That’s an apt ticker symbol for a company that is in some ways a bellwether for the current fascination with all things data. Tableau isn’t a big data company, per se, but its visualization software breathes life into many big data calculations. Its focus on making software that’s easy to use and that creates visually captivating charts has turned people from numerous professions into amateur data analysts. (I’ve even used it in the past, including for the first time in 2011.)

Christian Chabot

As Chabot told me during a conversation in 2011, “In any field of human endeavor … there are a hundred to a thousand more people who understand the data of that field more than they understand reporting and analytics.”

Anytime you read about a hot new visualization or analytics startup promising the moon, you’re also seeing the results of what Tableau has sown in terms of the user experience. Many of those same companies will be quick to tell you how limited Tableau’s capabilities are. It’s memory-bound, it doesn’t have a database, it’s not available in the cloud (or on the Mac operating system), it can’t do predictive analytics. All true.

Of course, if it raises the kind of capital it expects to by going public, it can build and buy a lot of those capabilities. If pricing stays flat all day Friday, Tableau stands to make $155 million from its 5 million shares. Previous estimates had Tableau’s market cap at around $1.7 billion at a price of $29 per share (the company’s S-1 filing is available here).

If investors have really bought into the company and the concept of a data-driven world, then who knows. Machine-data expert Splunk wnet public in 2012, flying the big data banner, and saw shares peak at 91 percent above its original asking price of $17.

I’m not suggesting Tableau is the biggest name in data, or even that it will some day become it. This next-generation analytics field is very young, with startups and larger vendors alike sometimes competing against themselves to win wholly new accounts than trying to displace legacy vendors within large enterprises. And every month, it seems, I come across some new startup that was built with the same principles in mind as Tableau, but with the advantage of having today’s best practices baked into its software.

But Tableau definitely commands a lot of the mindshare. How it fares as a public company could be a strong indicator of just how powerful the data movement is, and how well it capitalizes on a new influx of cash will determine how long it stays on the top of customers’ minds.

This post was updated at 7:01 p.m. to include previous estimates of the company’s market capitalization and a link to its S-1 filing.

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Lumira Cloud supposedly gives SAP an answer to the recent explosion in the cloud-based, self-service data visualization offerings. The HTML5-built BI service comes with a “monthly” subscription fee (albeit one that can only be ordered in annual chunks) and lets its users publish and share data visualizations with one another for viewing or editing on desktop or mobile devices.

SAP Lumira Cloud appears to be more an Dropbox-ish add-on for the desktop version of Lumira than a cloud-based replacement, but it does also allow the creation of datasets from Excel documents. The service, which integrates with on-premise data and naturally supports HANA, can also be used to share SAP BusinessObjects Design Studio files and SAP Crystal Reports documents.

This release appears to be the culmination of what SAP has been previously referring to as “project Photon” – supposedly the company’s “true departmental self-service BI offering.” The issue here, of course, is the monumental and somewhat confusing nature of the company’s portfolio. After all, doesn’t SAP already do this SME-courting, departmental analytics stuff through its BusinessObjects BI OnDemand product?

Try visiting at least one of the BI OnDemand product pages and you’ll be taken through to the Lumira page. Look at the Lumira Cloud FAQs and you’ll be told that BI OnDemand will continue to run “in parallel” to Lumira Cloud, but also that OnDemand customers can contact their account representative “to discuss the best timing and strategy” for migrating to the new service.

Perhaps this less-than-clear situation presages a simplification of SAP’s portfolio – no doubt more will be revealed at the company’s SAPPHIRE NOW conference this week. If it doesn’t, customers in search of next-generation data visualization tools have many far more straightforward options to check out.

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Technologically, SiSense is trying to split the difference between just about everyone else doing analytics — expensive full-stack business intelligence vendors such as Oracle, Microsoft, IBM and SAP; big data and data warehouse vendors pushing massive scale databases; and next-generation, visualization-centric vendors such as Tableau and QlikView. It’s fast, it’s has its own columnar database and HTML5 visualization technologies, can scale comfortably up about 100 terabytes, and is designed for business users rather than advanced data analysts.

SiSense’s secret sauce is a processing architecture built for speed even on small machines such as laptops. According to CEO Amit Bendov, the company’s product, called Prism, can handle a terabyte of data on a machine with 8GB of RAM because it relies primarily on disk for storage. Data is only moved to RAM as necessary, and then Prism uses vectorization and optimized instructions (that do one thing only, but do it across all the data that fit the query) to handle as much work as possible in parallel on the processor.

Source: SiSense

“We say in-memory is not the future, it’s the past,” said Bendov. ”We’re already two steps ahead.” Using Hadoop or Teradata for a handful of terabytes, he added, is overkill, “like driving a Humvee to the grocery store.”

Eldad Farkash, SiSense’s co-founder and CTO, uses a different analogy — that of buying beer — to explain the technology’s underlying rationale. Latency to the CPU from the processor’s L1 cache is like grabbing a beer from the refrigerator, whereas using the L2 or L3 cache is like riding a bicycle to the corner store. RAM is the equivalent of driving a car to the grocery store, and accessing data from disk is like going to the brewery itself. Prism knows it will have to go to the grocery store, but it gets as much beer as possible from the fridge and corner store first.

Once users are actually in the product and analyzing data, it’s a drag-and-drop experience to connect various data sources and points (although custom SQL is allowed, too). The actual analysis window features a canvas that can display numerous widgets (e.g. pivot tables, charts or dashboards) at once.

Bendov said SiSense’s revenue grew 520 percent in 2012 and its notable customers include Target, Merck, Samsung and Cisco. The new investment will be used primarily to bolster the company’s sales and marketing efforts — which thus far have been largely relegated to in-bound inquiries — and to support customers in different geographies (the company is based in Redwood Shores, Calif.). “Now’s the time to add oil to the fire,” he explained.

As impressive as it all sounds, though, SiSense’s biggest challenge might well be getting noticed above the fray that is the analytics space right now — especially among more well-known and arguably future-proof vendors and technologies. That said, being a low-cost option that users like and that actually works has proven remarkably effective in an era of cloud computing and bring-your-own-device, and SiSense appears to racking up users at a pretty rapid clip.

Any product that can prove its worth initially with the people who have to use it stands a good chance of sticking around and becoming a permanent part of IT budgets for years.

Feature image courtesy of Shutterstock user Iscatel.

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Fairfax, Va.-based DataRPM on Tuesday announced that it has raised $250,000 from angel investors and rolled out a new feature for its business-intelligence Software as a Service (SaaS) called Instant Answers. The feature uses natural-language processing to figure out what users want to see, based on typed or spoken queries, and displays the visualization that the software thinks is the best fit. Users can filter and comment on the results.

While methods and purposes vary, the idea of making software or a site respond to limited user input isn’t new. The approach reminds me of Facebook’s Graph Search, which rapidly delivers several options for search results based on likes, friends and other user information. Software from BeyondCore also comes to mind, as it quickly displays graphs and audibly speaks out its findings to show the biggest drivers of, say, revenue. BeyondCore CEO Arijit Sengupta took a few minutes of stage time at GigaOM’s Structure:Data conference in New York last month to show off the software.

More natural-language processing and machine-learning technology could make DataRPM’s Instant Answers tool a better choice in a crowded market. Perhaps the SaaS could keep tabs on which data users call up and how users might modify their searches if they don’t get the data or visualizations they want the first time around. Later, it could predict what users want. The original Instant Answers is nevertheless a good start.

Feature image courtesy of Shutterstock user anaken2012.

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Here’s how.

Data management

Hadoop has become the foundational underpinning of managing big data for organizations large and small. The amazing pace of innovation has only been accelerated with recent announcements concerning Greenplum Pivotal HD, Hortonworks Stinger and Cloudera Impala. The trajectory of these projects is crystal clear: the major Hadoop distribution providers are introducing real-time, interactive queries on top of Hadoop HDFS. This brings the best of both worlds together — well-known SQL-based query processing with the exponential scale-out ability of HDFS’s storage architecture.

Analytic transparency

Predictive analytics are essential for data-driven leaders to craft their next best decision. There are a variety of techniques across the predictive and statistical spectrums that help businesses better understand the not too distant future. Today’s biggest challenge for predictive analytics is that it is delivered in a very black-box fashion. As business leaders rely more on predictive techniques to make great data-driven decisions, there needs to be much more of a clear-box approach.

Analytics need to be packaged with self-description of data lineage, derivation of how calculations were made and an explanation of the underlying math behind any embedded algorithms. This is where I think analytics need to shift in the coming years; quickly moving away from black-box capabilities, while deliberately putting decision makers back in the driver’s seat. That’s not just about analytic output, but how it was designed, its underlying fidelity and its inherent lineage — so that trusting in analytics isn’t an act of faith.

User consumption

Even after achieving analytics transparency, challenges remain in a number of places: rolling out repeatable applications, creating best-practices, collaborating across organizations, evolving what was built, seamlessly recombining models, and eventually either sharing the strongest content back to the broader community or securely maintaining higher analytic intellectual property. This iterative, responsive approach to user consumption is key to modern analytical success. This is where something like an app store for analytics can really drive user adoption.

This brings me to the new analytic stack. The need for a modern, purpose-built analytic stack is critical. This is a stack that doesn’t worry about the source or shape of the data that is coming into it, but one that is able to ingest structured, unstructured and semi-structured sources seamlessly. One that can create meaningful output, can deliver clear-box predictive analytics and can quickly deploy analytic applications for broader user consumption.

Recently, Gartner released the 2013 BI and Analytics Magic Quadrant, while Wikibon released its 2013 Big Data Market Forecast. Both reports point to a clear signal that even as analytics is taking center stage, yesterday’s BI-ETL-EDW stack is wrong-sided for tomorrow’s needs, and quickly becoming irrelevant.

GigaOM’s Structure: Data conference in New York last week was an even clearer sign that this shift to a new analytic stack is happening. We saw an incredible, yet humbling validation that this future is already here:

• Hadoop (and NoSQL) are significantly disrupting in how we manage data, especially at petabyte scale.
• The rise of R and Stata over black-box analytics in academic circles is a strong leading indicator of where the commercial world is headed.
• Analytic consumption is beginning to move away from just data scientists to analysts and end-users via pre-packaged content and applications.

As this new analytic stack emerges, the big data community will continue be an exciting place for the decade to come.

George Mathew is president and COO of Alteryx. You can follow him on Twitter at @gkm1.

Feature image courtesy of Shutterstock user ramcreations.

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Based in San Francisco and Santa Cruz, Calif., Looker has developed LookML, a proprietary language based on the SQL programming language for relational data, to enable users with little to no development background to make their own custom SQL queries of sales data. Once a customer signs up, Looker’s analysts review the customer’s data and, over a few days, custom-build the tool with options for querying the customer’s various databases in specific ways, said Lloyd Tabb, Looker’s founder and CEO. Once in place, Looker can run on a customer’s on-premise hardware or on hosted servers.

Many companies offer business analytics tools, and they come in different flavors. Oracle, IBM and other legacy IT vendors offer data warehouse appliances, although those can take engineers months to implement, Tabb said. Other BI vendors, including GoodData, Mixpanel and Tableau, can store customer data in the clouds, but those display bigger-picture trends in event data and don’t correlate well with user data, which aren’t in clouds, Tabb said. And then there’s Redshift, Amazon Web Services’ new data warehouse product.

Despite all the competition, Tabb believes Looker has a place in the market. The company, which is emerging from stealth mode about a year after its establishment, has more than 20 customers, including Simply Hired.

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The open-source presentation of Query Builder, which permits existing DataSift customers’ developers to simplify the tool’s appearance and functionality, might seem like a matter of crossing big data with even more data. But it’s an important step in trying to prompt business decisions based on what companies can learn about users of Twitter, Facebook and other outlets, not just see what people are saying. Social media analysis becomes more actionable and worthwhile with this sort of functionality.

Plenty of companies ask Twitter to filter out certain parts of its enormous data set. But DataSift is one of just two companies licensed to syndicate the firehose of all Twitter feeds. (The other is Gnip.) Its internet-based Query Builder service also allows customers to run natural-language processing off the entire Twitter firehose and adjust it on the fly in several ways. The processing requires a massive amount of storage, to the tune of 1.3 petabytes, said Nick Halstead, DataSift’s founder and chief technology officer. With the open-source versions, developers can add the Query Builder’s streams and processing to business-intelligence platforms, and users won’t even be able to tell it’s running in the background, Halstead said.

With Query Builder, which was announced in August, users can also pull in Amazon forum messages, YouTube comments, bitly links, Topix posts, Facebook status updates and other social statements, in addition to tweets. The data streams and insight on them all cost subscribing customers $3,000 or more per month. Those users will be able to use open-source versions and get more employees on board.

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James Eiloart started in his post on Monday, with expansion in the region being his primary goal. He was previously at Alterian, where he spent nine years building that company’s cloud-based marketing business in EMEA. Now he’s on the data warpath. He told me:

“Tableau has been in Europe only for a year or so. We started relatively small, but Tableau has established an interesting foothold in Europe. It’s quite clear that Tableau sees real market demand in Europe, in the same way as we’ve seen market demand in the US … In some ways, the European economies make those problems exacerbated.

“We get a sense that, when the belt is being pulled in tight, finding affordable technology that helps you make business decisions becomes a higher priority.”

It goes without saying that Tableau ranks pretty highly when it comes to data visualization: after all, the company counts Apple, Skype and eBay among its customers, and it’s also gained exposure through usage by the likes of data-happy journalistic outlets such as The Guardian.

However, it’s not as though Europe doesn’t have its own big data and BI players, notably Germany’s SAP. Here, Eiloart touted Tableau’s targeting of the business rather than IT professional as a differentiator, along with the fact that Tableau scales down to more small-time organizations.

“I think the vast majority of traditional competitors tend to be pretty complicated,” he claimed. “They also tend to be quite expensive, and organizations don’t want to invest huge amounts of money. They want to buy tech in a small way, then if it’s providing value, they will spend more.”

In terms of specific expansion plans, Eiloart said more information will come out around the time of Tableau’s March release of version 8, which will bring features suited to larger-scale enterprise deployments — according to Eiloart, some large customers are hitting scalability issues, so this should fix that.

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