Data monetization, a form of monetization, may refer to the act of generating measurable economic benefits from available data sources (analytics). Less commonly, it may also refer to the act of monetizing data services. In the case of analytics, typically, these benefits accrue as revenue or expense savings, but may also include market share or corporate market value gains. Data monetization leverages data generated through business operations, available exogenous data or content, as well as data associated with individual actors such as that collected via electronic devices and sensors participating in the internet of things. For example, the ubiquity of the internet of things is generating location data and other data from sensors and mobile devices at an ever-increasing rate. When this data is collated against traditional databases, the value and utility of both sources of data increases, leading to tremendous potential to mine data for social good, research and discovery, and achievement of business objectives. Closely associated with data monetization are the emerging data as a service models for transactions involving data by the data item.
There are three ethical and regulatory vectors involved in data monetization due to the sometimes conflicting interests of actors involved in the digital supply chain. The individual data creator who generates files and records through his own efforts or owns a device such as a sensor or a mobile phone that generates data has a claim to ownership of data. The business entity that generates data in the course of its operations, such as its transactions with financial institutions or risk factors discovered through feedback from customers also has a claim on data captured through their systems and platforms. However, the person that contributed the data may also have a legitimate claim on the data. Internet platforms and service providers, such as Google or Facebook that require a user to forgo some ownership interest in their data in exchange for use of the platform also have a legitimate claim on the data. Thus the practice of data monetization, although common since 2000, is now getting increasing attention from regulators. The European Union and the United States Congress have begun to address these issues. For instance, in the financial services industry, regulations involving data are included in the Gramm–Leach–Bliley Act and Dodd-Frank. Some individual creators of data are shifting to using personal data vaults and implementing vendor relationship management concepts as a reflection of an increasing resistance to their data being federated or aggregated and resold without compensation. Groups such as the Personal Data Ecosystem Consortium, Patient privacy rights, and others are also challenging corporate cooptation of data without compensation.
Financial services companies are a relatively good example of an industry focused on generating revenue by leveraging data. Credit card issuers and retail banks use 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.
There are a 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 venture capital firm that invests in this sector, 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 homegrown 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.”
Selvanathan and Zuk offer a framework that includes “monetization methods that are outside the bounds of the traditional value capture systems employed by an enterprise… tuned to match the context and consumption models for the target customer.” They offer examples of “four distinct approaches: platforms, applications, data-as-a-service, and professional services.”
Some of the patents issued since 2010 by the USPTO for monetizing data generated by individuals include; 8,271,346, 8,612,307, 8,560,464, 8,510,176, and 7,860,760. These are usually in class 705 related to electronic commerce, data processing, and cost and price determination. Some of these patents use the term, the data supply chain to reflect emerging technology to federate and aggregate data in real time from many individuals and devices linked together through the internet of things. Another emerging term is information banking.
An unexplored but potentially disruptive arena for data monetization is the use of Bitcoin micropayments for data transactions. Because Bitcoins are emerging as competitors with payment services like Visa or PayPal that can readily enable and reduce or eliminate transaction costs, transactions for as little as a single data item can be facilitated. Consumers, as well as enterprises who desire to monetize their participation in a data supply chain, may soon be able to access social network enabled Bitcoin exchanges and platforms. Clickbait and data hijacking may wither as micropayments for data are ubiquitous and enabled. Potentially, even the current need to build out data broker managed data trading exchanges may be bypassed. Stanley Smith, who introduced the notion of the data supply chain, has said that simple micropayments for data monetization are the key to evolution of ubiquitous implementation of user configurable data supply schemata, enabling data monetization on a universal scale for all data creators, including the burgeoning internet of things.
ORIGINAL SOURCE: [WIKIPEDIA]
Source link: https://en.wikipedia.org/wiki/Data_monetization