Big Data

How much or too little? Assigning a dollar value to big data

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Alex Romanov is president and CEO of iSIGN.

Mention the idea of buying or selling data and it’s likely that a clandestine scenario is envisaged.

Case in point: Andrew Cole, a spokesman for the Colorado Secretary of State, confirmed in August that his state sold business information to marketers ranging from $200 per dataset to as much as $12,000. Last year voter registration data went for $58,000.

Mind you, none of these transactions are illegal or quasi-illegal. Not even close.

The reality is that data has been a publicly traded (and paid for) commodity for thousands of years. Most of the time, it is knowledge legitimately purchased or exchanged for something else. On rare occasion it’s acquired illicitly. And in the case of former NSA contractor Edward Snowden, top secret government information was given away in an effort to force a change in U.S. foreign and domestic surveillance practices.


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But when data is bought and sold as in Colorado, how is that information’s monetary value determined? The short answer is simple. Data’s value is determined by whatever amount the receiving party is willing to pay. Or, in simple economic terms, “fair market value.” But that, to me, sounds like taking the easy way out.

Something more is needed — a formula or guidelines that help gauge and standardize information’s value. This is especially pressing when discussing big data. Why? Because big data, as the phrase implies, involves enormous, almost incomprehensible volumes of information. Much of it is gathered via today’s smartphones, tablets, and mobile devices that, with the proper analytics, is proving a highly valuable resource. In fact, some 90 percent of the world’s data was generated in the last two years and companies like Facebook collect more than 500 terabytes of user information per day.

Big data’s big dollar value

So it’s not surprising that small dollar values assigned to each individual data point — even as little as a few cents — multiplied by large numbers like the above, equals the potential for significant return on investment (ROI). And the more information collected across varied datasets cross compared and analyzed, produces an even more accurate, more granular customer picture. It helps to think of it like GPS triangulation where at least three orbiting satellites pinpoint a portable navigation device’s location. The true value of the data is only realized once the information is used together.

When it comes to determining a data value formula, brands must first categorize the types of consumer insights they’re collecting. Generally they fall into two categories: point of sale and in-proximity, the latter reliant on smart digital signage, capable of sending timely and relevant messages to mobile devices. In-proximity data includes:

  • The number of mobile devices near a smart digital sign
  • How much time the potential customer spent by the sign, known as “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 usually a smaller set, consisting of a consumer’s basket size, the types of items purchased, and the time of day purchases were made. Considering that the ROI potential of big data is forecast to grow 600 percent in the next three to five years from 50 cents per dollar spent to $3.50 for every dollar invested, it’s critical marketers accurately assess data’s dollar value at literally the “byte” size level. My company estimates initial big data pricing between 20 cents to as much as a $1.40 per metric. And by adding loyalty program customer insights to this equation, data values could rise to as much as $3 or $4.

My advice for brands and marketers is simple. They must translate the data they gather into hit rates. So it’s not just about knowing how many mobile devices are in-proximity of a digital sign. The real value is knowing what percentage of those shoppers engage with the brand and transact. The value add comes as a cost-benefit analysis. How much does it cost a brand to gather this information versus the ROI potential of using that information to incentivize more valuable customers?

Seeing dollar signs: Now and in the cuture

In the big data business, exact formulas are hard to come by chiefly because retailers often seek brand-specific metrics (beyond the generalized list above and only they can determine its value). But the challenge is external, too. As the volume of data grows and collecting and analyzing it becomes less and less expensive, big data’s “per byte value” might drop. This is similar to when oil refineries come online or when new reserves are drilled, temporarily flooding the petroleum market causing a dip in barrel prices.

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 or frowned upon. It’s the next logical step in big data’s evolution. At almost six years old, big data as a household term (with roots that go back to the 1980s and 90s) has wowed us with the size of its datasets. Terabytes and petabytes are the light years and astronomical units of the digital age. But big numbers are only the beginning. Determining big data’s monetary worth is where the frontier of data management lies.

Alex Romanov is president and CEO of iSIGN. He previously worked at Alpine Electronics, where for 16 years. He established the Canadian operation and in three short years grew the brand to enjoy #1 market share deposing then industry leaders Pioneer and Kenwood. After leaving Alpine in 1996 he established his own marketing agency, bringing leading consumer electronic brands such as AST Computers into his portfolio and establishing sales and distribution for them, winning the top market share for them over then industry giants Compac, IBM, and Packard Bell. He later cofounded Spherex Inc., which developed and marketed an Xbox gaming audio system. 


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