Campbell's Law is the observation that once a metric has been identified as a primary indicator for success, its ability to accurately measure success tends to be compromised.Content Continues Below
In a paper entitled “Assessing the Impact of Planned Social Change,” American social scientist Donald T. Campbell described the effect of quantitative measurements on decision-making processes this way:
"The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor."
Campbell used crime rate as an example in his paper. He pointed out that a decrease in a city’s crime rate may not demonstrate a true reduction in the number of crimes that have been committed, but may simply reflect how the police force has changed procedures to lower the number. They may have decided, for example, to change which police encounters need to be formally recorded. They may also have downgraded some crimes to less serious classifications.
In the era of big data, Campbell’s Law is often cited as a warning about the dangers of making data-driven decisions based on a single key performance indicator (PKI). For example, a sales manager who requests monthly reports on the number of calls each sales representative makes may inadvertently cause problems for himself if he holds this metric up as an important criteria for bonuses. According to Campbell's Law, once the sales people know they are being evaluated on this particular metric, they may put more effort toward making sales calls and spend less time on other important tasks, such as trying to close sales.
See also: data-driven disaster