Data Monetization is the natural response to financial demands of storing and managing big data, the skills it requires to manage that data.

As Gartner reports "Data Monetization is increasingly becoming a significant business activity for most companies. While less then 10% of Fortune 1000 companies have a data monetization strategy today, it is projected that 30% of businesses will monetize their data and information assets by 2016.” 

But executives are still facing same question everywhere:

How do we turn our data into dollars? Is My data “good enough”?

It is very difficult to answer because the answers aren’t black and white.Here the first part of series will introduce a framework to approach Data Monetization Problem.

PART 1: What pain does the data solve?

Here are some use cases and examples on how company can monetize their data by solving others problem.

Telcos :

In the face of cutting edge competition,shrinking revenues,saturated opportunities and declining operating margins from their core communications services, telecom operators are retooling their business models to Data Monetization. But what problem they are solving?

Amount of time UK adults spend daily with mobile devices will surpass the mount of time spent online via desktop and laptop computers,according to eMarketer’s estimates.But Mobile devices present unique challenges for advertisers compared to other channels. It’s difficult, for example, to show mobile users enough ads or to carry out sophisticated tracking and targeting of ads.

But telcos has enough data to solve this problem.For example,Verizon has  102.8 million wireless customers, 6.2 million Internet users and 5.3 million TV subscribers. It’s a wealth of potential data that Verizon can use to power its advertising business.Verizon launched the Precision Market Insights group, it also rolled out Verizon Selects, a part of PMI that enables subscribers to opt in to receive more targeted ads – both online and off – based on their actions inside the Verizon network, including web browsing and mobile app usage, as well as location, demographics, interest data and even credit card info and physical mailing address.



Accenture and Capgemini have published reports describing the business benefits of GDSN based on extensive research with major associations, suppliers and retailers including Royal Ahold, The Coca-Cola Company, General Mills, The Hershey Company, The J.M. Smucker Company, Johnson & Johnson, Nestlé, PepsiCo, Procter & Gamble, Sara Lee, The Gillette Company, Unilever and Wegmans.

Retailers can predict demand and supply pattern to help manufacturer and supply chain.They can also help advertiser using customer spending behviours. 

For example, time-to shelf was reduced by an average of two to six weeks, order and item administration improved by 67 percent, and item data issues created during the sales process were reduced by an average of 25 to 55 percent

Healthcare Industry:

The ability to anonymize patient record data and aggregate it help medical facilities and practitioners by allowing them to Monitor the health of the population,Identify populations at high risk of disease and support administrative functions.Companies that buy the state data include IMS Health, a provider of prescription data; OptumInsight, a division of UnitedHealth (UNH), the biggest U.S. health insurer;a Portland (Me.)-based evaluator of hospital performance.Data helps researchers to investigate drug side effects or the performance of hospital surgical units by tracking the impact on patients.According to Mark Davies, the centre's public assurance director," there was a "small risk" certain patients could be "re-identified" because insurers, pharmaceutical groups and other health sector companies had their own medical data that could be matched against the "pseudonymised" records. You may be able to identify people if you had a lot of data. It depends on how people will use the data once they have it. But I think it is a small, theoretical risk,"

Tyre Manufacturers Using IoT:

For instance, tire manufacturer Pirelli collects data on tire pressure, temperature and wear-and-tear on trucks using sensors in its tires.This data gives Pirelli competitive advantage and help to improve Tyre quality, maintenance but it can also be monetize externally.Car manufacturer can buy this data to understand driving patterns to improve there service.Pirelli also offers that data as an add-on service for fleet managers and insurers, InfoWorld reports.

Selling locations Data 

Today, many companies already build massive demographic and behavioral databases on top of U.S. Census information about households to help retailers choose where to build new stores and plan marketing budgets.For example,AirSage and collect and anonymize, U.S. wireless carriers data in real time to help transportation planners and traffic reports.The software might infer that the owners of devices that spend time in a business park from nine to five are likely at work,so a highway engineer might be able to estimate how much traffic on the local freeway exit is due to commuters.

I am of the opinion that

Your Data is valuable only if its big enough to drive an impact to a business and deep enough to drive insights that help transform businesses. 


Stay tuned. Links will be added below as articles go live.

Part 1: What pain does the data solve?


Before proceeding, make sure you have legal and/or ethical right to market consumer data—and don’t move further without the proper rights and protections in place.

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Tags: Big, Data, IoT, Monetization, selling


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Comment by vivek upadhyay on January 20, 2016 at 6:58am

Brad Kolarov,

I Couldn't Agree With You More. It's bit tricky to find balance.I found some of them either doing data omitting or restricting themselves in fear of competitors.

Comment by Brad Kolarov on January 19, 2016 at 7:21am

Vivek, great article!!  Hopefully I'm not jumping the gun since this is Part 1.  We have several customers who have or are trying to monetize their data - but its not as easy as it seems.

As a services business we have worked many companies who try to sell their data, but find it difficult. Their first years are typically focused on growing their core business. Their data pipelines and stores are built for internal analytics, not for packaging, exporting, and selling data.

We have done several re-architectures and implementations for our customers to take their data pipeline and data store/s and rebuild and migrate their data. The result is a data pipeline that is no longer fragile, improves internal analytics, and allows for packaging and productizing data for sale.

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