Big Data’s Big Muscle

Author:Sanjiv Ranjan Das
SUMMARY

Computing power is driving machine learning and transforming business and finance

 
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Big Data’s Big Muscle Finance & Development, September 2016, Vol. 53, No. 3

Sanjiv Ranjan Das

Computing power is driving machine learning and transforming business and finance

The world has access to more data now than was conceivable even a decade ago. Businesses are accumulating new data faster than they can organize and make sense of it. They now have to figure out how to use this massive amount of data to make better decisions and sharpen their performance.

The new field of data science seeks to extract actionable knowledge from data, especially big data—extremely large data sets that can be analyzed to reveal patterns, trends, and associations. Data science extends from data collection and organization to analysis and insight, and ultimately to the practical implementation of what was learned. This field intersects with all human activity—and economics, finance, and business are no exception.

Data science brings the tools of machine learning—a type of artificial intelligence that gives computers the ability to learn without explicit programming (Samuel, 1959). These tools, coupled with vast quantities of data, have the potential to change the entire landscape of business management and economic policy analysis.

Some of the changes offer much promise.

Consumer profilingThe rapid growth in the adoption of data science in business is no surprise given the compelling economics of data science.

In a competitive market, all buyers pay the same price, and the seller’s revenue is equal to the price times the quantity sold. However, there are many buyers who are willing to pay more than the equilibrium price, and these buyers retain consumer surplus that can be extracted using big data for consumer profiling.

Charging consumers different prices based on their analyzed profiles enables companies to get the highest price the consumer is willing and able to pay for a specific product. Optimizing price discrimination or market segmentation using big data is extremely profitable. This practice was the norm in some industries—for example, the airline industry—but is now being extended across the product spectrum.

Moreover, the gains from price targeting also enable firms to offer discounts to consumers who would not otherwise be able to afford the equilibrium price, thereby increasing revenue and expanding the customer base, and possibly social welfare. Consumer profiling using big data is an important reason for the high valuations of firms such as Facebook, Google, and Acxiom, which offer products and services based on their customers’ data.

While big data may be used to exploit consumers, it is also changing business practices in a way that helps those same consumers. Firms are using the data generated from people’s social media interactions to better understand their credit behavior. Relating people’s past...

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