Pricing Out Big Data's Dollar Value Potential

by , Jan 7, 2014, 8:00 AM
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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:

  • The number of mobile devices near a smart digital sign
  • How long consumers remain at the sign, or “dwell times”
  • The total number of messages sent to smart mobile devices
  • Time of day and geographic location
  • The click-through rate (CTR) and the number of opt-in coupon or special offer downloads

Point-of-sale data is often a smaller set, consisting of a consume's basket size, the types of items purchased and the time of day purchases were made. Let's look at the following revenue potential:

Considering that the ROI potential of big data is forecast to grow 600% from 50 cents today to as much as $3.50 for every dollar invested within the next 3-5 years, it's critical that marketers assess data’s dollar value at the “byte” size level.

Some estimate Big Data pricing between 20 cents to $1.40 per metric. And by adding loyalty program customer insights, data values could rise to $3 or $4.    

Big Data = Big promise

In the Big Data business, exact formulas are hard to come by. That's because retailers often seek brand-specific metrics only they can determine. But the challenge is external, too. As the volume of data grows and collecting and analyzing it becomes less costly, Big Data's “per byte value” will drop.

Ultimately, assigning a dollar value on big data and the recognition that it is already so widely bought and sold isn't something to be feared. Assigning a dollar value on Big Data is the data industry's next logical step.

At almost six years old, Big Data as a household term has wowed us with the size of its data sets and bowled us over with the power of its customer insights. But big numbers are only the beginning. Determining Big Data's monetary worth is where the frontier of data management lies.

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