Is Data The Next Oil? An Information Monetization Framework

Originally published June 1, 2011

The industrial age saw oil fueling all manufacturing industry processes and machines. With the advent of the services age, we are seeing a phenomenon in which data / insights are fueling business processes. However, as information starts to engulf organizations, it can become a double-edged sword.  It can be a blessing for organizations that have the maturity to monetize data, and it can completely paralyze organizations who don’t know where to channel data. This article examines the emerging opportunities for information products by examining the market landscape following these 4 factors:

  • Key constructs: What are the key constructs of a data monetization framework?

  • Pricing models: What are the various pricing models to monetize data?

  • Monetization levers: How do we monetize data? What are the 5 levers to monetize data?

  • Real world best practices: What are some real world best practices in monetizing data?

To illustrate how businesses can embark on an information monetization journey, this article will look at real world examples of customers from the travel and banking industries.

Key Constructs

There are 5 key constructs to realizing a framework by which data is translated into real dollars.

Figure 1:  The 5 key constructs to monetizing information

(mouseover image to enlarge)

The first construct is the mapping of potential information product buyers. For example, a well-known currency trading and settlement organization that MindTree worked with identified that customers interested in paying for their information products were regulators, major banks doing international transactions, and large corporate and brokerage hedge funds.

The second construct is provisioned data. We can think of the provisioning mode as a way of “wrapping” or “packaging” the data to suit the needs of the information consumers. For example, currency settlement data can be provisioned for a corporate banking executive as benchmark data with respect to its peer group, whereas the same data can be provisioned as an exceptional alert to trading party personnel.

The third construct consists of all information products which are available as an “information product catalog” for the end consumers. In the case of the settlement bank, it consisted of reports being available in a pre-canned format, analytical outputs from sophisticated statistical modeling process and the service of “analysis on demand,” which is a customized request to analyze data.

The fourth construct is the actual data layer that consists of raw data. This raw data is not readily in a shape and form that can be directly consumed by the end consumer. It has to go through a transformation process before it is amenable for consumption. Depending on its texture, the raw data can be structured data or unstructured data. It can also be classified into internal data or external data, depending upon the source of the data. Typically, to enhance the value of the data provisioned, it makes sense to have an information portfolio spanning all four data buckets – structured, unstructured, internal data, and external data.

The fifth and most important construct is the pricing model to monetize the data. This dictates the business model of the information monetization process.

Pricing Models

There are 5 basic pricing models that can be used to monetize information assets. They are:

  • Pricing lever 1 – Pricing per consumer: Here, the product is priced by the number of consumers of the information product. For example, if there are 10 different traders interested in examining benchmark data for settlements, then we have a per trader, per year pricing model.

  • Pricing lever 2 – Price per information product: You can also price per information product. For example, we can give a settlement benchmark report to a large bank on a quarterly basis as a .pdf file and charge per report. The bank would have the capability to circulate this .pdf report among the various stakeholders within the bank.

  • Pricing lever 3 – Price per alert: In this model, for every exception from the data patterns sent to the settlement operator, a charge is levied.  The charge is billed on a monthly or quarterly basis.

  • Pricing lever 4 – Price per research request: Sometimes a large transcontinental bank may need to customize data research. These requests can be charged differentially as a new customized analytical process needs to be put in place to service the non- standard need.

  • Pricing lever 5 – Bundled pricing: This can be a hybrid pricing model consisting of some or all of the above pricing models. For example, a report can be priced per product and per consumer.

Monetization Levers

With the understanding of the various constructs of an information monetization framework, the next question to ask is “How do we go about creating new information products from existing information assets?”

Travel is a classic example of an industry that is data rich, but insight poor. We can typically use the following 5 monetization levers to help organizations find avenues to generate additional revenue stream from its information assets.

Figure 2: Example of travel industry levers

  • Information monetization lever 1 – Finding new data content to monetize: This involves bringing new consumer content into the existing repository and making it available for purchase by potential information buyers. For example, in the travel industry, large data warehouses typically contain reservation and cancellation statistics for properties, along with average revenue per user (ARPU) data. In such an environment, there are 2 additional data content categories that could provide insight on customer intent – user-generated content (ratio of positive to negative sentiments, posts by source, etc.) and search requests and responses (i.e., most heavily searched locations and criteria used to search for properties). This could help property owners forecast room bookings and ARPU.

  • Information monetization lever 2 – New information consumer (increasing breadth of addressable market): Using existing information assets, you can potentially find new consumers for those existing information assets. For example, when seeking potential consumers for existing travel statistics such as bookings, cancellations, ARPU, etc, we could identify the following list of potential information buyers as an addressable market:

Figure 3:  Potential consumers for existing travel statistics using info monetization lever 2

  • Information monetization lever 3 – New advanced analytical process: Along with the existing consumer repository, you could add a new analytical process such as behavioral segmentation of properties/ customers or propensity models for certain behavior, etc. One tangible scenario would be to create a 360-degree view of properties based on metrics such as a “look-to-book ratio,” ARPU, sentiment index, bookings, cancellations, etc. Then you can segment the properties, separating the good properties from the bad. This can be used for strategic interventions by the property owners.

Figure 4: Example of analytical variables


  • Information monetization lever 4 – Intersection of data points: New information products could potentially be found at the intersection of data – structured, unstructured, internal and external syndicated sources. For example, in the travel industry, you could create a product by doing co-relation analysis between property sentiments and bookings.

Figure 5:  Intersection of data points

  • Information monetization lever 5 – Introduce a new customer behavioral key performance indicator (KPI): For example, in the travel industry, you can introduce indices like property performance deciles score or positive sentiment index as additional behavorial metrics to understand travel consumer behavior.

After thoroughly reviewing the information monetization levers (which typically involves intensive workshops and market research), a final information product catalog evolves. It consists of 4 components:

  • The information product in the form of an analysis scenario

  • The raw input data required to create the information product

  • The analytical process or technique followed

  • The final end decision process the information product helps in optimizing

Figure 6: Sample of information catalog


During the industrial revolution, oil played a huge role in catalyzing the manufacturing industry. As the services business grows in both advanced and emerging markets, we see the advent of information products playing a similar role in optimizing business processes. Information that was once dormant can “come alive” and generate additional new revenue streams for the organization. All it requires is the structured and methodical application of the information monetization framework to marry the end consumer with the right information assets.

  • Derick JoseDerick Jose

    Derick Jose is the vice president of Advanced Analytics/Research within MindTree's Data & Analytic Solutions (DAS) Group, one of the world’s largest information management practices, which offers customers a one-stop-shop to capture, analyze, enhance, and view their business information. The DAS practice combines MindTree’s proven analytics, business intelligence, information management and research services for customers in the consumer packaged goods (CPG), retail, financial services, insurance, travel and media markets. Derick has 20 years of experience spanning consulting, advanced analytics and business intelligence solutions. He has worked extensively in the CPG, banking, telecom and retail industries. Derick can be contacted at

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