Ethical Imperatives in Marketing Analytics: A Framework for Responsible Data Practice in the Digital Age

Authors

  • Aishwaryaa Vasudevan Waggener Edstrom Communications, USA Author

DOI:

https://doi.org/10.32628/CSEIT25111296

Keywords:

Data Ethics, Marketing Analytics, Algorithmic Accountability, Digital Privacy, Responsible AI

Abstract

This article examines the ethical challenges and considerations in data analytics within marketing and public relations contexts, focusing on the intersection of technological advancement and professional responsibility. Through analysis of current practices, regulatory frameworks, and industry cases, the article explores critical issues including privacy protection, algorithmic bias, transparency in data collection, and professional accountability in automated decision-making. The article addresses the tension between leveraging detailed consumer insights and maintaining ethical boundaries, particularly in an era of increasing reliance on artificial intelligence and machine learning tools. The article proposes a comprehensive framework for ethical data analytics that encompasses privacy protection, bias mitigation, and transparent practices while acknowledging the evolving nature of digital marketing landscapes. The findings suggest that while regulatory compliance provides a foundation, organizations must develop robust ethical guidelines that go beyond basic requirements to build trust and ensure responsible data usage. This article contributes to the growing body of literature on digital ethics and provides practical recommendations for professionals navigating the complex relationship between data-driven decision-making and ethical responsibility in marketing and public relations.

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References

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Published

23-01-2025

Issue

Section

Research Articles

How to Cite

Ethical Imperatives in Marketing Analytics: A Framework for Responsible Data Practice in the Digital Age. (2025). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 1055-1063. https://doi.org/10.32628/CSEIT25111296