For those who doubt the value of Big Data, you only need skim the latest headlines. In early December, 2 million Gmail, Facebook, Yahoo and Twitter account passwords were stolen, representing a treasure trove of personal information. In China, hackers leaked the database of 20 million hotel guest reservations onto several Web sites. On a more positive note, investors have pumped $3.6 billion into Big Data startups just within the last year -- three-quarters of what was invested during 2008-2012. Whether for good intentions or bad, Big Data means big business. That statement is increasingly self-evident. The bigger goal as we approach 2014 is calculating the exact monetary value of the information contained in large datasets. The answer should be simple: Data's value is determined by the amount the receiving party is willing to pay. But that sounds like a copout. Something more is needed; a formula or guidelines that help standardize the information's value. This is especially pressing when discussing the gargantuan volumes of information -- much of it gathered via today’s smartphones and tablets and that with the proper analytics, that are proving a valuable resource. Currently, 90% of the world’s data was generated in the last two years and companies like Facebook, for instance, collects more than 500 terabytes of user information per day. In the U.S., 38% of businesses are investing in the technology tools needed to gather and analyze Big Data. Not far behind are Europe, Africa and the Middle East at 27%, 26% for Asia-Pacific and 18% for Latin America, according to the latest Gartner report on big data. Retailers, too, are learning that big data-based personalization can deliver 5 to 8 times the ROI on marketing spend and lift sales by at least 10%. "Byte" size data value adds up It’s not surprising that small dollar values assigned to individual data points -- even as little as a few cents -- multiplied by large numbers add up fast. And the more information collected and analyzed, produces an even more accurate customer picture. When it comes to determining a data value formula, brands must categorize the types of consumer insights they are collecting. Generally they fall into two categories: point of sale and in-proximity. In-proximity data includes: