Managers and entrepreneurs make abundant use of data and measurements, even in innovation management. In many cases, they use or refer to Key Performance Indicators (KPIs), to make investment decisions or monitor decisions. These KPIs are included in reports, dashboards, tableau de boards, or scorecards. They might be chosen based on past practices, on benchmarking - what other companies do - or on other unclear criteria. The KPIs entrepreneurs and managers use are important because their choice can affect the quality of the decisions they make. When they make their estimates using data analyses or experiments, they should behave more like scientists and pick metrics that are strategically congruent with the problem they are trying to solve, valid with respect to the hypotheses they wish to test, reliable across measurements and overtime and specific to the context. We also contend that entrepreneurs and managers should clearly understand the cause-effect relationships built into their predictions, distinguishing between leading and lagging indicators, and emphasizing leading indicators, which are those that really matter under conditions of uncertainty. Paying more attention to leading indicators would allow entrepreneurs and managers to update their beliefs and estimates, and change the course of action timely and effectively. In innovation management, as in management in general, lagging indicators are typically output-oriented, easy to measure, but hard to improve or influence. Examples of lagging indicators are the fraction of yearly revenues attributable to new products, or time to market, that is the number of months or years elapsing from concept to commercialization. Leading indicators are instead typically input or process-oriented. Examples of leading indicators are the amount and the degree of reusability of the knowledge generated in the innovation process, and the degree and earliness of customer involvement in the innovation process. They're often more difficult to measure but easier to influence. Lagging indicators are ex-post measures, while leading indicators are ex-ante measures. Leading indicators are often predictors of lagging indicators, that is they are causally linked - directly or indirectly - or at least strongly correlated. Contingent upon the decisions to be made and the pursued innovation strategy, entrepreneurs and managers should pick a set of leading and lagging indicators that allow to test their hypotheses and make good predictions. Two criteria are of particular importance to improve the quality of the inference: validity and reliability. The following chart illustrates what validity and reliability mean. Let's assume that an entrepreneur has formulated a hypothesis, as illustrated in the previous modules, and that s/he sets out to test it. The hypothesis is an “if… then” statement that connects two possible events or facts, and contains a prediction. In order to be testable, the events or facts that the hypothesis connects must be observable, and the prediction must be measurable. Therefore, the entrepreneur needs to pick measures of the events or facts included in the prediction, so that the hypothesis can be transformed into meaningful data. The two key properties of such measures are: one, validity, that is the extent to which the picked metric measures all and only the relevant aspects or dimensions of the events or facts the hypothetical statement predicts. Two: reliability, that is the extent to which the picked metric and/or measurement method provides consistent information about events or facts the hypothetical statement predicts, devoid of measurement error or variability.
