We’ve seen a steady flow of articles discussing machine learning, online privacy and personalization. While each of these topics is interesting in and of itself, the confluence of these three topics is where the digital debate is headed.
What data should algorithms consume in order to provide consumers with a personalized experience without infringing on personal privacy? What role do humans play in determining how technology leverages data? The articles detailed below address these subjects and present interesting perspectives that must be part of the larger debate surrounding personalization and privacy
“Careful: Your Big Data Analytics May be Polluted by Data Scientist Bias,” GigaOM – Every day we see articles exalting the power of big data. But with great power, come great responsibility. This article by Baynote’s very own data scientists, Haowen Chan and Robin Morris, explores how companies leveraging big data must be aware of potential data bias. From only gathering easily collectable data to improperly ruling out data due to preconceived notions, data scientists must constantly ensure they avoid introducing bias into analytics. If they don’t, the intelligence produced may not only be misleading, but also inaccurate. To help prevent data bias, Hawoen and Robin recommend four strategies: employ domain experts, look for white spaces, open a feedback loop and encourage data scientists to explore.
“UncommonGoods Reveals More of its Online Catalog with Automated Recommendations,” InternetRetailer – UncommonGoods, as the name suggests, is in the business of selling one-of-a-kind items ranging from home décor to gag gifts. As such, identifying affinities between items in order to provide relevant product recommendations is not an easy task. In this case study, Internet Retailer’s Amy Dusto examines how UncommonGoods turned to Baynote to help increase the number and relevancy of product recommendations offered to shoppers. With Baynote, UncommonGoods implemented algorithms to quickly search through entire product catalogs in order to identify and supplement recommendations handpicked by the merchandisers. The result is a more than twofold increase in recommendations showed to consumers and a 19.9% increase in conversions when customers click on a Baynote generated recommendation.
“The Value of Big Data Isn’t the Data,” Harvard Business Review – In this article, Northwestern professor and Narrative Science’s CTO, Kristian Hammond, explores the importance of the human link between big data, the story it tells and the action it leads decision makers to take. Currently, businesses are focused on putting technology in place to perform big data analytics. While this is a necessary step, the true value comes from taking the results of the data and developing a concise narrative that can then be turned into action. Kristian argues that, “To get scale from data interpretation, we have to embrace the power of the machine to extract and explain the data that it and it alone is in a unique position to analyze and then communicate.” At Baynote, we certainly agree that big data and machine learning play an important role in communicating business intelligence, but we must remember the importance of the human element – both developing the algorithms that machine learning runs on and the potential pitfalls of data bias discussed in the above GigaOM article.
“Personal Information is the Currency of the 21st Century” AllThingsD – Information is power. Just ask Google or Facebook. While these companies may sell products just like any retailer, these organizations’ true value lies in the huge quantities of consumer data. In this article, Tom Cochran, CTO of Atlantic Media, details how information is the new currency driving our daily lives. He concludes, “There is a zero-sum relationship between personalization and privacy. To get the personalized digital experience you want and have grown accustomed to, you have to accept the loss of your privacy.” Is this a fair exchange for giving up your online privacy?