A rather famous senior member of the intelligence community is widely reported to have once quipped words to effect that “there is no such thing as information overload – only poor analytical strategy.” This phrase, whether apocryphal or otherwise, for whatever its original intention has unfortunately often become shorthand for many managers seeking for their analysts to “process” more traffic, or faster, or otherwise increase some key productivity metric which may or may not result in good analysis.
Emerging technologies and the changing dynamics of interactions between individuals are however provably creating greater volumes of information: on the web, in open source publications, and in new communications technology. So too the increasing pressures of globalization as these technologies and interactions defuse to new geographies and cultures, each with characteristic patterns of usage.
By way of example, consider the Chinese New Year. The importance of personal relationships in Chinese culture, particularly among an increasing mobile Chinese population, creates new demands for ways to keep in touch with family and friends. This is especially highlighted during major holidays. Thus, it is not surprising to note the following Smart Mobs entry regarding short message service (SMS) usage volume during the Chinese New Year – a staggering 12.6 billion texts. This originated from the two largest mobile carries. Official statistics reportedly reflect as 393 million mobile phone users.
The sheer volume of transaction data contained within these messaging patterns would reveal much about individual social networks: activity patterns, strength of relationships, clique formation, and other indicators of note. Examining these relationships, however, would be a monumental task – even for a repressive Communist government with unlimited police powers and no system of checks or balances to restrain surveillance of its own people.
As new social networking technologies are introduced, this sea of potential data grows by the hour. For example, consider the potential utility of data contained in the social networking service “a small world”. This service, famous after its profile in Wired magazine featuring a member of the extended bin Laden clan, is favoured by many users for its elitism and robust gatekeeping protocols which act as formidable barriers to entry. While most if not all of its users are younger and more technology savvy and European, and while potentially wealthy now are of likely little interest to any intelligence service, they do represent the next generation of what once was called the “jet set”. These are business, government, and technology leaders of tomorrow. The robust accumulation of data regarding their interests, interactions, and social relationships could lead to very interesting future analysis if ever harnessed – and one cannot assume it will be an intelligence service, let alone a friendly service, that would ever seek out such a task.
These challenges will demand fundamentally different conceptions of how data moves, how it must be protected, and how it could be analyzed in the future. It may likely demand far different organizations and talents than currently found in the profession. This, like most intelligence functions, is a brutally competitive race, and the first to a workable solution will enjoy an edge that will be increasingly difficult to overcome as the rate of overload itself increases.
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