Seattle-based Lucid Commerce, a startup focused on business intelligence for direct marketers, has raised a round of equity funding, according to a regulatory filing. The total offering amount is listed as about $4 million. TechFlash reports that Lucid has raised $3 million from Kirkland, WA-based OVP Venture Partners and European venture firm Adinvest. OVP previously led Lucid’s $2.5 million Series A round, announced in June 2008. Lucid Commerce is led by co-founder and CEO Tyson Roberts, a former employee of Seattle-based aQuantive.

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Seattle-based Tableau Software, a company focused on data visualization and business intelligence, announced today the general release of its new product, Tableau 5.0. Elissa Fink, Tableau’s vice president of marketing, said the software package has more than 60 new features that make it more interactive, so more customers can dive deeper into charts and pull out trends more effectively. Tableau was founded in 2003 and announced a $10 million funding round from New Enterprise Associates last September. Its new customers include the Dallas Cowboys and Barnes-Jewish Hospital.

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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 …Next Page »

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Good Data, a startup developing user-friendly, Web-based tools for analyzing business intelligence data, has raised an undisclosed amount of venture financing from Cambridge, MA-based Generaly Catalyst and Boston-area technology investor John Landry (an Xconomist), according to a March 12 report by Scott Kirsner. The company formerly had an office at the Cambridge Innovation Center, but has relocated its headquarters to San Francisco, according to Kirsner. Its engineering operations remain in Prague, in the Czech Republic. Good Data put together $2 million in seed money last summer using funds from founder and CEO Roman Stanek as well as Esther Dyson, Tim O’Reilly, and New York venture firm Windcrest Partners.

[Update, April 28, 2009: Good Data has confirmed the investment in a press release, and says the amount was $2.5 million, bringing the company's total funding to $4.6 million.]

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PivotLink, a business intelligence software firm with offices in Bellevue, WA, and San Francisco, CA, announced it has closed a $10 million Series C round, led by StarVest Partners and backed by existing investors Trident Capital and Emergence Capital Partners. Pivotlink changed its name from SeaTab Software in January 2008.

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Cambridge, MA-based Dataupia, which makes server appliances designed to handle large volumes of business-intelligence queries, said yesterday that it recently closed a “Series B-1″ financing round building on its $16 million B round in October 2007. The company didn’t say how much extra money it had raised, but a report today in Private Equity Hub, based on a regulatory filing, puts the figure at $10 million.

The company is backed by Polaris Venture Partners, Valhalla Partners, and Fairhaven Capital, and has now raised some $40 million all told. In its announcement, Dataupia (pronounced “day-toe-pia”) highlighted its successes in 2008, which included a doubling of its customer base and enhancements to its Satori Server, which is designed to help companies spot trends in large collections of data such as e-commerce records.

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InforSense, a business intelligence software maker based in Cambridge, MA, and London, England, said today that founding CEO Yike Guo has stepped down, becoming chief technology officer, while David Bennett, the company’s former executive vice president of worldwide sales, has been promoted to CEO. At the same time, the company said it has secured an additional $5 million in financing from its existing investors, who include Elaia Partners, Fleming Family and Partners, Imperial Innovations Ltd, NPI Ventures, and Sitka Health Fund.

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At a time when many tech companies are reducing their payrolls, San Diego’s ParAccel just announced it’s moving into a new headquarters to accommodate its rapid growth. The venture-backed company, which develops business analytics technologies that accelerate database queries, says its workforce has doubled to 60 people over the past year.

“It’s great to be well-funded and in a hot market space,” says Kim Stanick, ParAccel’s vice president of marketing. The company also has operations in Cupertino, CA.

Barry Zane, who is now chief technology officer, founded ParAccel in 2005. Zane, who was also a member of the founding technical team at Netezza, led development of the ParAccel analytic database, which made its official debut in October 2007. Venture funding came from Mohr Davidow Ventures, Bay Partners, Tao Venture Partners, and Walden International.

ParAccel’s focus on business analytics has certainly been hot, especially in San Diego, which has a growing cluster of software analytics. Xconomy even prepared this useful map of the industry here.

Stanick described ParAccel’s analytic database as a general-purpose product used by a variety of customers, such as Merkle, one of the nation’s largest database marketing agencies. It is available as enterprise software, or as a virtual or packaged appliance that runs on standard computer servers.
After installing a ParAccel database last fall, Stanick says Merkle could generate marketing lists that were more accurate and comprehensive than its previous system, with a 200 percent improvement in processing time.

ParAccel’s growth has been strong enough that it usually has a few standing openings to fill. As Stanick put it, “We’re always looking for good engineers.”

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Cambridge, MA-based Good Data wants to do for business intelligence software what Salesforce has done for customer relationship management: put it on the Web and make it easier to use. In theory, that will give more people in an organization the ability to spot business trends and make informed decisions. Today, the 30-person Web 2.0 startup announced that it has collected $2 million in seed funding to test that theory, by turning its prototype into a full production system.

The money comes courtesy of tech-celebrity investors Esther Dyson and Tim O’Reilly, as well as Good Data founder and CEO Roman Stanek and New York-based VC firm Windcrest Partners. Good Data’s focus is “delivering bite-sized, use-as-you-go data analytics that will actually get used by normal people,” Dyson said in a statement announcing the investment. (It’s the third time Dyson has invested in a company headed by Stanek; the first two were Java programming company NetBeans and enterprise software infrastructure provider Systinet.) “I think of it as the data equivalent of an ATM: You can get the value out when and where you need it.”

“Business intelligence” or BI is the general term for the layer of software tools programmers have developed to make sense of, and help executives make decisions based upon, the untamed morass of data that most big companies collect in their transaction-level databases—for example, sales data from a chain of retail stores. The Boston area is a hub of sorts both for BI toolmakers (IBM’s Cognos division, Metatomix, Visual I|O) and for builders of the data warehouse software and appliances that often intermediates between transaction-level databases and BI interfaces (Kalido, Netezza).

Good Data is trying to set itself apart from all of these companies by combining the data-warehousing and business intelligence functions into single service, and offering the whole thing over the Internet, using the Software-as-a-Service (Saas) model pioneered by Salesforce and others. While its software isn’t yet available, a demo on Good Data’s site shows how users can upload raw data to the company’s cloud of servers, then experiment with different ways of arranging, filtering, and visualizing the data using simple, Web-based drop-down menus and drag-and-drop buttons. In the fictional case study used in the demo, a marketing executive is able to sort through historical sales data from a chain of convenience stores to identify the most effective promotional events at each store.

Overall, the analytical part of Good Data’s system seems to work a lot like Microsoft Excel or Google Spreadsheets, but with an attractive Web 2.0 pastel color scheme and, more importantly, built-in mechanisms for workers to access data from their Web browsers and to annotate and share their insights—for example, by saving custom graphs or tables that highlight interesting trends. (Good data is promoting the term “collaborative analytics” to describe this process. Click on the images at left for bigger views of Good Data’s user interface.)

As the company’s software engineers, who are mainly based in the Czech Republic, use the seed funding to get the Good Data tools ready for rollout, the company is collecting names of volunteers for an upcoming hands-on preview.

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Insightful, a Seattle-based provider of statistical analysis and data-mining software, announced today that it has agreed to be acquired by TIBCO Software, a business-infrastructure software firm based in California. The deal is worth some $25 million, and Insightful will become a wholly-owned subsidiary of TIBCO.

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Lucid Commerce, a Seattle-based provider of on-demand business intelligence for direct marketers, announced today that it has raised $2.5 million in Series A funding. The round was led by Kirkland, WA-based OVP Venture Partners. Lucid said the funding will be used to add capacity and scale the company’s operations.

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Netezza (NYSE: NZ) sells high-performance data warehousing appliances to companies such as Neiman Marcus, Amazon, and TJX that need to analyze terabytes of customer data quickly to detect fraud or help with marketing decisions. Thanks to new compression techniques being rolled out next month as part of the latest software upgrade for Netezza’s appliances, they’ll be able to sift through that data more quickly, and store more of it.

“These are big companies with datasets that are growing 20 to 50 percent annually,” says Phil Francisco, vice president of product management and marketing at Framingham, MA-based Netezza, which went public last summer in one of the Boston area’s biggest technology IPOs. “The ability to make that data actionable in a low-latency way [i.e., faster] has become a fundamental competitive lever in their businesses.”

Data warehouses are like staging areas for the raw information sitting in the transaction databases large companies manage to compile data on sales, customers, suppliers, and employees. The business intelligence software used to make sense of all this data can’t query the transaction systems directly, since they store data in many different formats. Data warehouse appliances extract the information and store it in a uniform way.

The new “Compress Engine” feature of Netezza’s servers, unveiled yesterday, will double the speed at which that stored data can be extracted from the devices’ disk drives, while at the same time allowing companies to store four or more times as much data on each machine. And all of that will be achieved through a simple software upgrade—one bonus of Netezza’s hardware-intensive approach to data warehousing, in which each storage server is actually a powerful computer containing special chips assigned to speed query-answering by filtering out irrelevant data as it streams off of hard drives.

Netezza calls those custom chips “FAST Engines.” FAST Stands for FPGA Accelerated Streaming Technology; FPGA, in turn, stands for Field Programmable Gate Array, connoting the fact that the chips aren’t designed for any specific application but can be reprogrammed on the fly to carry out any logical function. With the software upgrade, Netezza is assigning a portion of each FAST Engine just to handle data decompression, which is normally a CPU-intensive task that slows down everything else. “We’re able to do decompression at the same rate that we read the disks—so if your data is compressed at a 2-to-1 ratio and the read rate of your disk drive is 60 megabits per second, effectively we’re hitting a scan rate of 120 megabits per second,” explains Francisco.

In the end, that means companies can get answers to crucial business questions faster. (Netezza has a fairly informative blog post about the whole thing.) The new technology will be an optional feature available to Netezza customers for an extra fee as part of the 4.5 release of the Netezza Performance Server software.

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Kalido, the Burlington, MA-based data warehouse customization company we profiled in March, said yesterday that it’s opening a product development and servies office in Bangalore, India. The office will complement rather than replace Kalido’s existing operations in Massachusetts and London, the company says. “We’ve doubled our product portfolio in the last year and demand for our ‘Information Engine’ has grown significantly,” said Kalido president and CEO Bill Hewitt. “This new Center will serve as a regional hub for Asia as well as contribute to our development efforts worldwide.”

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I’d like to explain what’s cool about Kalido, a Burlington, MA, software company spun off five years ago by Royal Dutch Shell, but let’s start with a story about beer.

Labatt Breweries is Canada’s largest beer producer, brewing 60 brands of ale and distributing them in stores, bars, and restaurants across the great North. Because Labatt has bought up so many regional brewers over the years, and because every province in Canada regulates the sale and taxation of alcohol differently, the company has inherited dozens of different sales databases and reporting systems, making it extremely difficult to figure out exactly which Canadians are drinking what kinds of beer.

Typically, big companies get a grip on their sales data, customer information, departmental budgets, and the like by buying a “data warehouse” where all of the most important information from lower-level transaction systems can be organized for fast retrieval; then they use “business intelligence” tools to transform the information in the data warehouse into charts and graphs that give historical, current, or predictive views. Labatt was deep into all of these technologies. It had enterprise resource planning software from Oracle, customer relationship management software from Seibel and SAP, data integration tools from Informatica, and business intelligence tools from Cognos. But when Labatt itself became part of Belgian beverage conglomerate InBev a few years ago—meaning that it would have to start feeding business performance data up the chain to managers in Europe—the company realized that its various divisions were still counting the same things different ways.

That’s when Labatt turned to Kalido, which is the product of a similar but even more severe crisis at Shell. During the 1990s, the petroleum giant acquired some 75 companies using 110 different reporting systems, according to Kalido CEO Bill Hewitt. “The guys in Beijing would call a brand of oil ‘A’ and the guys in Cincinatti would call the same product ‘B’, and the process of rationalizing the data every year was taking far too long,” says Hewitt. “So they built a data warehouse and generated reports off that—but the problem was that every time they wanted a different report or brought in a new company they’d have to rebuild the data warehouse, which would take months. Finally they ended up separating the rules that governed the data warehouse from the data itself. Instead of building a new warehouse, they built a business model”—a graphical representation of Shell’s business, with active links back to the raw data.

With this new interactive tool, the graphical model didn’t simply depict but actually controlled the way data was piped between the company’s low-level operational systems and its business intelligence software. So adding a new subsidiary’s data to the mix would be almost as simple as drawing a new box in the org chart and connecting it to the proper parent divisions. Other changes in the organization could be replicated in the model simply by dragging boxes around and redrawing the arrows between them (more details on that below). And not only that, but because the data was now tied to a flexible model, analysts could store snapshots of the model representing different points in time, then run the clock backward and forward to examine different what-if scenarios.

Back to Labatt. In 2005, the company hired Kalido to build a new data warehouse that would bring together data from 93 transaction systems databases holding 16 years’ worth of records on products, customers, suppliers, employees, and so on. A real-life test of the Kalido installation and its flexibility came almost as soon as it was completed, when Labatt acquired yet another brewery. Labatt’s field sales staff wanted to integrate the new company’s product information into the sales database right away, so they could see how Labatt’s new sales totals would look. But the company’s accountants didn’t want to integrate the new information until the end of the fiscal year, fearing it would mess up the books. “In a traditional data warehouse you’d never be able to have it both ways,” says Hewitt. “But because we have the ability to recreate a view at any point in time, the data warehouse managers could give the sales people the view they wanted and still wait until the end of the year to consolidate the financial information.”

Okay, you may have a hard time getting excited about a new data modeling tool that reconciles incompatibilities in a company’s database infrastructure and brings out the full potential of business intelligence software. And admittedly, developments in enterprise software can seem deathly dull to outsiders who aren’t enmeshed in the complexities of running a big business. But if you follow the larger world of software engineering, then you may appreciate the novelty and power of Kalido’s idea, which builds on more than a decade of R&D on graphical models that define and drive underlying data structures.

The linking of graphical models to actual software code and data is the core concept behind the Unified Modeling Language first developed in the early 1990s by Rational Software, which went on to be acquired by IBM and is now at the epicenter of Big Blue’s software development platforms business. Suffice it to say, business data modeling is an idea that’s picking up steam, and that’s likely to become more important as …Next Page »

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