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TDWI Upside - Where Data Means Business

A Framework for Data Monetization

Data monetization in its simplest form is turning data into money. As you face the challenge of how to do this, there are some questions you need to consider.

Today, mega-companies such as Facebook and Google derive a large portion of their revenue from the effective utilization of data. They have mastered the art of data monetization -- turning data assets into cold, hard cash. These companies offer a free, public service that allows them to harvest massive amounts of data about their users. They then provide this data -- for a fee -- to advertisers that want to create a personalized marketing experience.

This is data monetization in its purest form, but it is not the only way your enterprise can turn data into money. You can use your data to identify business process optimizations that drive revenue and reduce expenses. Here are several potential opportunities for data monetization:

  • Identifying new revenue opportunities (products, markets, or customer segments)
  • Improving marketing impact through personalization
  • Identifying and proactively responding to customer satisfaction levels
  • Minimizing customer churn and extending customer retention
  • Optimizing the supply chain through data sharing with partners
  • Diagnosing revenue leaks and instituting corrective measures
  • Detecting and preventing fraud and piracy

These are forms of data monetization because they use data assets to create economic value.

Because profitability is a common goal across industries, it's no wonder that companies get excited about how they can leverage their data to meet or exceed their profitability targets. Here are the key factors you need to consider in order to take advantage of data monetization opportunities.

What Data Has Value?

The first step to data monetization is to take inventory of your data assets and identify what you have of value in generating additional revenue or actualizing cost savings. During this process, you will find that not all data is of equal value. Often, teams get so caught up in the technical definition of their data dictionaries that they lose focus on the ultimate value of this inventory.

As beneficial as it is to have a technical description of what data you have and in what systems that data lives, the key value of a data dictionary is to document what data elements you can leverage to derive economic value for your organization as well as how clean and complete this data is.

For Further Reading:

Data Monetization: A New Way of Thinking

5 Steps to Monetize Your Data

Monetizing the Digital Consumer Through Data

Who Is Your Audience?

Once you have identified which data has potential value, you must identify who is the potential audience for this data. These information consumers could be external to your organization (as are many of those Google and Facebook services) or internal groups such as sales, marketing, or operations who convert the information into revenue-generating or cost-saving opportunities. The goal is to get the right information to the right decision makers who can transform it into economic value.

How and When Will You Deliver the Data?

Once you understand what data is valuable and for whom it has value, identify how to deliver this information to these consumers in the most useful format. When you evaluate your delivery mechanism, you must consider what behavior ultimately drives economic value. If a report or dashboard facilitates this behavior, this delivery medium can be used.

However, if your reports and dashboards are not viewed or are viewed but not effectively used, you must evaluate other methods to get the information into your users' hands at the right time. Don't limit your thinking to only reports, charts, and graphs. At times, information delivery happens in a much different manner.

Take for example the advertising revenue generated by Google and Facebook. The most valuable data to advertisers is not a report detailing demographic information about users. Rather, the advertisers want real-time pairing of this user segmentation with an opportunity to place the perfect ad in front of the right consumer who is contemplating a buy decision.

In addition to identifying the right media channel and format, you also need to evaluate how important timeliness is in pairing your data with the monetization opportunity. In the arena of financial transactions, milliseconds can be the difference between useful and useless data. In marketing, you might have a longer period to effectively market to a segment of users once you have identified a targeted behavior.

When you look at an item on Amazon and then see advertisements for that product follow you around the Web, you've experienced that longer time span. You don't necessarily have to see ads for that item the instant after you initially look at it. In fact, the timing can happen strategically to keep your mind returning to your purchase decision for days or even weeks.

How Should You Process to Add Value?

Finally, data in its raw format is often not at its maximum efficacy. It is when elements of data, sometimes from multiple systems, are combined and information is inferred that value is maximized. When Google and Facebook analyze a profile, it is not based on a single search, visit, click, or like. It is based on the culmination of all the activity associated with a user. From this they can derive interests, income level, buying habits, and demographics. The more complete the profile becomes, the more valuable it is to potential advertisers who want to put their ads in front of a specific audience with a propensity to buy.

As you inventory your data and identify its value, identify what information could be derived from individual data elements and what tools are available to help you. In cases of big data or high-frequency data, extracting that data to an Excel spreadsheet and crunching through numbers is not going to be feasible or sufficient. This is where big data tools such as automated extract, transform, and load (ETL), algorithmic statistical processing, forecasting and prediction models, and massive parallel processing (MPP) can be used to ensure data timeliness as well as that the process happens in a consistent manner across billions of records.

The Bottom Line: Impact

Data monetization can be of significant value to your company, but to get there you must answer some fundamental questions. You must identify what data is valuable, who is your consumer, how to get them this data in the most effective way, and what you need to do to your data to maximize its impact. Once you have mastered this, you will find great opportunities to generate economic value for your organization.

About the Author

Troy Hiltbrand is the senior vice president of digital product management and analytics at where he is responsible for its enterprise analytics and digital product strategy. You can reach the author via email.

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