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Predictive analytics provider Opera Solutions has raised $84 million from equity investors in its first-ever funding round, but that amount shouldn’t be surprising for anyone familiar with the company. When I profiled Opera in May, I called it the big data expert you’ve never heard of. It looks like that’s about to change.
Opera has been flying under the radar, but it hasn’t exactly been struggling. Opera CEO Arnab Gupta told me in May that the company was already doing $100 million a year in revenue, growing fast since launching in 2004. It was able to grow so fast because of the inherent value of big data analytics and the Opera service. Gupta told me in a call this morning that he expects the company to do about $140 million next year.
According to the Wall Street Journal, (s nws) the funding values the company at about $500 million.
Opera does such good business in part because large customers such as Citicorp and Nissan don’t need much convincing that there’s value in gleaning intelligence from their data. But unlike many other big data products, Opera takes the guesswork out of the process. It helps customers determine the best strategies, and then processes and analyzes the data as a service. Customers don’t have to acquire analytics expertise, nor do they have to invest in expensive infrastructure.
It’s one of a handful of companies doing big data in the cloud, which is a trend I think will catch on in a major way over the next couple of years.
Opera also has put a focus on employing Ph.Ds. — currently about 90, with several dozen more having advanced scientific degrees — something that makes it more akin to blue-chipper IBM (s ibm) than to many of today’s big data startups pushing Hadoop or NoSQL technologies (although most, such as Cloudera, have at least a few Ph.Ds. on board). That might have something to do with the nature of predictive analytics and machine intelligence, which are very focused on mathematics and algorithms to continually improve accuracy.
Gupta also explained that Opera employs a large number of subject-matter experts in areas such as fraud detection, retail and marketing, which helps the company create relevant strategies based on a customer’s business. As I wrote in May:
“Signals” are the key to how Opera does what it does. … Gupta explains signals as pieces of information that have value in any given field. Opera, Gupta says, is built to “mine the signal versus mine the data [itself].”
Now’s the time to raise big data money
Opera sought funding, Gupta told me, because the company had pretty much reached its limits in terms of growing the business based on word of mouth. To grow to where it wants to be — somewhere between a niche player and IBM (s ibm) — he said Opera needs to step up its sales efforts and marketing push. Based on discussions with CIOs, Gupta thinks the analytics market will crest around the end of next year as CIOs face enormous data spikes, and Opera wants to have the funds to capitalize on the demand.
He also thinks some acquisition are in order, particularly around web-scraping technologies that help find pockets of relevant data wherever they’re hiding on the web. Otherwise, Gupta said, Opera will look to acquire small companies started by eccentric scientists without great business acumen, just to bring their minds on board, and niche companies focused on specific vertical markets.
If Opera’s goal really is to remain independent, its size and valuation might help it do that. At 600 employees and valued at nearly half a billion dollars, buying and integrating the company presumably would be a bigger deal for an Oracle, HP, IBM or Dell wanting to bolster its big data business than would be snatching up a young pure-technology startup and building a service play around the software.
One interesting nugget about Opera’s funding is that it got more than $50 million than it initially wanted, but it had to work to get it. The company is based in New Jersey, but Gupta said East Coast investors had a hard time buying in, partially because they were focused on analyzing Opera’s revenue numbers instead of looking at the technology and the big data space. In Silicon Valley, he said, investors had a far easier time seeing his company’s potential.
Feature image courtesy of Flickr user dullhunk.