60gb, 150gb, and 250gb;
  • a DirecTV DVR with a 360gb disk;
  • a single Seagate 750gb external HDD;
  • a few 1gb, 2gb, and a single 32gb SD cards for cameras;
  • a handful of 2gb, 4gb, and one 16gb USB flash drives;
  • and most recently a 250gb Xbox 360, for Kinect. 

All told, I’d estimate that my household data storage capacity totals 2.5 terabytes. A terabyte, you’ll recall, is 1012 bytes, or 1,000,000,000,000 (1 trillion) bytes, or alternately a thousand gigabytes.

Continue reading

Mix, Rip, Burn Your Research

You’ve done research; you’ve collected and sifted through mounds of links, papers, articles, notes and raw data. Shouldn’t there be a way to manage all that material that’s as easy and intuitive as, say, iTunes or Zune – helping you manage and share your snippets and research the way you share and enjoy your music?

Continue reading

Your choice, Dataviz as event or book

A friend wrote asking if I could make it to an event happening this week near DC. I can’t make it, but fortunately he also mentioned as consolation that he has a cool new book on the cusp of release – and I’ve now ordered my copy.

The Friend: legendary visualization and HCI guru Ben Shneiderman (Wikipedia entry). Ben is a computer-science professor at the University of Maryland and the founder of its well-known Human-Computer Interaction Laboratory (HCIL), as well as an ACM Fellow and AAAS Fellow.  He has done government a million favors over the years, consulting for agencies, including his recent work on the Recovery.gov site to help that platform of data – from hundreds of thousands of sources – organize, host, and visualize the data for millions of visitors.  I first got to know Ben through his support for better intelligence analysis – he helped invent a longtime intelligence analytics tool, Spotfire (see his article “Dynamic queries, starfield displays, and the path to Spotfire“).  Ben’s also well-known for his award-winning 2002 book Leonardo’s Laptop: Human Needs and the New Computing Technologies, which I enjoyed and still think about when brainstorming new techie toys.

Continue reading

Free Tools for the New Scientific Revolution

Blogs are great for supplementing real-life events, by giving space and time for specific examples and links which can’t be referenced at the time. I was invited to give a talk last week at the first-ever NASA Information Technology Summit in Washington DC, and the topic I chose was “Government and the Revolution in Scientific Computing.” That’s an area that Microsoft Research has been focusing on quite a bit lately, so below I’ll give some examples I didn’t use at my talk.

One groundrule was that invited private-sector speakers were not allowed to give anything resembling a “sales pitch” of their company’s wares. Fair enough – I’m no salesman.  The person who immediately preceded me, keynoter Vint Cerf, slightly bent the rules and talked a bit about his employer Google’s products, but gee whiz, that’s the prerogative of someone who is in large part responsible for the Internet we all use and love today.

I described in my talk the radical new class of super-powerful technologies enabling large-data research and computing on platforms of real-time and archival government data. That revolution is happening now, and I believe government could and should be playing a different and less passive role. I advocated for increased attention to the ongoing predicament of U.S. research and development funding.

Alex Howard at O’Reilly Radar covered the NASA Summit and today published a nice review of both Vint’s talk and mine.  Some excerpts: Continue reading

Enabling Eureka via Citeability

The story of Archimedes resonates with everyone, because we all regularly feel that rush of excitement that he famously felt when discovering the principle of water displacement: “Eureka!” he shouted, “I have found it!”

Whether it’s car keys or the perfect birthday present for a loved one, we know that feeling. But how often do you feel like shouting “Eureka” when you’re surfing the web looking for a particular piece of government information?

Continue reading

To fix intelligence analysis you have to decide what’s broken

“More and more, Xmas Day failure looks to be wheat v. chaff issue, not info sharing issue.” – Marc Ambinder, politics editor for The Atlantic, on Twitter last night.

Marc Ambinder, a casual friend and solid reporter, has boiled down two likely avenues of intelligence “failure” relevant to the case of Umar Farouk Abdulmutallab and his attempted Christmas Day bombing on Northwest Airlines Flight 253.  In his telling, they’re apparently binary – one is true, not the other, at least for this case.

The two areas were originally signalled by President Obama in his remarks on Tuesday, when he discussed the preliminary findings of “a review of our terrorist watch list system …  so we can find out what went wrong, fix it and prevent future attacks.” 

Let’s examine these two areas of failure briefly – and what can and should be done to address them.

Continue reading

Total Recall for Public Servants

MyLifeBits is a Microsoft Research project led by the legendary Gordon Bell, designed to put “all of his atom- and electron-based bits in his local Cyberspace….MyLifeBits includes everything he has accumulated, written, photographed, presented, and owns (e.g. CDs).” 

SenseCam - Click to enlarge

Among other technical means, Bell uses the SenseCam, a remarkable prototype from Microsoft Research.  It’s a nifty little wearable device that combines high-capacity memory, a fisheye lens passively capturing 3,000 images a day, along with an infrared sensor, temperature sensor, light sensor, accelerometer, and USB interface. My group has played with SenseCam a bit, and shared it with quite a few interested government parties and partners. More info on SenseCam here, and more on its parent Sensors and Devices Group in MSR.  

Continue reading

Education for Information Security in a Connected World

Much of what I work on involves technologies which address information security and cyber security. So I have to ask, Who is training our next generation of technologists? And are those educators doing enough to focus on the dynamically changing demands of Information Security?

Those fundamental questions took me to Chicago recently, to take part in a roundtable discussion sponsored by DeVry University, “The Demand for Information Security in a Connected World.”

Continue reading

Gunning the Microsoft Semantic Engine

New Bing Maps Beta with embedded data layers from Twitter and other social feeds, click to enlarge screenshot

There’s a lot of information on the Internet already. Every day, more is added – a lot more. And while there are a concomitant number of new analytic or sense-making tools on the web, they butt up against the fact that the data – the all-important data – is held in multiple places, formats, and platforms.

How are we going to deal with all this? One approach is almost mechanical: ensuring that datasets can be accessed commonly, as in our new Microsoft Dallas platform associated with the Windows Azure cloud platform.  In the government realm, the anticipated reliance on “government-as-a-platform” (a meme popularized by Tim O’Reilly) holds promise in allowing somewhat aggregated datasets, openly accessible.

Continue reading

Follow

Get every new post delivered to your Inbox.

Join 6,241 other followers

%d bloggers like this: