While Big Data promised tremendous breakthroughs, achieving this promise has proved uneven at best. Either organizations are measuring the wrong things or the algorithms designed to help analyze data are incomplete or, worse, flawed. One rather public example of a Big Data fail was Google's Flu Trends (GFT); the project meant to track global flu cases based on the volume of flu-related searches which passed through the company's search engine. However, the parameters were so broad that the results were over-reporting incidents of flu by nearly 2:1.

In his 2013 paper, Big Data: New Tricks for Econometrics, Google's chief economist, Hal Varian, noted "computers are in the middle of most economic transactions" – to the point that the global economy can no longer function without them. In recent years, computers have moved beyond simple tools to facilitate the movement of money and are now taking a lead role in initiating and managing millions of transactions per day. Process virtualization, driven by Big Data, algorithms, and Artificial Intelligence (AI), has accelerated to the point that computers will dramatically alter the world we live in. What happens when Big Data meets AI? For starters, the confluence leads to new insights and efficiencies for business and customers; and while some occupations will be eliminated, entirely new fields will be created.