In the seemingly endless sea of big data, businesses are greedily scooping up tidbits of information to shape audience messages, enhance customer relationships, guide product development and drive sales. However, clumsy attempts to make sense of the vast volumes of information floating around the Internet are leading to flawed algorithms, wrong predictions and dangerous conclusions that are threatening innovation, jeopardizing integrity and tanking profits.
KMPG's 2016 survey of 400 U.S. CEOs found that 77 per cent of leaders have concerns about the quality of data that drives their decisions. Despite the widespread mistrust, roughly half of the directors continue to use the gathered data and analytics to develop new products and services, steer digital marketing strategy, target new customers, improve cost efficiency and weigh risk.
"As the volume of data grows exponentially, so do the opportunities to use it," writes Brad Fisher, partner and U.S. leader for D&A at KPMG, in the U.S. CEO Outlook 2016 report. "Companies are only beginning to embrace analytics for greater capabilities and broader possibilities." This unexplored territory is bringing up big discussions about how this data must be used thoughtfully and responsibly.
The Growing Demand for Data Accountability
One of the biggest takeaways of the day was the issue of how organizations can apply the altruistic philosophy of corporate social responsibility (CSR) to big data. With looming legislation like the European Commission's General Data Protection Regulation (GDPR), massive changes are about to descend upon companies throughout Europe and the U.S. regarding the collection, sharing and security of data.
"You don't just do something because the regulations say you can or can't," Scriffignano warned the Silicon Valley audience as he discussed the ramifications of organizations being required to explain how their algorithms are being used in digital data products and services. "There should always be a difference between what you can do, what you should do and what you will do."
Simply collecting the information because it is available is not a conscience-based decision, Scriffignano argued. Likewise, we should be wary of making choices based on data simply because we can. At each step, we must vet the motivation behind our decisions and assess the data for its quality and authenticity.This introduces a social responsibility component in how data is gathered and distributed. Just because private information is out there doesn't mean it is true or that is should be used. "There is a moral imperative to behave differently around the way we produce and consume data, absolutely!" Scriffignano stated emphatically.
Are Companies Irresponsibly Constructing Truth Through Data?
A second aspect of corporate social responsibility, concerns how organizations construct truth throughout the discovery, curation, synthesis, fabrication and delivery of data. Algorithms and research questions are inherently built with the biases and motivations the creators hold, which ultimately influence the results and mould the "truth." "Inclusivity matters -- from who designs it to who sits on the company boards and which ethical perspectives are included," advocates a New York Times article on the very real problems with data today.
In today's society, data is viewed as proof of a scientific fact. In reality, data is not an absolute true or false. It is subjective, colored in shades of gray and constantly in flux. Depending on how data is exploited, its meaning can be bent to support any fact-filled argument.
For example, different aspects of a recent Pew Research Center survey could be emphasized to "prove" Americans' attitudes about current race and inequality issues. An article in a liberal publication might cite the fact that 61 per cent of survey respondents say more changes are needed to achieve racial equality. In contrast, a conservative paper might applaud the progress 30 per cent of Americans feel the country has achieved in mending racial relations. While neither fact is untrue, how the argument is constructed around the evidence influences how readers view the issue. Current articles in respected outlets like Psychology Today, The Atlantic and Vox rely on a vast array of facts and figures to paint a picture of past and present racial relations in the U.S. However, the sensational headlines blasting across news channels and social media feeds are influencing how the nation is processing these high-profile, emotionally turbulent events.Since it is so easy for opponents to undercut credibility with counterclaims that are just as valid, organizations must be able to support their assertions with clear visions and untainted motivations. "The problem with the truth, as we learn more, what we thought to be true changes," Scriffignano explained as he shared his views on the realities of constructivist practices. "Taken too far, when things change, we could redact what we wrote, and no one could ever find any evidence of it having been any different than what we say to be true today. We have to be very careful that we don't help to create that."
The Future Implications of Big Data on Society
Data has the potential to identify problems that we didn't even know existed. However, even when information uncovers eye-opening truths, it can lead us down the wrong paths if we are not asking the right questions or engaging in the proper analysis. All of these swirling issues make it difficult to trust unstructured big data.
"We, the human race, have to be more thoughtful about remembering where we learned things," Scriffignano stressed. "We need to get a handle on the data we are creating. I support transparency, but we need to proceed with caution so that we do not create barriers."
One of the potential barriers being that "all of our attention spans have contracted" says Crovitz.
As we move forward with the collection, implementation and regulation of the vast and varied volumes of data available at our fingertips, we must look beyond the obvious assumptions and collective viewpoints to correctly identify the issues at hand. By continually examining the data supply chains and infrastructures, companies can ensure that they have a clear direction for where they are going with the information they are gathering so that they do not drown in the ever-evolving sea of data overload.
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