The Talk Note
In ancient Greece, people asked the Oracle of Delphi life’s greatest questions. She would listen, slip into a trance and eventually make a prediction. The future is unknown and frightening and people believe in a prophecy alleviates some of the worry by declaring the outcome.
Big data serves as a modern oracle. Yet despite the high volume of data, using it proves difficult. Executives complain that their substantial investments into big data systems are not paying off in better employee decisions or more innovations. Tricia Wang, an ethnographer who studies patterns in how people use technology, has examined this discrepancy.
When Wang was a researcher for Nokia in 2009, she conducted field studies in China to determine how people in low-income brackets used technology. She worked as a street vendor and lived among migrants to observe various social groups. Her qualitative research revealed a large change on the horizon: Many of China’s poor yearned for smartphones, and some would invest more than half their monthly income to own an iPhone knockoff.
Unfortunately, Nokia rejected her insights because they contradicted its big data. Yet, the company’s data drew from surveys that assumed consumers weren’t aware of smartphones.
As a result, Nokia’s business “fell off a cliff.” Like Nokia, many companies disregard data that don’t come from a quantitative model. This narrow approach works for analyzing data from finite systems, such as an electrical power grid. But when systems are evolving and mutable, relying on big data alone doesn’t suffice. People fall prey to the “quantification bias,” the unconscious preference of the “measurable over the immeasurable.”
This bias makes it easy to disregard important findings that don’t manifest numerically.
Even the Delphic oracle used human insights or rather, the temple guides who translated her babbling did. Geological research shows that the oracle’s temple sat upon two earthquake faults, allowing it to fill with ethylene gas. Temple guides tempered the woozy oracle’s predictions with their own knowledge and observations.
Similarly, you can integrate big data and “thick data,” the qualitative research that combs human “stories, emotions and interactions” to reveal insights and context.
For example, Netflix’s recommendation algorithm enabled it to make incremental improvements. But thick data revealed viewers’ propensity to binge watch: information Netflix used to great success. Enhancing algorithms with thick data can improve many aspects of society – such as law enforcement, health insurance and business – and even save lives.
More Book Reviews
- The Principles by Ray Dalio (10/10★)
- Lead Right for Your Company’s Type (How to Connect Your Culture with Your Customer Promise) , William E. Schneider, AMACOM, 2017
- The Social Organism, A Radical Understanding of Social Media to Transform Your Business and Life, Oliver Luckett and Michael J. Casey (Hachette Book Group USA, 2016) (6/10 ★)
- The Life-Changing Magic of Tidying Up, The Japanese Art of Decluttering and Organizing, Marie Kondo, (Ten Speed Press, 2014) (7/10★)
- How to Speak Money, What the Money People Say – and What It Really Means, John Lanchester (2014)
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Categories: Book Review