Big Data: Insightful or Exclusionary?
Big data is no longer big news. Vast amount of complex data of every kind are now collected everywhere and by every industry. And the analysis of big data has become big business.
But when it comes to information for and about consumers, is big data really everything it promises to be? And do we all have the potential to benefit equally from the insights derived from that data? In an effort to answer these questions, the Federal and Trade Commission (FTC) recently investigated the advantages and risks to consumers of using big data, releasing its findings in the report, Big Data: A Tool for Inclusion or Exclusion? In it, the FTC cautions companies against using big data in ways that could lead to discrimination, and explains how they should analyze and use that data in compliance with consumer protection laws. Based on the information gathered from a public workshop of numerous stakeholders in various industries, the FTC report discusses how companies can use consumer data to benefit themselves, their customers, and society at large—all while minimizing legal and ethical risks.
Comprehensive and even-handed, the report covers both sides of the coin. On the one hand, it recognizes the societal advantages that big data can provide, citing several areas where it has been used to help underserved populations, such as education, healthcare, and employment. On the other hand, the report highlights the risks involved in using large sets of consumer data, including how it can be used—inadvertently or otherwise—to exclude certain populations from benefits.
Overall, the FTC report is a good read. More importantly, it can serve as an authoritative reference for any organization or professional using big data to make decisions that end up affecting consumers, one way or another. Marketers in particular could benefit from considering some of the questions—and implications—raised in the report:
- How representative is your data set? You should consider whether the data you're using might be missing information about certain populations, which increases the risk of either underrepresentation or overrepresentation.
- Does your data model account for biases? You should consider how and where bias might be introduced in either the collection or analysis of big data and develop strategies to overcome that bias.
- How accurate are your predictions based on big data? You should remember that while big data can detect correlations—in some cases counterintuitive—it doesn’t explain which of them are actually meaningful.
- Does your reliance on big data raise ethical or fairness concerns? You should question what goes into an analytics model and balance its predictive value with considerations of fairness and inclusion.
Read more about the FTC’s findings on the societal benefits and exclusionary risks of using big data. Download the full report: Big Data: A Tool for Inclusion or Exclusion?