Data Analytics – Ethical And Privacy Issues - Eaton Business School

Data Analytics – Ethical And Privacy Issues

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Data Analytics – Ethical And Privacy Issues

Big data is like that brimming cocktail – all the punch served in one glass. And yet, the one letdown that takes away the punch is the privacy concern. Facebook, the one-stop shop where people love to post tiniest details of their lives has been a topic of much discussion these days. And if you have been one of those who took the infamous ……, then you are justified in knowing that someone knows about you much more than you probably ever imagined. Can organizations decide not to collect data? No, they cannot. Because as rightly said, data is the ‘new oil’. Think of it again, why would Facebook have paid close to $16 billion to acquire WhatsApp? All those data that was flying off the billions of mobile users across the world meant unparalleled power. The kind of power that is yielded through monetization of such data. It helps organizations to understand the consumer profiles and lets them do targeted marketing – or, what is typically called ‘personalization of experience’. Well organizations claim that this personalization provides customer benefits (imagine, all those recommendations that pop up on your Amazon screen). That’s one side. On the other side, without the customer being aware, what else could their data reveal about them? And to what extent is that data being used? That’s the ethical ‘gray area’. Who decides what is right? The customer or the organization? The Facebook-Cambridge Analytica scandal could have upped our obsession about the whole data privacy and ethical issues, but it is indeed essential to understand the relevance of values in the digital societies that we are a part of.

So, in the context of big data analytics, what are the major ethical issues? Libby Bishop (2017) identifies the principal issues as follows:

  • Privacy which can be protected by limiting the data collected. Also, altering data in a manner that makes it less revealing as well as regulating access to data are proposed to ensure the privacy of data.
  • Informed consent whereby users are made fully aware of the purpose of the intended present as well as future uses of data. This may save embarrassment for organizations as well as customers who for instance, unsuspectingly fill out customer data only to find themselves the helpless targets of some marketing campaigns of which they did not want to be (Read Target example where the discovery of teen girl’s pregnancy was known to the retail group even before her family did).
  • An absence of any de-identification or anonymization terms that will help masking or removal of any elements that might identify a person.

An answer to the above issues and other not-highlighted-here ones, it is important for organizations to look at the following five principles mentioned in Unified Ethical Frame for Big Data Analytics (Abrams, 2015):

  • Beneficial – Does our use of data benefit consumers as much as it benefits us?
  • Progressive – Do we have a culture of continuous improvement and data minimization?
  • Sustainable – Are the insights we identify with data sustainable over time?
  • Respectful – Have we been transparent and inclusive?
  • Fair – Have we thought through the potential impacts of our data use on all interested parties?

Organizations that deal with big data must make sure the above serve as a checklist when running analytical activities. Big data was intended for analytical purposes. And it should remain so. Losing insight of that fact will lead to absolute chaos of the ethical foundations of our digital societies if any.

References

Abrams, M., 2015. Unified Ethical Frame for Big Data Analysis, s.l.: s.n.

Bishop, L., 2017. Big data and data sharing: Ethical issues, s.l.: UK Data Service.

 

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