Those three-week forecast analysis reports are a thing of the past, too.
"Before, it could take us three weeks to get a response to some of our questions simply because we had to process a lot of data," Christensen said. "We expect that we can get answers for the same questions now in 15 minutes."
With the time to build forecasts reduced by 97 percent, Vestas gained a significant edge over competitors, because they could get the jump on forecasting an area when talking to potential customers. Plus, since the reports were based on more accurate data models, the level of returns on turbines increased, even as the level of initial investment decreased.
Like many big data deployments, this wasn't simply a case of Vestas saying "we need a big data solution, regardless of cost." The conclusion that big data would be able to help came only after Vesta's sales and research teams started collaborating together to work on the age-old problem of increasing revenue. It quickly became apparent that by fine-tuning their data, Vestas would have a very good chance to improve. Looking at the potential gains, versus the real costs of deploying IBM's Hadoop solution, Vestas decided the expenditure would be worth it. Their decision has paid off.
And, with the increasing production of wind-generated energy, companies like Vestas can use all the savings they can get.
At a glance:
Vestas, a Danish wind turbine-maker, uses InfoSphere BigInsights, IBM's Hadoop-based big data solution, to build its database, drop query times, and increase revenue.
• Vestas gathers wind data from 79,000 sources, including 44,000 wind turbines
• Data queries take 15 minutes versus up to three weeks
• Vestas' wind turbines generate 20 percent of the world's wind power
• Increased data resolution from 27-square kilometer grids down to three-square kilometers
• 18-24 petabytes is the potential data available in Vestas' wind data library
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