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Business Ethics at the Core of Successful and Sustainable Analytics

Started ‎03-15-2021 by
Modified ‎05-10-2021 by
Views 401
Paper 1065-2021

Mike McGuirk, Lecturer, Marketing Division, Babson College



The International Institute for Analytics has included analytics ethics as a top-five business imperative in both its 2019 and 2020 Analytics Predictions and Priorities list. Interestingly, this heightened need to focus on ethical practices coincides with a separate, related business priority to ramp up efforts to identify new ways to collect consumer data and apply analytical techniques on this data to gain a competitive advantage. In fact, in a recent study completed by Forrester Research, more than 50% of companies surveyed said they plan to launch initiatives to expand their ability to source external data. And many of these firms will appoint data hunters to lead these initiatives. In this session, we will dive into this critical moment in time for the analytics and data science communities. This is a time when an increased reliance on these practices will intensify the need to incorporate sound judgment and ethical business practices across all facets of analytics operations. Unfortunately, there have been too many recent cases of companies committing transgressions that quickly erode consumer trust in their ability to manage data and deploy ethical analytics practices. This session will explore the important role educational institutions and businesses can play to help inspire and empower students and analytics professionals to use ethical and socially responsible data collection and analytics practices.


Watch the presentation

Watch Business Ethics at the Core of Successful and Sustainable Analytics by the author on the SAS Users YouTube channel.


Current observations on data collection and analytic practices


When done correctly, the collection of consumer data and the use of analytics to mine that data informs the development of better products, more personalized marketing communications, and the delivery of superior customer experiences with brands. That is, it is a Win-Win, for consumers and businesses. It helps businesses operate in a more customer-centric manner and results in consumers receiving timely access to products and services that meet their needs, ultimately leading to greater customer satisfaction and brand loyalty.


Unfortunately, several high-profile cases of businesses misusing consumer data, whether intentionally or unintentionally, have contributed to a growing level of consumer concern. For example, during the launch of the highly anticipated Apple Credit Card, it became clear that the algorithm used to establish individuals' credit limits contained gender bias and was giving women far lower credit limits than men, even if they had equal or greater assets than their spouse.


These types of wrongdoings have resulted in a significant drop-off in consumers' willingness to provide valuable feedback in business surveys. In addition, growing consumer dissatisfaction with data collection and analytics practices has resulted in more regulation over how businesses can collect and use consumer information. The General Data Privacy Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) are two relatively recent examples of new data regulations that have been implemented that put greater control in the hands of the consumers regarding how their data can be used in business operations.



What can be done moving forward to promote ethical data collection, data science, and analytics business practices?


The presentation associated with this paper delves into the role that higher education, businesses, and the analytics community can play to bring greater awareness to this critical issue. It also provides several best practices that businesses should consider adopting to significantly lower the risk of data being misused within their organizations. This ranges from the adoption of data literacy training programs to the creation of cross-functional, diverse data governance teams. For example, we must:


  • explain how safeguards can be implemented that reduce the risk of deploying unintentionally biased predictive and ML algorithms

  • demonstrate how many traditional approaches to consumer segmentation using gender, ethnicity, and socioeconomic status often perpetuate the exclusion of consumers - instead, segmentation based on consumer attitudes and preferences are preferable because they focus on consumers self-reported interests and needs
  • challenge analysts and students to reflect on the appropriate and inappropriate use of consumer data


Ultimately, it will take an enterprise-wide commitment to ethical data collection and analytic practices to win back consumer trust and secure the sustainability of this incredibly valuable business resource.


Recommended Reading

97 Things About Ethics Everyone in Data Science Should Know, by Bill Franks, O'Reilly Media, 2020.

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‎05-10-2021 02:24 PM
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