Anyone with access to a Web browser can get summaries of billions of keyword searches, and this information is highly predictive of present and future economic activity, such as housing purchases and prices. Mobile phones, automobiles, factory automation systems and other devices are routinely instrumented to generate streams of data on their activities, making possible an emerging field of "reality mining" to analyze this information. Manufacturers and retailers use radio-frequency identification (RFID) tags to deliver terabits of data on inventories and supplier interactions and then feed this information into analytical models to optimize and reinvent their business processes.

Much of this information is generated for free, by computers, and sits unused, at least initially. A few years after installing a large enterprise resource planning system, it is common for companies to purchase a "business intelligence" module to try to make use of the flood of data that they now have on their operations. As Ron Kohavi at Microsoft memorably put it, objective, fine-grained data are replacing HiPPOs (Highest Paid Person's Opinions) as the basis for decision-making at more and more companies. For example:

-- Enologix has used this approach to help Gallo vineyards accurately predict the wine ratings that Robert Parker would give to various new wines

-- UPS has mined data on truck delivery times to develop a new routing method

-- Match.com as even developed new algorithms for matching men and women for dates

For each innovation, analysts drew on new measurement technologies to supplant human experts who relied more on intuition. However, for all its strengths, measurements have a shortcoming. They cannot determine causality. (A simple example: Shoe sizes and readings scores are correlated for school children, but one does not cause the other; instead, they both reflect a third variable, which is age.) Fortunately, science has a second powerful tool designed precisely to address questions of causality.

That tool is called experimentation.

AN EXPERIMENT EVERY SECOND

Science has been dominated by the experimental approach for nearly 400 years. Running controlled experiments is the gold standard for sorting out cause and effect. But experimentation has been difficult for businesses throughout history because of cost, speed and convenience. It is only recently that businesses have learned to run real-time experiments on their customers. The key enabler was the Web.

Consider two "born-digital" companies, Amazon and Google. A central part of Amazon's research strategy is a program of "A-B" experiments where it develops two versions of its website and offers them to matched samples of customers. Using this method, Amazon might test a new recommendation engine for books, a new service feature, a different check-out process, or simply a different layout or design. Amazon sometimes gets sufficient data within just a few hours to see a statistically significant difference.