Data monetization, a form of monetization, involves maximizing the revenue potential from available data by institutionalizing the capture, storage, analysis, effective dissemination, and application of that data. Said differently, it is the process by which corporations, large and small, leverage data to increase profit and efficiency, improve customer experience and build customer loyalty. The practice, although common since 2000, is now getting increasing focus as regulatory (for instance in the financial services industry Gramm–Leach–Bliley Act and Dodd-Frank) and economic pressures increase on businesses in the United States from 2008-2011.
Financial services companies are a relatively good example of an industry focused on replacing lost revenue by leveraging data. Credit card issuers and retail banks are using customer transaction data to improve targeting of cross-sell offers. Partners are increasingly promoting merchant based reward programs which leverage a bank’s data and provide discounts to customers at the same time.
- Identification of available data sources – this includes data currently available for monetization as well as other external data sources that may enhance the value of what’s currently available to the corporation
- Design and rapidly deliver new 'data' services to both internal employees and customers - this allows data to be converted directly into revenue generating insight or services.
- Storage and access to the data – many global corporations have inefficient data storage infrastructures, which stymies centralized, efficient access to required data
- Business intelligence and analytics – drawing predictive insights from existing data becomes the basis for using data for profit with enhancing product extensions or customer experience improvements
- Presentment and monetization – the last phase of monetizing data includes determining best delivery vehicle to promote new data-based products or ideas to existing or potential customers. Examples may include new or enhanced channels such as web, mobile or response mechanisms for offers.
- Improved decision-making that leads to improved profits, decreased costs, reduced risk and improved compliance
- More frequent decisions (e.g., make decisions daily versus monthly)
- More timely (lower latency) decisions (e.g., make purchase recommendations while the customer is still on the phone or in the store)
- More granular decisions (e.g., localized pricing decisions at the store level versus the city level or seasonality-based supply chain and procurement decisions).
There are an extremely wide variety of industries, firms and business models related to data monetization. The following frameworks have been offered to help understand the types of business models that are used:
Roger Ehrenberg of IA Ventures, a VC firm that invests in this space has defined three basic types of data product firms:
- "Contributory databases. The magic of these businesses is that a customer provides their own data in exchange for receiving a more robust set of aggregated data back that provides insight into the broader marketplace, or provides a vehicle for expressing a view. Give a little, get a lot back in return – a pretty compelling value proposition, and one that frequently results in a payment from the data contributor in exchange for receiving enriched, aggregated data. Once these contributory databases are developed and customers become reliant on their insights, they become extremely valuable and persistent data assets.
- Data processing platforms. These businesses create barriers through a combination of complex data architectures, proprietary algorithms and rich analytics to help customers consume data in whatever form they please. Often these businesses have special relationships with key data providers, that when combined with other data and processed as a whole create valuable differentiation and competitive barriers. Bloomberg is an example of a powerful data processing platform. They pull in data from a wide array of sources (including their own home grown data), integrate it into a unified stream, make it consumable via a dashboard or through an API, and offer a robust analytics suite for a staggering number of use cases. Needless to say, their scale and profitability is the envy of the industry.
- Data creation platforms. These businesses solve vexing problems for large numbers of users, and by their nature capture a broad swath of data from their customers. As these data sets grow, they become increasingly valuable in enabling companies to better tailor their products and features, and to target customers with highly contextual and relevant offers. Customers don’t sign up to directly benefit from the data asset; the product is so valuable that they simply want the features offered out-of-the-box. As the product gets better over time, it just cements the lock-in of what is already a successful platform. Mint was an example of this kind of business. People saw value in the core product. But the product continued to get better as more customer data was collected and analyzed. There weren’t network effects, per se, but the sheer scale of the data asset that was created was an essential element of improving the product over time."
Q Ethan McCallum and Ken Gleason published an O'Rielly eBook titled Business Models for the Data Economy
- Simplify Access
- Packaging of data (with analytics) to be resold to customers for things such as wallet share, market share and benchmarking
- Integration of data (with analytics) into new products as a value-added differentiator such as On-Star for General Motors cars
- GPS enabled smartphones
- Geolocation-based offers and location discounts, such as those offered by Facebook and Groupon  are other prime examples of data monetization leveraging new emerging channels
On March 19, 2013 the Chicago Chapter of the Product Development and Management Association (PDMA) held an event titled "Monetizing Data: An Evening with Eight of Chicago's Data Product Management Leaders"
- Ehrenberg, Roger. "Creating competitive advantage through data". IA Ventures' blog. Retrieved 23 November 2013.
- Gleason, Ken (2013). Business Models for the Data Economy. O'Reilly. ISBN 978-1-449-37223-1.