A Survey of Data Pricing Methods

Abstract

The advancement of information technologies such as Web 2.0 and Cloud Computing and the increasing deployment and use of the Internet of Things have promoted the capture, processing and storage of data over the last few years at a rate not seen before. The amount of data that are created, captured, or replicated globally was 33 Zettabytes in 2018 and is predicted to grow to 175 Zettabytes by 2025. The considerable amount and high availability of data have a substantial potential value to businesses and governments.

Data are symbols that describe the properties of objects and events. Information is processed data that provide answers to “who”, “what”, ”where”, “when” and “how many” questions. Data themselves are not necessarily meaningful. Information is derived from data with a specific purpose and, thus, is meaningful in certain contexts.

This paper attempts to comprehensively review the state of the art of existing data pricing methods to provide a general understanding of this emerging research area. Also, it proposes a novel classification of data pricing methods in which the methods are grouped according to the fundamental properties of data to be priced.