Today, organizations globally wrestle with how to extract valuable insights from diverse data sets without invading privacy, causing discrimination, harming their brand, or otherwise undermining the sustainability of their big data projects. Leaders in these organizations are thus asking: What management approach should businesses employ sustainably to achieve the tremendous benefits of big data analytics, while minimizing the potential negative externalities?
This Paper argues that leaders can learn from environmental management practices developed to manage the negative externalities of the industrial revolution. First, it shows that, along with its many benefits, big data can create negative externalities that are structurally similar to environmental pollution. This suggests that management strategies to enhance environmental performance could provide a useful model for businesses seeking sustainably to develop their personal data assets. Second, this Paper chronicles environmental management’s historical progression from a back-end, siloed approach to a more proactive and collaborative “environmental management system” method. An approach modeled after environmental management systems—a Big Data Management System approach—offers an effective model for managing data analytics operations to prevent negative externalities.
Finally, this Paper shows that a Big Data Management System approach aligns with: (A) Agile software development and DevOps practices that companies use to develop and maintain big data applications, (B) best practices in Privacy by Design and Privacy Engineering, and (C) emerging trends in organizational management theory. At this critical, formative moment when organizations begin to leverage personal data to revolutionary ends, we can readily learn from environmental management systems to embrace sustainable big data management from the outset.
Dennis D. Hirsch & Jonathan H. King, Big Data Sustainability: An Environmental Management Systems Analogy, 72 Wash. & Lee L. Rev. Online 406 (2016), https://scholarlycommons.law.wlu.edu/wlulr-online/vol72/iss3/4