During the Cold War, the KGB was so good at identifying undercover CIA agents that officials worried that there was a highly placed mole in the agency. But as Jonathan Haslam, a professor at Princeton University, wrote earlier this fall, that wasn’t the case at all.
It turns out that the KGB was quite effective in mining data. The KGB gathered publicly available information on deployed U.S. Foreign Service personnel, along with observations and data from allied countries, analyzed it, and discovered how the agents’ housing and pay patterns were markedly different than those of the State Department officers the agents were posing as.
Data, Analysis, Insight
The KGB’s Yuri Totrov was able to find 26 independent indicators which invariably distinguished CIA agents from the genuine and otherwise harmless State Department field service officers, or FSOs.
For example:
- The CIA pay scale was significantly higher than for FSOs.
- FSOs could and typically did return home after a 3–4-year tour. Agents did not.
- When agents did return home, they did not show up in State Department listings.
- FSOs were always recruited before the age of 31. Agents could be older.
- Only real FSOs attended the three-month training session at the Institute for Foreign Service.
- Field agents might be reposted within a country. FSOs never were.
This wasn’t rocket science, and it didn’t require a high level mole as the more paranoid CIA chiefs suspected. No, the patterns and insights just popped right out of the data when the right analysis and investigative techniques were applied.
These same sort of insights are available to businesses, although they may be hidden somewhere in a pile of enterprise data. There are a number of descriptive analytic approaches, data mining and classification techniques, available to handily tackle this problem. The most well-known include clustering, market basket analysis, and decision trees, much of which can even be accomplished visually and without the need for specialized skill sets.
Date: December 11, 2015