Data was important when Andrew Feng began his career as a credit card analyst in the mid-1990s. It just wasn’t as valuable as the tapes it was stored on.
“Employees in the IT department regularly erased and reused those tapes,” said Feng, who worked with a massive mainframe computer that took up most of a room. “It is hard to imagine throwing away the valuable customer data. But the practice saved $40 per tape.”
Today, the amount of information that those big mainframes could store can easily fit into an iPhone. But even more dramatic is the drop in storage costs: In 1967, one gigabyte of data storage cost $1 million; today it costs 2 cents.
That enormous decrease in the cost of data storage is one factor in the evolution of financial management, a field that went from relative data scarcity to data abundance in about 50 years. The other factors are the increase in computing speed and the availability of vast amounts of financial transactions, social networks, and location data from traditional banks as well as businesses like Facebook, Uber, and Google Maps.
This spring, Feng will teach a new course that synthesizes these trends. Titled Analysis and Interpretation of Financial Data, it is one of six required courses in the reimagined Certificate in Managerial Finance.
A Field Transformed by Data
An increase in analytical capacity has resulted from this data explosion. Now financial managers can find better answers to questions like: “Where should we deploy our company’s financial resources? What are our cost centers? Where are our growth opportunities? And, how do we manage the 20 percent of customers who generate 80 percent of our costs?”
Of course, as even a casual observer of the news can tell, data can be used for both good and malign ends—and Feng intends to discuss this issue too. Two recent documentaries he will incorporate into the class are “The Great Hack,” by Netflix, and “Prediction by the Numbers,” by PBS.
“Combined, they provide a sharp contrast between the great benefit versus the great damage that can be brought by big data,” Feng said.
It’s a different world than the one Feng entered when he started out as a young analyst in the credit card business. Now, for example, he would have reams of data to help root out fraud or predict whether a customer would be a good credit risk.
“Back then, it was more like how you read people and look for clues in much delayed financial transaction data,” Feng said.
Despite their uncool image today, the cumbersome mainframes were the beginning of what Feng calls “the granddaddy of Big Data.” Now, he says, we have entered something akin to a “Golden Age of Data.”
“Today we have Big Data captured via your daily activities, both online and offline—Amazon, Google, Netflix, YouTube, Yahoo, and Facebook,” to name a few, Feng said. Good analysts can leverage this data to help with business decisions—and present their findings to a broader audience using tools like Excel and PowerPoint, which don’t require advanced programming skills.
“You want to develop the skill to see through lots of noise, to hopefully find some patterns,” Feng said. “You want to put a kind of life to the dry data and put a story behind it.”
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