استراتژی قیمت گذاری اطلاعات بر اساس کیفیت داده  Data pricing strategy based on data quality

1. Introduction The advent and ubiquity of Web 2.0, social networks, cloud computing, and ‘‘Software-as-a-Service” has expanded the volume of personal, business, and public data at an alarming rate. Big data volumes, and the diversity of such data, are a defining feature of the modern world, with significant financial and commercial implications. Enterprises rely not only on the acquisition of data in itself, but also on professional third-party platforms that collect data from various sources (Mohanty, Jagadeesh, & Srivatsa, 2013). Increasingly, data providers appreciate the gradual commercialization of data, and have established network platforms for data trading (Schomm, Stahl, & Vossen, 2013), thereby giving rise to data marketplaces. Armstrong and Durfee (1998) introduced the term ‘data marketplace’ to denote the ensemble of agents involved in commercial transactions. A typical data market comprises three main roles: data providers, data consumers, and a data-market owner. Data providers supply data to the data market and set the corresponding prices. Data consumers buy the data that they need. Acting as the intermediary between providers and consumers, the owner negotiates the pricing mechanism with those providers and manages the data transactions (Tang, Amarilli, Senellart, & Bressan, 2014). Currently emerging data platforms include Factual,1 Infochimps,2 Xignite,3 and the Windows Azure Data Marketplace4 (Stahl, 2013). The latter, for example, encompasses more than one hundred data sources for sale, Infochimps contains 15,000 data collections, and Xignite focuses on financial data. The emergence of data markets has prompted the design of a new kind of business model in which information and analysis tools effectively become tradable electronic goods (Muschalle, Stahl, Löser, & Vossen, 2012). In data markets, data products are processed and sold like information products at appropriately defined prices to data consumers. The present study defines data products as datasets in the form of tradable data goods after crawling, reformatting, cleaning, encrypting, and other processes. This includes government data, medical data, financial data, ecommerce data, and traffic data.