As recently as a decade ago, most business decisions were based on very limited data. More recently, an explosion of data, a dramatic decline in the cost of processing power, and advances in machine learning have created the expectation that we will be able to capitalize on a data windfall to revolutionize most aspects of business. The data (from the Latin datum, a thing that is given) on which machine learning and analytics are built are taken as given and incontrovertible. But what managers, data scientists, and social scientists think of as data is in fact not given. It is the outcome of a process of measurement — an interaction between an observer, a technique or apparatus, and a context.

As every seasoned executive knows, asking for data to inform an important decision can set in motion a process in which facts and figures are colored, filtered, obscured, deleted, shaded, clipped, and even fabricated. In practice, the result depends on whom you ask for it (observer dependence), how you ask for it (frame dependence), and when and under what conditions you ask for it (context dependence). Equally, the process by which we ask people to report preferences, emotions, and perceptions can interfere with the underlying state, to the point where enquiries can create rather than report the states they refer to. “Are you happy?” triggers a complex set of considerations about self and others that make it overly simplistic to interpret “yes” as “s/he is happy.” Research suggests that individual dispositions and propensities are actually second-person specific (“happy toward whom?”; “signaling happiness in the presence of whom?”).

Business does not have a clear and cogent way of dealing with these limitations of data and measurement, and they are usually ascribed to error and noise. But these limitations are precisely the grist of quantum mechanics — one of the most successful predictive theories humans have developed to describe the world. Quantum mechanics has also produced a model of measurement that uses concepts like indeterminacy, superposition, entanglement, and observer-dependence very precisely. This model can be used in other contexts, with phenomena that do not occur on the space-time scales of quantum mechanics.