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Monetising information is more than just selling your data

Thu 4 Aug 2016
FYI, this story is more than a year old

According to various dictionary definitions, to monetise something is “to convert an asset into or establish something as money or legal tender.” But Investopedia goes on to suggest, “The term monetise has different meanings depending on the context. It can refer to methods utilised to generate profit, while it also can literally mean the conversion of asset into money.” Conversion is the key. 

This introduces a range of possibilities for information, all of which we see in today’s information economy with increasing frequency: Information is used as legal tender (or at least in place of legal tender in many kinds of transactions). Information most certainly is used to generate a profit–and not just by Google, Facebook and the rest of the digerati, but by just about every business in every industry today with even just a copy of Excel. And yes, information is regularly converted directly into money by a growing marketplace of household name data brokers likeACNielsen, Bloomberg and Equifax, and by clever upstarts like Tru Optik (measuring unmonetised demand for movies, TV shows, video games and software, based on P2P traffic), AULIVE (harvesting and categorising the worlds’ trove of patents), Onvia (aggregating, categorising and anticipating RFPs from federal and local government departments), and Apervita (a marketplace for healthcare related data and algorithms). And hundreds, perhaps thousands, of others. 

But let’s dispel the notion right away that information monetisation is just about selling your data. It’s much more than this. The range of ways to do so is endless. Try not to get caught up in these other information monetisation myths and vision visors which hinder business leaders from realising anything near the full promise of data:

Information Monetisation Myths

  • Monetisation means selling data
  • Requires an exchange of cash
  • Only involves your own data
  • The data is in raw form
  • We’re not in the information business
  • Nobody would want our data
  • We just give our data to our suppliers and partners

Endless Economic Alternatives for Information

The first and biggest vision roadblock to monetising information is a failure to think beyond selling information. It’s best not to get painted into this corner lest you limit the economic potential of your information. Instead, think more broadly about “methods utilised to generate profit.” These methods can range from indirect methods in which information contributes to some economic gain, or to more direct methods in which information generates an actual revenue stream.

Indirect methods of information monetisation can include using data to reduce costs, improve productivity, reduce risks, develop new products or markets, or build and solidify relationships.

With indirect methods for monetising information abound, and we do them daily and for most processes. The problem is that most organisations fail to measure the information’s economic impact. So how can they claim they’re monetising it? They can’t really. And this presents a real roadblock to budgeting for any information-related initiatives. Although an inability to measure information’s top or bottom line impact shouldn’t stop you from using it, in reality it probably does limit how well, how broadly, and how creatively you deploy it. So let’s put a stake in the ground:You are indirectly monetising information only if you are measuring its contribution to economic value. This may not quite be an aphorism, but it’s certainly useful.

By way of illustration, here are just a couple of the hundreds of examples Gartner has compiled of indirect information monetisation:

Financial Stress Test? Citi is Stressed No More.

Consider March 5, 2015 when Citigroup added $9 billion in market capitalization and a dividend increase of 500%. That morning the U.S. Federal Reserve had released the results of the second phase of its annual Comprehensive Capital Analysis and Review (CCAR) stress tests on major banks. Citigroup had passed with flying colors–the cleanest test of top US banks–by correlating and analysing 2600 macroeconomic variables with revenue streams from dozens of business units with the help of machine intelligence technology from Ayasdi. They had uncovered variable permutations which were difficult to identify using basic business intelligence approaches, and reduced this process from three months to two weeks. In using information to demonstrably reduce risk and improve compliance, Citigroup had added billions in market value.

Belk No Longer Balking at Advanced Analytics

Or consider how the Carolina’s-centered mid-range upscale department store chain, Belk, is monetising information to measurably optimise merchandising, marketing and real estate investments. By blending and analysing data from its millions of customers across thirteen different databases, along with census, ethnicity, and population migration data, with the help of self-service data integration and analytics software from Alteryx, it developed attrition models to scorecard customers by spend level, purchase history and other dimensions to identify and target high-value multi-channel customers. In doing so, Belk increased diverse and non-diverse spend, increased the number of multi-channel customers, optimised assortment plans and store format, and improved store opening and closing decisions. As a result, it has almost doubled the number of online and in-store customers.

Just as monetising any kind of asset doesn’t necessarily involve selling it, monetising information doesn’t only mean selling or licencing it either. In fact the opportunities for indirectly monetising information arguably are broader than those for monetising it directly.

You are only limited by your imagination…and your ability to measure and attribute the benefits.

Article by Doug Laney, Gartner Blog Network

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