Given the rising likelihood of failure for companies today – combined with the ever-increasing amount of data – it’s cheaper than ever for companies to take risks.
That’s the assertion of Alistair Croll, author of the book “Lean Analytics,” which focuses on how companies, especially startups, can better focus on innovation and change through data analysis.
“The duration of your working life is now almost certainly greater than the lifespan of a company,” Croll says in a New York Times blog post. “That makes everyone more willing to accept that they will be disrupted.”
The average tenure of a company in the Standard & Poor’s 500 is now about 16 years, down from 60 years in 1959, according to Richard Foster of Yale University, the article notes.
Given the diversity of information that’s now available through big data – combined with the advent of such things as cloud computing that have driven down computing costs – it’s cheaper for companies to take risks and fail than ever before, the post notes.
“When things like that happen, companies focus less on costs, and more on experimentation about what is going to make their original idea work,” Croll adds. “There is more desire to experiment. In the past, a leader was someone who could get you to do stuff in the absence of information. Now it’s the person who can ask the best question about what’s going on, and find an answer.”
Thomas Redman, president of Navesink Consulting Group and author of “Data Driven: Profiting from Your Most Important Business Asset,” also espouses the benefits of big data experiments.
“In short, when used properly, experimentation brings the power of the scientific method to the problems companies face today,” he notes in a Harvard Business Review blog post. “This means the attendant focus, sharp definition of the question, careful design, data you can trust, and in-depth analyses – just what is called for in many situations.”
He suggests that experimentation has a rich history in product development and market research, contributing to hundreds of thousands of improved products in all economic sectors.
Still, he points out that many managers may not trust experimentation because it involves sampling.
“I’ll never understand why so many otherwise smart managers will trust a slightly off-target population of data that’s known to be loaded with errors over a small, spot-on, high-quality sample, but they do!,” he says. “The only way I’ve found to combat this issue is to clearly explain the many benefits of experimentation and present them in a powerful, but balanced, manner.”
When it comes to big data experimentation, Redman says:
- Managers should understand that in many cases, big data experiments do not have to be a series of complex operations done under extreme conditions; many can be small-scale, narrowly focused real-world trials
- If companies have big data they can trust, they should use it without experimentation
- Organizations cannot adopt a one-size-fits-all approach to experimentation
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