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Data monetization, a form of monetization, is generating revenue from available data sources or real time streamed data by instituting the discovery, capture, storage, analysis, dissemination, and use of that data. Said differently, it is the process by which data producers, data aggregators and data consumers, large and small, exchange sell or trade data. Data monetization leverages data generated through business operations as well as data associated with individual actors and with electronic devices and sensors participating in the internet of things. 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 data 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〔http://www.freepatentsonline.com/y2014/0032267.html〕 and implementing vendor relationship management〔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,〔http://personaldataecosystem.org〕 Patient Privacy Rights,〔http://patientprivacyrights.org/〕 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. ==Steps== # 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. # Connect, aggregate, attribute, validate, authenticate, and exchange data - this allows data to be converted directly into actionable or revenue generating insight or services. # Set terms and prices and facilitate data trading - methods for data vetting, storage, and access. For example, many global corporations have locked and siloed data storage infrastructures, which stymies efficient access to data and cooperative and real time exchange. # Perform Research and analytics – draw predictive insights from existing data as a basis for using data for to reduce risk, enhance product development or performance, or improve customer experience or business outcomes. # Action and leveraging – the last phase of monetizing data includes determining alternative or improved datacentric products, ideas, or services. Examples may include real time actionable triggered notifications or enhanced channels such as web or mobile response mechanisms. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Data monetization」の詳細全文を読む スポンサード リンク
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