There have been so many news stories and hype about big data, and how it can transform your business, that it begins to sound like something you would find. It is often portrayed as the answer to all things that cause problems for organizations. There are promises that it will identify the right customers for marketing campaigns and help academic institutions select the perfect students for their admissions process. Don’t let skepticism turn you into a cynic. It is indeed true that having more data, especially historical data, often will help model predictions be more accurate. Using additional sources of data, such as social networks, will help organizations make better predictions about customer choices and preferences, because all of us are influenced to some degree by those in our social network, either the physical or the virtual one. There is danger in not taking the time to know how much weight to give to this large amount of newly available information, when that information is compared to all of the other attributes that affect a person’s decision‐making process. Returning to the poor customer service and my social network example, it is clear that the people in my social network are now more likely to cancel services, but how much more likely? One company that takes many different metrics and creates a single aggregate score is Klout; the higher your Klout score, the more influential you are online. Klout uses comments, mentions, retweets, likes and so on to create this score. The company is able to measure online influence only because that is all the data to which it has access. The best advice is to be an informed data user. Having seven years of customer history instead of three in August 2008 would not have helped in any way to predict people’s upcoming spending habits in the United States. In just a few weeks, the financial markets were going to collapse and several large investment fi rms were going to declare bankruptcy or be purchased at fi re sale prices. The U.S. government would begin a massive bailout of the financial markets that would, in some way, affect everyone in the industrialized world. No amount of data would have helped. No amount of data analysis modeling of the preceding months’ spending could forecast what the months after the bailout would look like for the average consumer. In order to build useful models in that time, you needed competent practitioners who understood how to simulate and adjust for economic conditions that no one in the workforce had seen before. There are, however, two major advantages of using all the available data in solving your analytical business problems. The first is technical, and the other is productivity through an improved workflow for analysts.