Dataupia Helps Consumer Giants Tackle Big Data
Foster Hinshaw has a theory. The most successful consumer-oriented companies—the Wal-Marts, Amazons, L.L. Beans, and Staples of the world—are successful not just because they understand their customers, but because they can operationalize that understanding. They collect massive amounts of information about past transactions and store it in data warehouses, and they actively mine those warehouses using business intelligence software.
Hinshaw illustrates with a story. “I called up L.L. Bean an hour before the cutoff time for FedEx Christmas delivery. I said, ‘I want this green sweater for my wife.’ This great customer rep says, ‘I’m sorry, that sweater is not in stock in green.’ So I was ready to run out to the mall. But then she said, ‘However, we see that your wife also likes teal, and we do have that sweater in stock in teal.’ Now, I won’t admit it in public, but I didn’t know that my wife liked teal. But somehow L.L. Bean did know that, and once she said it, it was obvious. So she was able to help me with the stuff I needed. That is what you call operational data warehousing and business intelligence.”
Hinshaw pays attention to such episodes because data warehousing has been his life for much of the last decade. In 2000 he founded Marlborough, MA-based Netezza to build data-warehousing appliances that speed up certain kinds of business-intelligence (BI) queries. Netezza’s devices are best for “deep analytics,” Hinshaw says—the kinds of questions that only a handful of PhD statisticians at each big company would even know how to ask. “They represent about 5 percent of the usage pattern in the large data market,” he says.
“But while I was at Netezza, people started asking, what about the mainstream, the other 95 percent, the guys doing routine BI?” Even before Hinshaw left Netezza (which eventually went public, in one of the top-grossing IPOs in Massachusetts in 2007), he started thinking about a new kind of appliance that would be optimized for more mainstream BI queries—questions that don’t require heavy modeling, but do require culling through terabytes of data.
And the result was Dataupia (pronounced day-TOE-pia). The Cambridge company, founded in 2005 and funded by Polaris Venture Partners, Valhalla Partners, and Fairhaven Capital, makes massively parallel data warehousing appliances that combine servers and storage with software designed to speed up BI-type queries. I got an introduction to Dataupia’s technology and its business last September during a visit with Hinshaw, and received an update a few weeks ago from Tony Sirianni, who replaced Hinshaw as CEO in early March. (Hinshaw remains active in the company as chairman of its board and its “main technical visionary,” in Sirianni’s words.)
Hinshaw’s key vision was that BI questions could be answered faster if database software ran on processors that were physically closer to the hard drives where data is warehoused. In addition, Hinshaw’s architecture splits both data and queries across scores of processor/storage nodes. But Dataupia customers don’t have to master parallel programming to make this all work—the appliances’ built-in optimization software handles that. “We enable users to implement massively parallel systems with their existing databases,” Hinshaw says. “Instead of throwing out their Oracle or IBM DB2 or MySQL server, they can keep that whole thing in place and just add our engine, Satori Server, underneath it, and get the benefits.”
Hinshaw and Sirianni say Dataupia’s machines are perfect for companies where getting meaningful answers to BI queries means searching databases with billions of rows. For example, if you’re a wireless provider working to keep your existing subscribers from defecting, you might want to scan your customers’ call records and offer them customized plans based on their individual usage habits. But if you’ve got tens millions of subscribers, each making hundreds of calls a month, that becomes a hefty job. “I was with a telco customer the other day who looked at me somberly and said they expect data volumes to triple this year alone,” says Sirianni. “They need a business intelligence infrastructure that will scale with them.”
Sirianni should know BI—he joined Dataupia from Cognos, one of the leading makers of business intelligence analytics software, where he rose to senior vice president of worldwide field operations and helped the company grow from $10 million to $800 million in annual revenue. (IBM bought the Ottawa, Ontario-based company, which also has major operations in Burlington, MA, in 2007; at $4.9 billion, the acquisition was IBM’s biggest ever.)
Ultimately, hardware from companies like Dataupia should help companies make better use of software from companies like Cognos. “One of the challenges I had at Cognos was that as I went around and talked with senior IT executives, I was able to predict whether they were going to be successful with a data-warehousing effort by how they had their architecture set up, and whether it was going to handle the loads,” Sirianni says. “A lot of them were not prepared, and it just felt like the data was going to inundate them.”
Plenty of customers seem to be reaching out to Dataupia for a lifeline: the company says it doubled its customer base in 2008. Sirianni believes the economic downturn will play in Dataupia’s favor, as customers with burgeoning data stores look to all-in-one options that could help them avoid buying separate servers and storage. To make sure companies can use its hardware without disrupting their existing operations, the company continues to tweak Satori Server to be compatible with more database programs—just yesterday it announced that it had added support for Oracle Database 11g, one of the company’s main platforms for grid computing.
Dataupia has raised about $40 million in venture capital, including a reported $10 million Series B-1 round that closed in the fourth quarter of 2008, and Sirianni is optimistic that the company will be able to attract more financing as needed. “The company brought me on board to scale the organization, and that takes money,” he says. “I believe the VCs are going to finance the company appropriately to give me the opportunity to succeed with that.”