PRIVACY-CENTRIC DATA WAREHOUSING IN MARKETING: NAVIGATING THE INTERSECTION OF ANALYTICS AND REGULATORY COMPLIANCE
Keywords:
Privacy-First Data Warehouses, Marketing Analytics, Data Protection Regulations, GDPR Compliance, Ethical Data ManagementAbstract
In the era of data-driven marketing, organizations face the dual challenge of leveraging vast amounts of consumer data for analytics while adhering to increasingly stringent privacy regulations. This article examines the concept of privacy-first data warehouses as a solution to balance marketing effectiveness with regulatory compliance. We explore the evolution of data warehouses in marketing, analyze the impact of regulations such as GDPR and CCPA, and propose a framework for implementing privacy-centric data infrastructures. Through a combination of technical strategies, including data minimization, purpose limitation, and advanced security measures, alongside organizational approaches like privacy impact assessments and data governance frameworks, we demonstrate how businesses can maintain robust analytics capabilities while prioritizing consumer privacy. Case studies of successful implementations are presented, along with an analysis of challenges and future trends in privacy-enhancing technologies. This article contributes to the growing body of knowledge on ethical data practices in marketing. It provides practical insights for organizations seeking to navigate the complex landscape of big data analytics and privacy compliance.
References
S. Erevelles, N. Fukawa, and L. Swayne, "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, vol. 69, no. 2, pp. 897-904, 2016. https://doi.org/10.1016/j.jbusres.2015.07.001
M. Goddard, "The EU General Data Protection Regulation (GDPR): European regulation that has a global impact," International Journal of Market Research, vol. 59, no. 6, pp. 703-705, 2017. https://doi.org/10.2501/IJMR-2017-050
A. Kumar, "From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior," Journal of Marketing, vol. 80, no. 1, pp. 7-25, 2016. https://doi.org/10.1509/jm.14.0249
P. Adamopoulos, A. Ghose, and V. Todri, "The Impact of User Personality Traits on Word of Mouth: Text-Mining Social Media Platforms," Information Systems Research, vol. 29, no. 3, pp. 612-640, 2018. https://doi.org/10.1287/isre.2017.0768
S. Mithas and J. Rust, "How Information Technology Strategy and Investments Influence Firm Performance: Conjecture and Empirical Evidence," MIS Quarterly, vol. 40, no. 1, pp. 223-245, 2016. https://doi.org/10.25300/MISQ/2016/40.1.10
K. Berman, "Beyond the Last Touch: Attribution in Online Advertising," Marketing Science, vol. 37, no. 5, pp. 771-792, 2018. https://doi.org/10.1287/mksc.2018.1104
D. L. Hoffman, T. Novak, and H. Peralta, "Building Consumer Trust Online," Communications of the ACM, vol. 42, no. 4, pp. 80-85, 1999. https://doi.org/10.1145/299157.299175
R. K. Chellappa and R. G. Sin, "Personalization versus Privacy: An Empirical Examination of the Online Consumer's Dilemma," Information Technology and Management, vol. 6, no. 2-3, pp. 181-202, 2005. https://doi.org/10.1007/s10799-005-5879-y
S. Spiekermann and L. F. Cranor, "Engineering Privacy," IEEE Transactions on Software Engineering, vol. 35, no. 1, pp. 67-82, 2009. https://doi.org/10.1109/TSE.2008.88
C. Dwork, "Differential Privacy: A Survey of Results," in International Conference on Theory and Applications of Models of Computation, Springer, Berlin, Heidelberg, 2008, pp. 1-19. https://doi.org/10.1007/978-3-540-79228-4_1
B. Custers, "A comparison of data protection legislation and policies across the EU," Computer Law & Security Review, vol. 34, no. 2, pp. 234-243, 2018. https://doi.org/10.1016/j.clsr.2017.09.001
D. J. Solove and D. Keats Citron, "Privacy Harms," Yale Law Journal, vol. 131, no. 1, pp. 1-63, 2021. https://www.yalelawjournal.org/article/privacy-harms
P. Voigt and A. von dem Bussche, "The EU General Data Protection Regulation (GDPR): A Practical Guide," Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-57959-7
C. Dwork and A. Roth, "The Algorithmic Foundations of Differential Privacy," Foundations and Trends in Theoretical Computer Science, vol. 9, no. 3-4, pp. 211-407, 2014. https://doi.org/10.1561/0400000042
Q. Yang, Y. Liu, T. Chen, and Y. Tong, "Federated Machine Learning: Concept and Applications," ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1-19, 2019. https://doi.org/10.1145/3298981