We’ve already discussed the dangers big data can create by luring organizations into complacency – organizations that only experiment with data that’s close at hand instead of venturing into the unknown and exploring new territory.
But the immense promise of using data analysis to uncover the secrets of big data is best suited to the types of companies that lean toward innovation, an area that can be challenging for companies struggling just to survive in the rocky economy and – as Harvard Business Review notes – the largest companies.
Large companies tend to just create operational efficiencies instead of experimenting with innovative new products, services or approaches. This is a result of the natural business life cycle, HBR notes.
When companies first launch, their successes are not judged by their earnings but by their abilities to unearth problems in the market as well as their abilities to design solutions to solve those problems.
But within mature organizations, the measure of success is profit.
“Once a business figures out how to solve its customers’ problems, organizational structures and processes emerge to guide the company towards efficient operation,” according to HBR. “Seasoned managers steer their employees from pursuing the art of discovery and towards engaging in the science of delivery. Employees are taught to seek efficiencies, leverage existing assets and distribution channels and listen to (and appease) their best customers.”
But companies that discourage employees from experimenting and asking questions outside of those required to keep the status quo, risk “ossifying” and facing the same consequences that video rental stores and big box electronic retailers have faced, argues Brian Sommer, CEO of consultancy TechVentive, in a series of columns for ZDNet.
“For some firms, their inability to detect a change in the market and then to adapt around it are crippling,” Sommer notes.
For example, he says that big box electronics retailers should have been asking themselves if the credit tightening in 2008 would harm their efforts to sell televisions to people with poor credit. They should also have been asking if Apple’s iTunes store would prompt a decrease in walk-in traffic as people opted to download music and movies.
“A nimble firm experiments,” he notes. “Thomas Edison tried something like 6000 attempts at creating a long-lasting light bulb. Edison would have never been allowed 1% of those at most companies. The insights from these experiments will guide the eventual rollout of game-changing new solutions/processes/etc. The use of analytics may very well become a delineator between the successful firms of tomorrow and the failing firms of yesteryear.”
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- To hear how organizations that have adopted in-memory computing can analyze larger amounts of data in less time – and much faster – than their competitors, watch our on-demand webcast, “In-Memory Computing: Lifting the Burden of Big Data,” presented by Nathaniel Rowe, Research Analyst, Aberdeen Group and Michael O’Connell, PhD, Sr. Director, Analytics, TIBCO Spotfire.
- Download a copy of the Aberdeen In-Memory Big Data whitepaper here.