Quantifying the Data Currency’s Impact on the Profit Made by Data Brokers in the Internet of Things Based Data Marketplace

Abstract

With the widespread use of the Internet of Things, a large volume of data is generated through the Internet. Businesses rely heavily on the generated data, especially personal data, to improve their services or create new revenue streams. Data brokers collect, analyze, and sell this data to those businesses (also known as data consumers), creating a personal data trading ecosystem. Many deem the current personal data marketplace untrustworthy with the lack of transparency in the relationship between data providers and data brokers/consumers. Also, existing personal data quality models used by these trading models do not accurately reflect the characteristics of the real-world situation represented by the data. That is why creating a modified trading model incorporating key data quality metrics in the data pricing process is crucial, as high-quality data is more valuable to businesses. Outdated data is one of the most common data quality defects, leading to loss of potential customers, wasted budgets, and reduced customer satisfaction. The data trading model should also facilitate some degrees of controls to data providers while maximizing the data brokers profits. This study provides a data trading model that includes the willingness to sell of data providers, the willingness to buy of data consumers, and a data quality model that incorporates data currency as a key metric. The urgency of using such a data quality model comes from personal data being liable to temporal decline and the need to build a trustworthy ecosystem. The study uses experimental quantitative research and existing personal datasets to show the impact of data currency on the profit of data brokers in the IoT based data marketplace.