Since then, Accel has put a name on the types of companies it’s seeking to fund with the new allocation — namely, those selling what it calls “data-driven software.” That’s a fancy way of saying that it’s not looking to fund infrastructure-level software such as Hadoop or NoSQL databases, but rather software that leverages these technologies and others in order to make analytics simpler. It wants to fund startups targeting business users rather than data scientists.

Accel Partner Ping Li (second from right) at Structure 2011. (c) Pinar Ozger

This type of company isn’t too difficult to come by anymore. Just about everywhere you look, someone is trying to put a big data spin on an old problem or invent some new methods for doing business intelligence. Accel has recently funded a number of them including RelateIQ, Opower, Sumo Logic  and Causata. Among the non-Accel-funded startups GigaOM has covered in just the past few months are Ayasdi, Wise.io, Spinnakr, Statwing and BloomReach.

All this interest in data-driven software is no doubt inspired by the proven utility and wildly successful initial public offerings by enterprise data software companies such as Splunk and Tableau. Entrepreneurs can see the value in rethinking legacy business software or processes for the era of big data and cloud computing, and investors have dollar signs in their eyes as they try to get a piece of the most-promising companies.

As with all trends, much of this startup and investing activity will prove to be overkill, but there’s no denying the promise that the right products have for everyone involved. Businesses really are hurting for better ways to make sense of all the data they’re generating and being exposed to, and they’ll pay handsomely to software vendors that can solve the problem.

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The company took so long to officially launch, CEO Eric Kish told me, because it took such a long time to build. The 28msec history goes like this: The early investors are database industry veterans (one was employee No. 6 at Oracle) who, at some point in 2006, envisioned an explosion in data formats and databases. Their solution was to create a platform able to extract data from any of these sources, transform it into a standard format, and then let users analyze it using a single query language that looks a lot like the SQL they already know. 28msec is based on the open source JSONiq and Zorba query languages and will be available as a cloud service.

That’s about all Kish is willing to spill right now with regard to the technology.

As for the company itself, it has been staffed thus far primarily by Ph.Ds. in query technologies from ETH Zurich in Switzerland, where co-founder Donald Kossmann is a professor. Every year since 28msec was founded, it has hired one of his graduates to help build the product. The company brought on Kish, a serial entrepreneur, as CEO in 2012.

28msec was originally based in Zurich, but is in the process of shifting its base to Palo Alto, where Kish lives. It has raised $5.5 million in capital from friends and family, and already has paying customers.

As for the name, 28msec, it’s a reference to the time it takes for a database to access data stored on a hard disk. After the headquarters, maybe that name will be the next thing to change given the prevalence of flash and RAM as database storage media. “Seven years later,” Kish acknowledged, “it’s not relevant anymore.”

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If you’re unfamiliar with Hive, it’s a project that Facebook developed in 2008 to make Hadoop function more like a traditional enterprise data warehouse. Hive stores data inside the Hadoop Distributed File System in structured format, and then allows users to query it using a language very similar to SQL. Until very recently, Hive has been the de facto method for querying (in a traditional sense) data stored in Hadoop, and it has proven immensely popular as more companies have begun tackling their big data woes with Hadoop.

Hive wasn’t built for speed

However, as more companies got used to Hadoop, they also began to notice its shortcomings. One of them is around MapReduce, a powerful but not-exactly-speedy method of processing data that requires running the job across every node in the cluster in order to find the right data. Although the Hive interface is that of a SQL query, it relies on on MapReduce as the processing engine.

(For more on how Hadoop and its flavor of MapReduce came to be, read this post on the history of Hadoop. To see me speak with Google Fellow and MapReduce creator Jeff Dean about how far Google has moved from a MapReduce-centric computing model, come to Structure next month.)

Users wanted faster, more-interactive query processing on top of Hadoop, similar to what they had grown accustomed to with data warehouse systems such as Teradata, Greenplum and Netezza. Hadoop vendors such as Cloudera (with Impala), MapR (with Drill), IBM (with Big SQL) — as well as a spate of startups — have obliged with their own new technologies that in various ways blend the familiarity of SQL with the scalability of Hadoop. EMC Greenplum, now Pivotal, has transplanted its existing database system inside of Hadoop.

Even Qubole, a cloud-based startup from Hive creators Ashish Thusoo and Joydeep Sen Sarma, is keeping an eye on how projects such as Impala and Shark (from the University of California, Berkeley’s AMPLab) might factor into its plans.

Giving Hive a better “Stinger”

Hortonworks, the Yahoo spinoff dedicated to driving the Apache Hadoop bus, is sticking with Hive. But is has a plan, and a point.

Essentially, VP of Products Bob Page told me during a recent briefing, “It just makes more sense from our view to have everything done in one place.” He means that Hive is already the method by which most people are already comfortable using SQL to access Hadoop data, so there’s no use rocking the boat by adding yet another technology into the mix. Hortonworks will just make Hive faster to the point (100x) where it’s at least in the ballpark of what these entirely new systems are capable of doing, but where users still use the same tools for interactive and batch queries.

It has in place a three-phase plan, under the “Stinger” codename, in order to make this happen. The first phase, now available as part of the Hive 0.11 release, is a new set of analytic functions and a columnar file format that Page says has resulted in a 50x performance increase over the previous version. The next phase is to move YARN Hive off of MapReduce and onto a still-under-development processing framework called Tez.

“You’ll see phase two come to bear later this year,” Page said, once YARN — a new resource manager that lets Hadoop clusters run multiple processing engines simultaneously — is ready for production.

The third phase is a whole new vector query engine for Hive and new tools for intelligent query planning. Page didn’t have a target date in mind for that phase, except to note that “we’re not talking about a five-year cycle.”

SQL isn’t the end game for Hadoop

It would be easy to dismiss Page’s and Hortonworks’ optimism about Stinger as a sweet lemons type of rationalization — the company was founded around Apache Hadoop and can’t really go about developing entirely new products outside that foundation — but they also appear to have their eyes focused on a future where SQL isn’t too big a differentiator.

SQL is the way folks used to data for the last 30 years can see how Hadoop fits in their environment, Page said, but the compelling thing about Hadoop “is it really unlocks a new way about how one thinks about storing and processing data.” Once YARN is ready to go, he added, there will be new avenues of innovation in areas like graph analysis and stream processing.

Page comes from a place of credibility when he talks about this evolution in thinking. Before coming to Hortonworks in March, he was vice president of analytics platform and delivery at eBay, a company that knows its way around big data. When people get all their data in one place, they want to do more things with it, he explained. The thinking becomes less about using Hadoop to lower cost and more about “How do I use Hadoop to increase my top line?”.

Besides, Page noted (echoing the sentiment of just about everybody else in the Hadoop space, including Cloudera CEO Mike Olson), even as companies turn Hadoop into their primary data store, it’s difficult to see Hadoop ever entirely replacing high-value relational data warehouse systems like Teradata. One could argue, then, that there’s no real purpose in trying too hard to match those systems in terms of capabilities.

At eBay, he said, they ran an in-depth analysis to see if it was economically or technologically feasible to collapse its big data workloads onto a single system. eBay has dozens of petabytes stored in Hadoop and possibly more within various Teradata appliances. The result: “We just couldn’t find a way in which we could justify collapsing everything we do into one system.”

Feature image courtesy of Shutterstock user vblinov.

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“We’re thrilled,” Tableau co-founder and CEO Christian Chabot told me during a call after the market closed. One should hope so.

Chabot and his fellow co-founders stand to make a lot of money if today’s closing price holds up, as does its sole investor NEA. The firm put $15 million into Tableau since it launched in 2003, and has rode that sum to profitability and more than $127 million in annual revenue.

Here’s a quick chart (made using Tableau Public) showing who owns how many share and what they’re potentially worth.

The company didn’t really need more capital to operate, Chabot said, but one of the primary drivers was to raise awareness of the company. It has about 12,000 customers, he said, but there are millions more possible users. As part of attracting them, the company is going to expand globally and is working to improve its reach across mobile devices, the cloud and the Mac operating system.

“I don’t believe in the this whole ‘or’ philosophy with computers,” Chabot said. “It’s ‘and’” — meaning people will use desktops and tablets and smartphones.

More prominence and more users singing its praises might also dispel the notion that Tableau is just about visualization. It has some fairly advanced features under the covers (as a commenter to my earlier post about the company’s influence pointed out), even if they’re hidden by the relatively simple user experience.

“Tableau is not a visualization company, per se, it’s really an analytics company,” Chabot said.

However, if the company really wants to expand its reach to everyone one who wants to gain knowledge from data — something Chabot calls a “timeless human need” — it might actually need to get simpler. More marketing can let potential business users know about new features like forecasting and data-extraction, but it won’t make a dentist is Des Moines better at formatting his data.

After raising $254 million in its IPO, though, Tableau is in a good place to do whatever it has to.

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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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There are two primary reasons why, the first of which is obvious: Companies are tired of managing applications and infrastructure, so something that optimizes a common task using techniques they don’t know on servers they don’t have to manage is probably compelling. It’s called cloud computing.

The other reason is that the big part of big data really is important if you want to get a really clear picture of what’s happening in any given space. While no single end-user company can (or likely would) address search-engine optimization, for example, by building a massive store comprised of data from hundreds or thousands of companies as well as the entire web, a cloud service dedicated to that specific task can.

From web security to systems management, we’re already seeing how centralized data stores provide SaaS companies a broad view into what’s happening that can then be filtered down to serve each individual customer’s specific situation. BloomReach, a SaaS startup that helps companies optimize web-page content, is another good example of this principle in action.

How do you say, “cotton maxi dress”

Ideally, BloomReach Head of Marketing Joelle Kaufman told me, the company wants to help customers ensure they get found in web searches by making sure they’re not invisible (buried deep down), irrelevant (not saying anything meaningful on their sites) or incompatible (not speaking their consumers’ language). On Tuesday, the company announced a new feature called Continuous Quality Management, which lets customers continuously monitor their pages to ensure they’re still featuring the right products and the right terminology. It’s the latest addition to a seemingly useful service that’s built atop a big data foundation few — if any — of its customers would ever attempt to build themselves.

BloomReach is able to help companies optimize their sites because it’s constantly crawling the web in order to figure out how everyone else is describing their content, laying out their pages and structuring their links. Running on the Amazon Web Services cloud, BloomReach runs more than 1,000 Hadoop jobs a day that process about 5 terabytes of data and a billion data points about users’ site behavior. With the latter, co-founder and CTO Ashutosh Garg explained, the company is trying to figure out who’s visiting sites, what they’re doing, how long they’re spending there and how they’re related in terms of behavior.

“You need to have the right amount of data and from the right places before we can do anything with it,” he said. “… It’s a massive machine learning problem.”

When you consider all the possible ways something could be described or formatted, the scale of the problem becomes more evident. Simple semantic analysis like associating “desk” and “table” is easy, Garg explained, but what if some wants a lightweight camera and you only have its exact weight listed without any indication of how it compares to other options? What if people searching for “smartphones” really mean “Android phones,” but you’re top-loading your results with BlackBerry phones and Windows phones?

Another of Garg’s hypotheticals has to do with consumers’ presentation biases. If, for example, they’re looking at a lot of websites that look the same or focus on the same things (e.g., megapixels for digital cameras), they’ll expect to see the same things from every site.

10 nonillion possibilities: Choose 1.

From a sheer numbers perspective, things get even hairier when you’re trying to determine the relationship between any two pages in order to figure out the best path for links to to take. Garg said this is what computer scientists call an NP-complete problem, which means the amount of time it takes to process the results is exponentially greater than the amount of content you’re analyzing. So, for example, analyzing 40 pages doesn’t take 10 times as long as analyzing 4 pages, but more like 100 times longer.

Actually, BloomReach CEO Raj De Datta gave me another example of this problem when we spoke in early 2012. Here’s how I described it then:

[I]f a company wants to display just 1,000 products across 100 pages, De Datta explained, there are 10-to-the-28th-power (10 octillion) possibilities for how to do that. When it comes time to describe those products, there are 10-to-the-30th-power (10 nonillion) possibilities.

If a website has a million pages, Garg said, “it will take you longer than the life of the universe to solve that problem.”

Where this type of problem arises, BloomReach turns to Monte Carlo simluations, a favorite technique of physicists and Wall Street quants. The method involves running lots of simulations over large data sets in order to determine approximate results in a reasonable time frame. (And if all this isn’t enough computer science and cloud infrastructure for you, I suggest attending our Structure conference in June, which features a who’s who list of speakers, including Google’s Jeff Dean, Facebook’s Jay Parikh and Netflix’s Adrian Cockroft.)

Different queries, different pages

Things get even trickier when you’re trying to change the content of web pages in real time as people are searching for things. This isn’t the best method for organic search, where pages need to stay pretty consistent with the indexed versions, but it can be ideal in situations such as paid search and mobile. There are millions of ways to segment buyers, Garg explained, and how accurately you assess their intent and display your content can make the all the difference. Whether someone is a new or repeat visitor often matters, as does whether someone is price-conscious (e.g., the query included “cheap”) or perhaps searching for a particular brand.

Source: BloomReach

Around the holidays, the company actually realized something interesting: The bounce rate on queries for things like “gifts for dad” or “gifts for co-workers” was pretty high, but so was the conversion rate. The time to conversion was relatively fast, as well. It turns out, Garg explained, that people don’t like to overthink certain gifts too much, so if something is presented in a visually appealing manner and is within their price range, they’ll buy.

But creating these types of models involves more than meets the eye. For all the talk about machine learning — and machines do a majority of the work for BloomReach — people also play a critical role. A person might know better than a machine whether something was likely purchased as gift, Garg explained, or they might spot the offensive content on the T-shirt the machine decided was ideal.

“Humans are really good at creativity, thinking through stuff,” he said.

Smart humans are also good at knowing when they’re overmatched, which is why SaaS is so valuable in the big data era. CMOs could try doing what BloomReach or similar companies such as DataPop are doing, or they could pay someone to do it much better. Guess which route the smart ones will take.

Feature image courtesy of Shutterstock user Andrea Danti.

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Storage, databases, query processing and algorithms are all extremely important — heck, visualization is next to nothing without them — but in a data-driven world where is obsessed with insights, they’re just the foundational layers. They are to big data what server and network configurations are to mobile-app development on platforms like Parse. If you’re going to find out new things from massive and highly complex data sets, or going to give new types of people the ability to analyze even simple data, the presentation of that data and the ability to create consumable presentations are critical.

With that in mind, here are six startups I’ve seen trying to fundamentally change the way that data is visualized. Some are highly complex under the covers, some are not and none are perfect, but they’re all doing their part to make us rethink what it means to look at data and make spreadsheets and static charts look like relics. (And this list is by no means exhaustive, so feel free to add your favorite visualization tools in the comments.) We’ll be highlighting data visualization at our design-focused RoadMap conference in San Francisco in November (sign up here to get first access to tickets this Summer).

Ayasdi

The idea of network graphs isn’t new, but Ayasdi’s approach to it is. Under the covers, there’s an HBase data store, a technique called topographical topological data analysis and hundreds of machine learning algorithms to churn through complex data sets and determine the similarity among the data points. To the end user, though, there’s a map of the data set that looks a lot like a network graph (only it’s probably not network data) highlighting clusters of related data points that analysts might want to investigate further.

BeyondCORE

BeyondCore actually operates under the same basic premise as Ayasdi — show users the significant correlations so they don’t have to think of the queries that will uncover them — but it uses some different techniques to get there. It uses a different visualization method, too: BeyondCore sticks to standard charts, but actually offers the option of having an avatar talk users through the correlations the software has discovered.

ClearStory

ClearStory has a pretty unique product in the works — even if it’s keeping many details and all of its screenshots under lock and key until its formally launches. Essentially, though, it’s trying to tell stories via visualizations that display mashups of numerous data sources, update automatically when the source data changes, and invoke collaboration and social concepts. Here’s Co-founder and CEO Sharmila Mulligan explaining the idea behind ClearStory at Structure: Data in March.

Datahero

Unlike so many data startups, Datahero isn’t trying to woo people fed up with business-intelligence software or the difficulties of getting insights from Hadoop data. Rather, it’s trying to let people with simple business or personal data make simple charts without ever having to enter an Excel function or worry too much about how their spreadsheets are formatted. Early on, Datahero’s visualizations are still pretty commonplace (bars, pies, plots, etc.), but it’s the ease of creating them that’s so unique.

Platfora

Platfora has undertaken the ambitious task of trying to make analyzing mountains of data stored in Hadoop clusters as easy as analyzing their own Stripe data might be for developers using Datahero. It’s based on a foundation of Hadoop and massively parallel query processing, but is presented like an HTML5 version of current visualization golden boy Tableau that’s all about dragging, dropping, and visually slicing and dicing through data. The latter capability is actually critical in a big data world where there are likely more data points than you can ever digest at once.

Zoomdata

Zoomdata is far from the only analytics company to support mobile devices, but it’s one of the few I know of (Roambi also comes to mind) designed primarily for them. Zoomdata connects to standard business data sources, but takes advantage of touch screens and the D3.js visualization project to offer up some visually interesting charts that are designed to be manipulated like an artist’s palette.

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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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