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In which country would a company with fewer than 50 employees decide to limit its own growth? In France, it would seem, according to research published by a team of London School of Economics professors.

Employee protection legislation in France places onerous demands on larger companies and has a direct impact on productivity, say the researchers. Luis Garicano, who led the LSE team, has come up with these figures after analysing data from 67,000 companies over five years.

This kind of “big data” analysis is proving to be one of the trendiest subjects in business schools. From government legislation to personalised medicine to online marketing, big data and data analytics promise to help organisations make better decisions.

What exactly is big data?

“You’re never going to get a concise, consistent answer,” says Andrew McAfee of MIT Sloan. Some people define it with numbers – anything with millions of bits of data – others by the number of data warehouses involved. Others take a management perspective. Robert Dammon, dean of the Tepper school at Carnegie Mellon University, says it is data that is not organised in the way a company wants.

Murat Kristal, operations management professor at the Schulich school at York University in Toronto agrees. “The most important thing is not the size of the data but how you can use the data to make decisions,” he says.

So how does data analytics work?

It is about asking the right questions to get the information on which to make business decisions. But it is about more than data mining and building statistical models, says Robert Grossman, operations management professor at Chicago Booth.

“[It’s about] how you embed the analytical models in your products to increase profits or reduce risk, for example.”

Many of the techniques for collecting and analysing data have been around for decades, says Prof Grossman, citing the examples of credit scoring and direct mailshots.

Why now?

Big data analytics is meat and drink to engineering and science departments, but the collection of huge data sets in the consumer industries has brought the techniques into business school.

Why is it so important?

“A lot of people have been making decisions by the seat of their pants,” according to Prof Garicano. “In management people are learning to make much better decisions (using data).”

What are the potential problems?

Handling large amounts of consumer data can involve issues of privacy and ethics. But the biggest question is how to ask the right questions and interpret the answers, says Prof Garicano. “The problem is causality. I think one of the key things is to teach students the pitfalls of reverse causality, [for them to ask] what are the alternative causes that could explain this.”

What courses are available?

Chicago Booth has a course on its MBA programme, as does the Tepper school at Carnegie Mellon University, while at the LSE the techniques are an integral part of masters and undergraduate programmes. NYU Stern and Schulich run standalone masters degrees in Business Analytics.

Who is leading the field?

Wharton marketing professors Eric Bradlow and Peter Fader are known as world experts in analysing consumer data to predict trends and formulate business strategy. They are the academic directors of Wharton’s Customer Analytics Initiative.

At MIT Sloan, Erik Brynjolfsson, professor of management science, and Prof McAfee are specialists in analysing how technology is changing business and management. At LSE, Prof Garicano is conducting countrywide investigations, combining national data sets with additional research.

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