According to various dictionary definitions, to monetize something is “to convert an asset into or establish something as money or legal tender.” But Investopedia goes on to suggest, “The term monetize has different meanings depending on the context. It can refer to methods utilized 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 like ACNielsen, Bloomberg and Equifax, and by clever upstarts like Tru Optik (measuring unmonetized demand for movies, TV shows, video games and software, based on P2P traffic), AULIVE (harvesting and categorizing the worlds’ trove of patents), Onvia (aggregating, categorizing 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 monetization 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 monetization myths and vision visors which hinder business leaders from realizing anything near the full promise of data:
Information Monetization Myths
- Monetization 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 monetizing 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 utilized 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 monetization 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 monetizing information abound, and we do them daily and for most processes. The problem is that most organizations fail to measure the information’s economic impact. So how can they claim they’re monetizing 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 monetizing 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 monetization:
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 analyzing 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 monetizing information to measurably optimize merchandising, marketing and real estate investments. By blending and analyzing 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, optimized 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 monetizing any kind of asset doesn’t necessarily involve selling it, monetizing information doesn’t only mean selling or licensing it either. In fact the opportunities for indirectly monetizing information arguably are broader than those for monetizing it directly.
For more on the infonomics and the topic of information monetization, refer to these Gartner publications:
Follow me on Twitter @Doug_Laney #infonomics #GartnerEIM #GartnerBI
Tags: analytics big-data bigdata business-intelligence data data-broker data-monetization economics eim infonomics information information-management information-monetization information-value machine-learning monetization open-data
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.