What Is the Privacy Paradox? An Introduction for Business Leaders
Business leaders are struggling to leverage Big Data due to the risks associated with consumer privacy.
Today’s Lack of Privacy in Big Data
Predictive Analytics empowers business leaders to make informed, data-driven decisions for their consumer markets, building on compelling market insights derived from hoards of consumer data.
From identifying new market segments to forecasting the next winning consumer trend, a qualitative collection of Big Data can help organizations to study consumer behavior and boost growth.
While Big Data has the endless potential to feed business leaders with fresh market insights, the growing body of regulations on data privacy has put a cap on what organizations can do with consumer data.
Business organizations have a tremendous amount of consumer data in their hands. Because of increased security concerns, organizations may not be able to put all of their consumer data to work.
A lot of companies are afraid that this kind of regulation will kill their business models, and in some cases they may be right. (Prof. Alex Pentland, With Big Data Comes Big Responsibility, Harvard Business Review)
Towards the Privatization of Big Data
With the possibility of risking consumer data privacy, organizations are faced with the pressure to find new solutions while still making use of Big Data. One of these solutions is to encrypt an organization’s consumer data.
Non-encrypted, “regular” consumer data is data which tells the story of the consumer. It can reveal personal details about consumers’ whereabouts, habits, preferences, relationships and the communities they belong to.
Using non-encrypted consumer data can lead to compromising data privacy in some way, as consumers may not have authorized the use of their personal information by a particular organization or organizational branch.
It is not just consumer data itself which can reveal personal information — its metadata, too, can expose many details about a person’s whereabouts.
Credit card metadata, for example, “can reveal civil, political, or religious affiliations; they can also reveal an individual’s social status, or whether and when he or she is involved in intimate relationships.” (Source, p.9)
Encryption is slowly becoming a standardized method of securing consumer data. Encrypted data is regular data which has been encoded, i.e.; it has been converted to strings of illegible symbols — a series of letters and numbers.
From the consumers’ end, encrypted data safeguards their personal information. For business organizations, encrypted consumer data typically equates to data which cannot be analyzed to gain new consumer insights.
More Privacy, Less Data?
Most accounts of the Privacy Paradox focus on the dilemma experienced by the consumers themselves — having to choose between leveraging services provided by digital platforms versus sharing more personal information.
Business leaders also experience their own version of the Privacy Paradox, depending on how cognizant their organization becomes of data privacy.
Below, a simple graph illustrates the decreasing potential of Big Data for consumer analytics in relation to increasing measures for consumer privacy.
When starting with a “blank canvas” for data privacy, everything goes — Big Data can be collected from many places and analyzed freely. However, as data privacy measures increase, so do the opportunities for collecting and using Big Data decrease, and analytical results may suffer consequently.
With ever-expanding digital footprints, and the burning desire for organizations to understand consumer behavior, it is unlikely for consumer data to reduce in its numbers.
Public backlashes against recent privacy scandals seem to indicate that consumers who gain awareness of privacy concerns start caring more deeply about safeguarding their personal information.
Choosing between regular and encrypted data always leaves one party dissatisfied. Either the consumer’s personal information is subject to being exposed or business leaders do not have enough consumer data to analyze.
In current Big Data dynamics, the more security measures a business organization takes to safeguard consumer data privacy, the less ability it has to analyze data for meaningful insights.
In current Big Data dynamics, the more security measures a business organization takes to safeguard consumer data privacy, the less ability it has to analyze data for meaningful insights.
Why Encryption Is Not the End of Big Data
With the Privacy Paradox putting business leaders in a dilemma between regular and encrypted consumer data, new solutions are sought to address this painstaking problem.
But have people been looking in the right direction?
Imagine if it were possible to do Big Data analytics on consumer data and gain qualitative insights without having to worry about data privacy.
Imagine if it were possible to do Big Data analytics on consumer data and gain qualitative insights without having to worry about data privacy.
This skilled art seemed impossible — until now. With Endor’s newfound ability to do Big Data analytics on encrypted data, organizations no longer have to weed out valuable consumer data and give up on high-quality analytics.
Developed by Endor, organizations can use an automated A.I.-powered engine to compute over encrypted Big Data, gain insights and boost market growth. The process is compliant with rules and regulations, safeguarding the data privacy of consumers while supporting business objectives.
To learn more about Endor’s unique ability to do Big Data analytics on encrypted data, make sure to follow Endor’s blog for upcoming posts.
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