Innovation Patterns and Big Data

Big data is often generated by devices configured for collection based on the occurrence of events. Events can occur based on scan rates (collect yield from a combine every five seconds), from status change (pitch is a strike, count is now 3-1), or from rule execution (SandP 500 VIX > 24.5). Domains such as finance and physics, where big data was first collected and analyzed, were the first to create new theories and innovative new markets, and those innovations are now finding their way into domains where data collection has recently become feasible. For example, the financial options pricing method known as Black-Scholes is now used to estimate the future value of baseball players. These innovations are often the answer to questions formulated with innovation theory. Innovation theory would suggest new domains where big data is now available, and it.