3 Factors Of A Successful Data Monetization Strategy

NewImageWe are at a tipping point for the realization of value from data-oriented services (big data, data science, etc.). Those that see limited growth opportunities in traditional application and services development are already well underway in this data science transformation phase. For those that don’t see the need to monetize their data and information assets, it may be an Extinction Level Event (ELE) that is competitively unavoidable. Either way, understanding the effective components of an actionable data monetization strategy is extremely important.

Data Monetization is the process of actively generating value from a company’s data inventory. Today, only 1% of the world’s data is being analyzed (IDC); while at the same time, 100% of the data is costing companies CapEx and OpEx on a daily basis. Consumers and business line owners are beginning to recognize that the insights locked in data that reflect personal usage, location, profile and activity has a tangible market value. This is especially true when you apply the Power of Three principle to corporate data sets.

A data monetization strategy actively looks to extract latent value through three principle venues:

Howitworks smallLevel 1. Aggregating and Analyzing – Companies look to drive incremental revenue by aggregating multiple data sources (Power of Three) and conducting deep analyses through data science. The resulting models are then used to drive changes in the decision making process for operational, sales, and marketing. Ownership of value is retained and protected, but the cost of value generation is the highest of the three models.

Level 2. Licensing and Selling – Companies are launching ventures that package, license, and resell corporate data (creating new data sets and insights), or using data sets to launch new information-based products. For example, placing their point-of-sale (POS), internal social,  relationship-oriented, and other data online for business partners to subscribe. Ownership of value is transferred, but the cost of value generation is the least costly of the three models (cost of sales and marketing)

Level 3. Crowdsource Data Insights – Based on the deriving value from the crowd, data is supplied to the crowd for analyses that produce specific actionable outcomes. For example, Kaggle.com is a data prediction competition platform that allows organizations to post their data and have it scrutinized by the data scientists in exchange for a prize. Ownership of value is retained or shared and the cost of value generation is distributed throughout the crowd at a compensation based on tiered rewards (cost of 1st place, 2nd place, and 3rd place rewards << total cost of all data science activities for N participants).

Of all three strategies, crowdsourcing data insights (Level 3) tends to offer the highest long term benefits at the least capital and operational costs. Companies can retain the intellectual property from the insights derived through third party analyses, but not directly incurring the operational costs associated with hiring resources. A true win win.

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 (Gartner). As big data management consultants and data scientists, working with lines of business, begin to address these drivers, we should expect to see one or more of these venues fundamentally change the we monetize our businesses.



Categories: Data Monetization, Data Science

Tags: ,

2 replies

  1. Interesting post. i don’t thing crowdsourcing is practical for all companies (especially when privacy/security is concerned), but maybe advances in security, depersonalization, and obfuscation will make it a more viable option for all companies/industries

  2. @ed Having some experience with highly regulated industries (healthcare, finance, et cetera), I would have to disagree. Data de-personalization is already mandatory in quite a few cases, and has been practiced for years. Big data, of the “corporate secret sauce” variety usually relies on context.

    With large data-sets, there is some built-in “security through obscurity” given that the context of insights is what makes them actionable. By way of example: knowing that the most tweeted phrase in Egypt on and immediately after February 11th, 2011 was “The Guy Behind Omar Suleiman” doesn’t in of itself do you much good. It has no context unless you had been following Egyptian politics at that time. Later, after the context was explained to everyone, it became a joke.

    @Dr. J I would love to re-blog this, as I found it a very succinct summary of the available options.

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