I’ve been reading the fascinating book, The Dead
by David Hoffman. The book recounts the height of Cold War
tensions driving the nuclear arms race between the Soviet Union and United
States in the 1980s, and in particular mentions a Soviet system called Dead
Hand. The Soviets greatly feared a preemptive US attack that would disable
Communist party leadership and internal communications, and effectively prevent
the USSR from launching a counterattack. While laughably reminiscent of
playground antics 30 years later, mutually assured destruction was a key tenet
of nuclear war, and a strategy that gave one side even a slight advantage was
perceived as incredibly threatening.

How it was supposed to work

Dead Hand was designed to automate a retaliatory strike if
Soviet leadership were disabled, and would automatically launch a series of communications
missiles, which would then launch all available nuclear weapons at the United
States. While details of the system are still mysterious, apparently the Soviet
military was unwilling to enable the system in a fully automated mode,
preferring one last human check, from deep within a hardened bunker, before
control of a retaliatory strike was handed over to automation.

One interview in the book suggests that no one knew if the
soldiers trained to activate Dead Hand would actually perform their duties in a
real nuclear war and, for some, this was a positive characteristic of the
system. The author notes that some of the designers found it appealing that in
the heat of nuclear Armageddon a lone soldier might decide to spare half the
world, rather than launching a strike that would effectively nuke the other

While it’s unlikely any Big Data initiatives we are dealing
with have the capacity to end life as we know it, we’re often enamored with a
similar concept of completely automated processes. For some in marketing, the
ultimate expression of Big Data is a department with a CMO, and perhaps an
administrator to write checks to the various providers, completely automating
marketing functions.

The human touch

Most of us have fallen victim to a mistakenly released or
poorly targeted Big Data-driven marketing campaign. One of my favorites was an
advertisement for “male enhancement” on a major global newspaper’s
paid online site. Whatever fraction of a penny that might have resulted from a
click on the ad, certainly wasn’t worth the subsequent brand damage. Similarly,
highly automated stock trading systems have triggered a minor market crash and
resulted in a suspension of trading. Even in the highly disciplined scientific
fields, data-driven analyses predicted rampant global cooling a few decades
ago, while current science points toward the opposite trend.

It’s easy to cast this as “machines gone wild”
fear mongering, but just as the Soviets added one final human element to their
Dead Hand system, it’s worth adding a human element to Big Data-driven
applications as well. Big Data is based on complex, layered models, each with
its own set of assumptions. Even the best models struggle to accommodate a
rapidly changing macroeconomic environment or predict disruptive market
changes. Neither humans nor machines are infallible, but combining the two
makes for a far more robust system than relying totally on one, especially when
business strategy, customer perception, and financial resources are at stake.