5 ethics principles big data analysts must follow

5 ethics principles big data analysts must follow

5 ethics principles big data analysts must follow

Big data is not only big—it is also powerful and error prone, notes Susan Etlinger, an industry analyst with Altimeter Group, in her 2014 TED talk. "At this point in our history... we can process exabytes of data at lightning speed, which also means we have the potential to make bad decisions far more quickly, efficiently, and with far greater impact than we did in the past."

Besides the potential for bad decisions, Etlinger believes that humans place too much faith in technology, including, for example, our blind acceptance of charts and graphs developed from big data analysis.

As to what might be done to improve the situation, Etlinger and Jessica Groopman write in their Altimeter report The Trust Imperative: A Framework for Ethical Data Use (PDF) that businesses and organizations building and/or using big-data platforms need to start adhering to ethical principles.

To incorporate ethics, Etlinger and Groopman suggest studying The Information Accountability Foundation's (IAF) paper A Unified Ethical Frame for Big Data Analysis, and paying particular attention to the following principles (Figure A).

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"Data scientists, along with others in an organization, should be able to define the usefulness or merit that comes from solving the problem so it might be evaluated appropriately." (IAF)

"The first principle for ethical data use is that it should be done with an expectation of tangible benefit," write Etlinger and Groopman. "Ideally, it should deliver value to all concerned parties—the individuals who generated the data as well as the organization that collects and analyzes it."

The authors offer Caesars Entertainment as an example. Joshua Kanter, senior vice president of revenue acceleration at Caesars Entertainment, mentions, "Before conducting any new analysis, we ask ourselves whether it will bring benefit to customers in addition to the company. If it doesn't, we won't do it."

"If the anticipated improvements can be achieved in a less data-intensive manner, then less-intensive processing should be pursued.

 



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