I started with an article from Bernard Marr which describes Big Data from a beginners point of view. This article says that “…almost every action we take leaves a digital trail. We generate data whenever we go online, when we carry our GPS-equipped smartphones, when we communicate with our friends through social media or chat applications, and when we shop. You could say we leave digital footprints with everything we do that involves a digital action, which is almost everything. … Data is generated and shared when our “smart” home devices communicate with each other or with their home servers. … The term “Big Data” refers to the collection of all this data and our ability to use it to our advantage across a wide range of areas, including business.”
How does it work?
Again, quoting from the Bernard Marr article: “Big Data works on the principle that the more you know about anything or any situation, the more reliably you can gain new insights and make predictions about what will happen in the future. By comparing more data points, relationships begin to emerge that were previously hidden, and these relationships enable us to learn and make smarter decisions.”
How is Big Data used?
Many companies use the data they collect about their customers to improve services and products. Starbucks, for example, uses the data they collect from their Rewards program as explained in this article. “In the words of Starbucks CTO Gerri Martin-Flickinger, “With about 90 million transactions per week we know a lot about what people are buying, where they’re buying, and how they’re buying. If we combine that information with other data like weather, promotions, inventory, insights into local events, we can actually provide better, personalized service for customers.” This ultimately creates a positive feedback loop. Starbucks uses data to improve its product offerings and customer experience, leading to increased sales from both existing and new customers, as a result more customers sign up to become Starbucks Rewards memberships, Starbucks captures more data from its large user base and the cycle repeats itself.”
Companies do need to be careful in how they use Big Data. The New Yorker lists several points in their article.. For example:
- “Remember that correlation doesn’t imply causation. A correlation between two variables (ice-cream consumption and shark attacks) may well be due to a third variable (summer weather). These days, spurious correlations often emerge from data mining, the increasingly common practice of trawling large amounts of information for possible relationships. For instance, there is a statistically significant—but, one hopes, meaningless—relationship between the annual divorce rate in Maine and the annual per-capita consumption of margarine in the United States.
- “Beware of Big Data hubris. The Google Flu Trends project, which claimed, with much fanfare, to anticipate seasonal flu outbreaks by tracking user searches for flu-related terms, proved to be a less reliable predictor of outbreaks than a simple model of local temperatures. (One problem was that Google’s algorithm was hoodwinked by meaningless correlations—between flu outbreaks and high-school basketball seasons, for example, both of which occur in winter.) Like all data-based claims, if an algorithm’s abilities sound too good to be true, they probably are.
- “Know that machines can be racist (or sexist, or otherwise prejudiced). Computer models designed to predict individual criminal behavior have shown bias against minorities, possibly because the data used to “train” their algorithms reflect existing cultural biases. Machines are as fallible as the people who program them—and they can’t be shamed into better behavior.”
If you’d like to learn more about Big Data, check out Bernard Marr’s latest book Data Strategy.
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