Quantifying the Data Currency’s Impact on the Profit Made by Data Brokers in the Internet of Things Based Data Marketplace
##plugins.themes.bootstrap3.article.main##
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 Outdated data is one of the most common data quality defects, leading to loss of potential customers, wasted budgets, and reduced customer satisfaction.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.
Downloads
References
-
Elvy SA. Paying for privacy and the personal data economy. Colum. L. Rev., 2017;117:1369.
Google Scholar
1
-
Heinrich B, Hopf M, Lohninger D, Schiller A, Szubartowicz M. Data quality in recommender systems: the impact of completeness of item content data on prediction accuracy of recommender systems. Electronic Markets, 2021 Jun;31:389-409.
Google Scholar
2
-
Gartner_Inc. How to create a business case for Data Quality Improvement [Internet]. (2018) Available from: https://www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-data-quality-improvement
Google Scholar
3
-
Cichy C, Rass S. An overview of data quality frameworks. IEEE Access, 2019 Feb 15;7:24634-48.
Google Scholar
4
-
Cook J. Outdated data management could be costing businesses ?32.5m per year - business leader news [Internet]. 2021 [cited 2023 Jun 18]. Available from: https://www.businessleader.co.uk/outdated-data-management-could-be-costing-businesses-32-5m-per-year/.
Google Scholar
5
-
Wortmann F, Fl?chter K. Internet of things: technology and value added. Business & Information Systems Engineering, 2015 Jun;57:221-4.
Google Scholar
6
-
Pereira AC, Romero F. A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manufacturing, 2017 Jan 1;13:1206-14.
Google Scholar
7
-
Boyes H, Hallaq B, Cunningham J, Watson T. The industrial internet of things (IIoT): An analysis framework. Computers in industry, 2018 Oct 1;101:1-2.
Google Scholar
8
-
Tan ET, Halim ZA. Health care monitoring system and analytics based on internet of things framework. IETE Journal of Research, 2019 Sep 3;65(5):653-60.
Google Scholar
9
-
Gartner_Inc. Gartner says 4.9 billion connected ?things? will be in use in 2015 [Internet]. [cited 2023 Jun 18]. Available from: https://www.gartner.com/en/newsroom/press-releases/2014-11-11-gartner-says-nearly-5-billion-connected-things-will-be-in-use-in-2015.
Google Scholar
10
-
Davenport TH, Ronanki R. Artificial intelligence for the real world. Harvard business review, 2018 Jan 1;96(1):108-16.
Google Scholar
11
-
Pera A. Towards effective workforce management: Hiring algorithms, big data-driven accountability systems, and organizational performance. Psychosociological Issues in Human Resource Management, 2019;7(2):19-24.
Google Scholar
12
-
Hyers D. Big data-driven decision-making processes, Industry 4.0 wireless networks, and digitized mass production in cyber-physical system-based smart factories. Economics, Management, and Financial Markets, 2020;15(4):19-28.
Google Scholar
13
-
Daugherty PR, Wilson HJ. Human+ machine: Reimagining work in the age of AI. Harvard Business Press, 2018 Mar 20.
Google Scholar
14
-
Davenport TH, Bean R. Big companies are embracing analytics, but most still don?t have a data-driven culture. Harvard Business Review, 2018 Feb 15;6:1-4.
Google Scholar
15
-
Gartner_Inc. How to create a business case for Data Quality Improvement [Internet]. [cited 2023 Jun 18]. Available from: https://www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-data-quality-improvement.
Google Scholar
16
-
Vashisht S, Gaba S, Dahiya S, Kaushik K. Security and privacy issues in IoT systems using blockchain. In Sustainable and Advanced Applications of Blockchain in Smart Computational Technologies, 2022 Aug (pp. 113-127). Chapman and Hall/CRC.
Google Scholar
17
-
Debnath D, Chettri SK, Dutta AK. Security and privacy issues in internet of things. In ICT Analysis and Applications 2022 (pp. 65-74). Springer Singapore.
Google Scholar
18
-
Sicari S, Rizzardi A, Coen-Porisini A. Insights into security and privacy towards fog computing evolution. Computers & Security, 2022 Jun 30:102822.
Google Scholar
19
-
Younan M, Houssein EH, Elhoseny M, Ali AA. Challenges and recommended technologies for the industrial internet of things: A comprehensive review. Measurement, 2020 Feb 1;151:107198.
Google Scholar
20
-
Privacy Security a Connected Life: A Study US, Trend Micro Ponemon Inst., 2015.
Google Scholar
21
-
Mi?ura K, ?agar M. Data marketplace for Internet of Things. In 2016 International Conference on Smart Systems and Technologies (SST) 2016 Oct 12 (pp. 255-260). IEEE.
Google Scholar
22
-
Sharma A, Pilli ES, Mazumdar AP, Gera P. Towards trustworthy Internet of Things: A survey on Trust Management applications and schemes. Computer Communications, 2020 Jul 1;160:475-93.
Google Scholar
23
-
Benndorf V, Normann HT. The willingness to sell personal data. The Scandinavian Journal of Economics, 2018 Oct;120(4):1260-78.
Google Scholar
24
-
Liang F, Yu W, An D, Yang Q, Fu X, Zhao W. A survey on big data market: Pricing, trading and protection. IEEE Access. 2018 Feb 16;6:15132-54.
Google Scholar
25
-
Zhao Y, Yu Y, Li Y, Han G, Du X. Machine learning based privacy-preserving fair data trading in big data market. Information Sciences, 2019 Apr 1;478:449-60.
Google Scholar
26
-
Yu J, Cheung MH, Huang J, Poor HV. Mobile data trading: Behavioral economics analysis and algorithm design. IEEE Journal on Selected Areas in Communications, 2017 Mar 2;35(4):994-1005.
Google Scholar
27
-
Al-Fagih AE, Al-Turjman FM, Alsalih WM, Hassanein HS. A priced public sensing framework for heterogeneous IoT architectures. IEEE Transactions on Emerging Topics in Computing, 2013 Jun;1(1):133-47.
Google Scholar
28
-
Jang B, Park S, Lee J, Hahn SG. Three hierarchical levels of big-data market model over multiple data sources for Internet of Things. IEEE Access, 2018 Jun 7;6:31269-80.
Google Scholar
29
-
Oh H, Park S, Lee GM, Heo H, Choi JK. Personal data trading scheme for data brokers in IoT data marketplaces. IEEE Access, 2019 Mar 10;7:40120-32.
Google Scholar
30
-
Nakamoto S. Bitcoin: A peer-to-peer electronic cash system. Decentralized business review. 2008 Oct 31:21260.
Google Scholar
31
-
Gupta P, Kanhere S, Jurdak R. A decentralized IoT data marketplace. arXiv preprint arXiv:1906.01799. 2019 Jun 5.
Google Scholar
32
-
Bajoudah S, Missier P. Latency of Trading Transactions in Brokered IoT Data Marketplace in Ethereum. In 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI) 2021 Oct 18 (pp. 254-263). IEEE.
Google Scholar
33
-
Parssian A, Sarkar S, Jacob VS. Assessing data quality for information products: impact of selection, projection, and Cartesian product. Management Science, 2004 Jul;50(7):967-82.
Google Scholar
34
-
Heinrich B, Klier M, Schiller A, Wagner G. Assessing data quality?A probability-based metric for semantic consistency. Decision Support Systems, 2018 Jun 1;110:95-106.
Google Scholar
35
-
Al-Salim W, Darwish AS, Farrell P. Analysing data quality frameworks and evaluating the statistical output of United Nations Sustainable Development Goals? reports. Renewable Energy and Environmental Sustainability, 2022;7:17.
Google Scholar
36
-
Hinrichs H. Data quality management in data warehouse systems. University of Oldenburg, Oldenburg, Germany; 2002.
Google Scholar
37
-
Chen Y, Avitabile P, Dodson J. Data consistency assessment function (DCAF). Mechanical Systems and Signal Processing, 2020 Jul 1;141:106688.
Google Scholar
38
-
Heinrich B, Klier M. Assessing data currency?a probabilistic approach. Journal of Information Science, 2011 Feb;37(1):86-100.
Google Scholar
39
-
Mahdavinejad MS, Rezvan M, Barekatain M, Adibi P, Barnaghi P, Sheth AP. Machine learning for Internet of Things data analysis: A survey. Digital Communications and Networks, 2018 Aug 1;4(3):161-75.
Google Scholar
40
-
Helfert M. Perspectives of big data quality in smart service ecosystems (quality of design and quality of conformance). Journal of Information Technology Management, 2018;10(4):72-83.
Google Scholar
41
Most read articles by the same author(s)
-
Ihsan Said,
Yanzhen Qu,
Improving the Performance of Loan Risk Prediction based on Machine Learning via Applying Deep Neural Networks , European Journal of Electrical Engineering and Computer Science: Vol. 7 No. 1 (2023) -
Alan Raveling,
Yanzhen Qu,
Quantifying the Effects of Operational Technology or Industrial Control System based Cybersecurity Controls via CVSS Scoring , European Journal of Electrical Engineering and Computer Science: Vol. 7 No. 4 (2023) -
Tony Hoang,
Yanzhen Qu,
Creating A Security Baseline and Cybersecurity Framework for the Internet of Things Via Security Controls , European Journal of Electrical Engineering and Computer Science: Vol. 8 No. 2 (2024) -
Sushanth Manakhari,
Yanzhen Qu,
Improving the Accuracy and Performance of Deep Learning Model by Applying Hybrid Grey Wolf Whale Optimizer to P&C Insurance Data , European Journal of Electrical Engineering and Computer Science: Vol. 7 No. 4 (2023) -
Jolynn Baugher,
Yanzhen Qu,
Create the Taxonomy for Unintentional Insider Threat via Text Mining and Hierarchical Clustering Analysis , European Journal of Electrical Engineering and Computer Science: Vol. 8 No. 2 (2024) -
Sohiel Nikbin,
Yanzhen Qu,
A Study on the Accuracy of Micro Expression Based Deception Detection with Hybrid Deep Neural Network Models , European Journal of Electrical Engineering and Computer Science: Vol. 8 No. 3 (2024) -
Issayas M. Haile,
Yanzhen Qu,
Mitigating Risk in Financial Industry by Analyzing Social-Media with Machine Learning Technology , European Journal of Electrical Engineering and Computer Science: Vol. 6 No. 2 (2022) -
Edwin A. Agbor,
Yanzhen Qu,
Improving the Performance of Machine Learning Model Selection for Electricity Cost Forecasting in Homebased Small Businesses via Exploratory Data Analysis , European Journal of Electrical Engineering and Computer Science: Vol. 7 No. 2 (2023) -
Daniel Rodriguez Gonzalez,
Yanzhen Qu,
Improving the Performance of Closet-Set Classification in Human Activity Recognition by Applying a Residual Neural Network Architecture , European Journal of Electrical Engineering and Computer Science: Vol. 6 No. 2 (2022) -
Justin Morgan,
Yanzhen Qu,
Ordered Lorenz Regularization (OLR): A General Method to Mitigate Overfitting in General Insurance Pricing via Machine Learning Algorithms , European Journal of Electrical Engineering and Computer Science: Vol. 8 No. 5 (2024)