Wikipedia defines Big Data as “an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications.” In layman’s terms, we could say that it is more information than we sometimes know what to do with. Still there are several ways to come to terms with how Big Data works and can be used in our society; two of these ways are Informationism and the Rhetorical Model. These models are opposing approaches to viewing Big Data, and each can be broken down into the following basic premises.
- Everything can be reduced to information
- This information speaks for itself
- Computers are best at processing (because they have no emotion)
- This leads to infallible decision making
Rhetorical Model: Information is not everything
- Not everything can be reduced to information (values, desires, culture)
- Information does not speak for itself because it can be manipulated
- Computers are limited in their processing
- All judgments are valuable because they are open to criticism and revision
No matter which system you personally find to be more accurate, both methods acknowledge that while information may or may not be everything, it is a big part of our world today. But just how big of a part? By interpreting Big Data, businesses can learn what to sell, how to market their products and in general how to run a successful company. In spite of the numerous possibilities Big Data provides, though, we hit a couple snags. The first problem is that this surplus of raw information, this Big Data, is difficult to interpret. The second major problem is that not only is this information difficult to interpret, it’s difficult to put a price on. How much can a purchase history of a supermarket be sold for? Who would pay for information regarding what people “like” most often on Facebook? Exactly what are a company’s “intangible assets” (AKA data and information) worth? How much is Google really worth?
A recent Wall Street Journal article discussed this second problem concerning a lack of price tag. In the article Mr. Douglas Laney, an analyst at Gartner Inc., estimated that Kroger, the fifth largest retailer in the world, takes in about $100 million each year just from data sales. This data, processed by computers, mainly includes trends in purchasing history which are then sold to the vendors that stock the shelves of Kroger supermarkets. In turn, the vendors who buy this information interpret the data to adapt their products and marketing to the consumer, which is a complicated and complex process in itself.
So can we just say that the Big Data owned by Kroger is worth $100 million? It’s not quite that easy. Laura Martin, an analyst with Needham & Co. notes, “Data is worthless if you don’t know how to use it to make money.” Obviously, Kroger’s data is not worthless. Vendors are presumably earning much more than the $100 million they are paying for the raw information or else they would not be investing quite so much money into it. So we have established it’s not worthless. Great. Now what is it worth?
Again, the answer is not so simple. No one knows the exact cost of information because data is not a physical resource like buildings or currency. How much is a company making based off of the information and data they buy? There appear to be too many variables to tell. One must take into account the shelf-life of data as well as its future worth. Big Data is a perishable commodity; if people’s tastes change, data could be rendered useless. So with confidence, we can conclude that information may or may not be worth more in the future. This sounds like it has now become a problem for the financial experts and it has. Even the Financial Accounting Standards Board has struggled to come up with standards and rules for this information driven economy. The matter has been brought before the Board twice between 2002 and 2007 and still there is no clear way to discover what price tag to put on Big Data.
This confusing lack of regulation creates a so-called “blind spot” in many companies, especially ones such as Google, Facebook and eBay. These companies rely heavily on the collection and sale of Big Data. The three of them have combined assets minus combined debt of close to $125 billion. But the combined value of the companies’ shares is $660 billion. This incredibly large difference is the reflection of the stock market’s assigned monetary value to the companies’ algorithms, patents and accumulation of information on their customers and users. All these are things that do not show up on their data sheets.
So can we determine Big Data’s price tag? It is my personal opinion that right now we cannot, not with much accuracy. At least not “we” as in the general public. Even financial experts seem to be having a difficult time of it; there are just not enough guidelines and statistics for such a volatile market as information. Maybe a few years down the road.
The Wall Street Journal article referenced earlier in this post elaborates more on the subject of Big Data’s worth. You can read it for yourself by clicking on the following link:http://online.wsj.com/articles/whats-all-that-data-worth-1413157156