“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.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• NewNet Q4: Platform mania and social commerce shakeout
• NewNet Q4: Platform mania and social commerce shakeout
• The importance of putting the U and I in visualization

The company’s product, Spire, which provided full SQL support on top of the HBase NoSQL database, was one of the first products to try to blend Hadoop’s scalability with the robustness and familiarity of SQL. That’s now an increasingly crowded space (and has grown since that linked graphic was created). In March, Drawn to Scale expanded its support to MongoDB, as well.

I wasn’t shocked when Stephens told me the news — questions about the four-year-old company’s financial health had been swirling for a while — but to hear of its financial woes was a bit surprising. His account in the post pretty much echoes what I had heard from others:

“It seemed we had everything going for us — paid customers such as American Express, Orange Telecom, Flurry, and 4 others. Our technology worked brilliantly, we had a big hiring pipeline, and we had great media presence against our competitors who raised 10-100x more cash.”

He added:

“Yet five days before we signed term sheets for a big A round or sold the company, we started getting hit by a series of black swans — and we just didn’t have what we needed to recover. I’ll leave the public detail at that level, but I will say that paying employees’ health insurance out of your meager savings is a powerful incentive to change course.”

Up to this point, the company had raised $925,000 from RTP Ventures, IA Ventures and SK Ventures. There’s no word yet on what will come of the company’s intellectual property.

As Stephens — who’s now doing an entrepreneur-in-residence gig at Ping Identity and helping out other startups (including popular wardrobe app Cloth) — succinctly put it during a phone discussion, “We just don’t have the horsepower to keep running the company.”

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Scaling Hadoop clusters: the role of cluster management
• The importance of putting the U and I in visualization
• 2012: The Hadoop infrastructure market booms

The topic arose at the clouderati-filled Cloud 2020 meetup in Las Vegas last week (where someone suggested we’ll have a cloud duopoly of Amazon Web Services and Google) and it’s back in the public eye again this week with the general availability of Google Compute Engine. I think we might get an idea how the cloud computing market will play out by looking at the fast-food industry.

The analogy goes like this: Fast food restaurants offer their consumers essentially the same things as public clouds offer their customers – convenience, speed, standardization, flexibility and everything else that comes with not having to prepare a meal from scratch or deploy applications on physical gear. And if all anyone wanted was fast, cheap hamburgers, fries and maybe some sort of chicken sandwich, the more than 33,000 McDonald’s across the world would probably do the trick.

However, when I come to any major intersection in a big city (and even in some small towns), I usually see no less than two national fast food chains taking up corner real estate. If I drive a little down the road, I’ll likely see a few more, and possibly some regional chains thrown in, as well.

Not all hamburgers are created equal, it seems.

Why should cloud computing be any different? If all anyone wanted was a virtual server, they’d probably go with the omnipresent Amazon Web Services. But when features, price, security, network connectivity and related services come into play, it becomes easy to see why there’s such an appetite for more options.

Amazon is to McDonald’s as Google is to …

Amazon Web Services = McDonald’s and Yum Brands rolled into one: AWS is to the cloud what McDonald’s is to fast food. It was the first, it’s the biggest and it’s the best known. All things being equal, there would be no reason for anyone to go anywhere else for cloud computing because AWS delivers reasonable services at a fair price (sometimes downright cheap), is omnipresent and can pretty much handle whatever scale you throw at it.

Only, if we consider the virtual server the hamburger of public cloud, the object store the French fries and the cloud database a chicken sandwich, AWS starts to look like a lot more than just a McDonald’s. You might look at it more like Yum Brands, the parent company of Taco Bell, KFC and Pizza Hut. The Amazon platform is about far more than just machine images and some standard storage and database features. It has myriad services covering everything from configuration to big data, and they’re all designed to integrate tightly with one another — like one of those KFC/Taco Bell combination restaurants that dot the urban landscape.

AWS, like McDonald’s, is the undisputed champion. Source: Wikipedia Commons

Rackspace = Wendy’s: Wendy’s is the No. 2 fast-food franchise in the United States, a title I think Rackspace probably holds in the cloud space (although assessing cloud market share is a little more difficult than assessing fast-food market share). And much like Wendy’s places a premium on the quality of its products, Rackspace places a premium on the quality of its service. CEO Lanham Napier has gone so far as to say it’s “playing a different game” than Amazon.

What he means is that Rackspace doesn’t need to compete with AWS by constantly driving down prices because Rackspace customers value service and will pay for it. Maybe, but the company might take a hint from what’s happening with Wendy’s as it struggles to maintain its No. 2 status against a feisty Burger King that’s largely following the McDonald’s playbook. If market share is important, higher prices aren’t often the best recipe for maintaining it.

The Angry Whopper, like App Engine, probably isn’t for everyone.

Google = Burger King: That cloud version of Burger King nipping at Rackspace’s heels is Google. It already has all the standard fare in servers, storage and databases, but it’s also hipper than the rest (or at least it tries to be), it takes some chances on product design (sometimes to the love-it-or-hate-it extreme) and, like Burger King with the Whopper, what it does well, it does really well. In Google’s case, that’s perform at scale.

If Google keeps adding services and cutting the costs of everything, there’s no reason it can’t become the world’s No. 2 cloud provider — some have already bestowed that honor upon it — and maybe challenge AWS a decade down the road.

Microsoft = Arby’s: Despite Microsoft’s best efforts to market it otherwise, Windows Azure is still largely viewed as a cloud platform for running .NET applications and generally doing all things Windows. Not that that’s a bad thing — a lot of people really like Windows and, by many accounts, Windows Azure is a fine platform. It’s like going to Arby’s: the menu offers a lot of things, but you go for the roast beef.

Joyent, Virtustream, CloudSigma et al = In-N-Out Burger, Culvers, Five Guys et al: These cloud providers, like their analogous restaurant chains, are damn good at what they do and their patrons are loyal. They’re typically designed for maximum performance, maybe security, too, and will play around with new infrastructural or programming components in order to maintain their edge. They might even be the best at certain things and have some major customers (I’ve seen Maseratis leaving the In-N-Out drive-thru), but cost, geography or the desire to get a chicken sandwich, too, limit the number of users they can attract.

Yes, In-N-Out is delicious — and that’s about the entire menu.

VMware = Del Taco: According to my colleage Barb Darrow, VMware’s new VMware vCloud Hybrid Service will “be run from partner data centers and sold by VMware’s channel but managed by VMware.” Del Taco sounds like a Mexican place but also has hamburgers, fries, shakes and even iced coffee. And I don’t know anyone who eats there.

OpenStack = Frozen French fries, or cheeseburger-flavored Doritos: It really depends on who you ask (some would even say it’s like kale). If you’re grilling burgers and cooking fries, you’re essentially trying to recreate the fast-food experience at home. On the bright side, when you’re making the hamburger patties and cooking the fries, you can control how much salt you add and ensure everyone who handles them washes their hands. It might turn out great, but it’s never really the same.

Perhaps I’m being overly pessimistic, but I’m beginning to suspect that OpenStack-based public clouds (of the non-Rackspace( rax) variety) will end up being a lot like cheeseburger-flavored Doritos. In name, they’re like cheeseburgers, but after a few bites you’re left saying, “Hey, Doritos doesn’t make cheeseburgers …”

Everyone else = everyone else: Even after all this, we’re still left a bunch of different cloud providers and a bunch of different fast food chains. You might compare the telcos to Jack in the Box, Carl’s Jr. and Hardees in that they’re big and make money, but they’re pretty much non-factors in the grand scheme of things. Then there are your various web hosts and others, which might compare with some local chain restaurants. And different countries will certainly have their own cloud providers just like they have their own takes on fast food.

In the end, though, it’s just hard to see how cloud computing becomes a two-horse race any more than the fast-food industry is a two-horse race. Sure, there are three clear leaders (with No. 1 having a big lead), but there’s plenty of business to go around because aside from some core similarities, no two providers are the same. And as long as more applications are developed and need a cloud to call home, there will be developers and CIOs with very different ideas of what makes a cloud platform great.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Cloud computing infrastructure: 2012 and beyond
• Infrastructure Q1: IaaS Comes Down to Earth; Big Data Takes Flight
• Takeaways from the second quarter in cloud and data

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.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• The importance of putting the U and I in visualization
• 4 iPad apps to help wrangle data
• Health care and big data in 2012

AWS has a managed NoSQL database service called DynamoDB that’s replicated across three availability zones to ensure its stays up. Google’s Cloud Datastore sounds eerily similar, according to the product’s website (although Google calls its product “NoSQL-like). It’s fully managed, built for speed and scale and is replicated across data centers. For some queries, Google even promises that Cloud Datastore will support ACID transactions.

Although the services advertise similar features in terms of availability and scalability, they’re quite different technically. Cloud Datastore is based on Google’s BigTable database (and a library called Megastore on top of it) while DynamoDB is based on Amazon’s Dynamo database. You can get details on Datastore  and how it works here. Pricing information is available here.

If its goal is to compete with AWS, though, Google’s cloud platform still has a long way to go. Yes, it has most of the key services in place and even some seeming advantages in certain areas, but it’s lacking the incredible breadth of services AWS offers — everything from virtual server instances to a devops service to a hosted data warehouse. It’s also lacking a seven-year reputation for being an all-around reliable platform and an ever-growing list of large-enterprise users.

Of course, there’s also an argument to be made that Google doesn’t really have to compete with AWS at all when it comes to cloud computing. AWS made a name for itself by  taking all the new workloads from startups and corporate developers who wanted to build new types of applications and didn’t want to deal with the IT department; Google has the same opportunity ahead of it. New programming languages like Go and the unique nature of the rest of Google’s services, Cloud Datastore included, could make it the go-to place for a class of developers that likes to push the envelope in terms of application design.

Oh, and Google has a little ace up its sleeve called Android. If someone is so inclined to develop mobile applications for the most-popular mobile operating system on the planet, there are worse places to host them.

This post was updated at 5:35 p.m. to clarify that DynamoDB and Cloud Datastore are based on different underlying technologies.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Infrastructure Q1: Cloud and big data woo enterprises
• Amazon’s DynamoDB: rattling the cloud market
• Emerging trends in the non-relational database market

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.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• A near-term outlook for big data
• Cloud computing infrastructure: 2012 and beyond
• Infrastructure Q1: Cloud and big data woo enterprises

Graph analysis is among the hottest techniques around for making sense of large datasets, primarily by determining how tightly different data points are related or how similar they are. The term “graph” came into the broader lexicon along with social networks, which built social graphs to assess the relationships among their millions of users, but the technique has much broader uses.

My LinkedIn social graph

Guestrin said GraphLab’s algorithms are used in a lot of recommender systems, but he also cites fraud detection in banking networks and intrusion detection in computer networks as potential applications. We’ve covered graphs as the analytical model of choice for everything from content recommendation to tracking lab work in genomics. Really, though — especially when combined with machine learning — graph analysis can be applied to anything where there’s too much data for a person to possibly analyze the relationships between every point.

One of Ayasdi’s graph-like data maps

Google also famously uses a graph-processing system called Pregel as part of PageRank. Although a number of graph databases and other projects have popped up in the past few years, Guestrin said GraphLab is actually a contemporary of Pregel. He and some colleagues at Carnegie Mellon built a small system for their lab about five years ago, then released it into the open-source world with few expectations that it would catch on. Now, he added, Pandora and WalmartLabs are among the project’s user base.

Among those other projects are graph databases such as Giraph (an open source, Hadoop-based Pregel clone developed at Facebook) and Neo4j (which also has a commercial arm, called Neo Technology), as well as Twitter’s Cassovary and fellow University of Washington project Grappa. Guestrin said GraphLab can work with most of them, particularly if they’re not designed to do machine learning at scale like GraphLab is. Some efforts, he noted, are focused on simply storing data in graph form (e.g., databases) or in providing simple graph analysis.

As for when we’ll actually see the results of the effort to commercialize GraphLab, Guestrin said it will be a while. Right now, he’s focused on the next open source release of GraphLab in July. However, the company will begin engaging with commercial users over the next several months to determine what types of features they would expect in commercial graph-analysis software.

The bigger question to come out of all this graph activity, though, is how big a market we’ll ultimately see for graph-analysis or any other specific technique. As companies get more comfortable with big data from a technical standpoint, they’re getting more interested in the different types of analysis it allows for too. This is evidenced by the quest to make Hadoop support myriad processing frameworks aside from MapReduce.

We already have a handful of commercial graph products on the market — including an industrial grade one called YarcData from supercomputer maker Cray — but how many will there eventually be? And if graph analysis is all the rage right now, what comes next?

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• A near-term outlook for big data
• 12 tech leaders’ resolutions for 2012
• Connected world: the consumer technology revolution

Word that Lucky Sort is shutting down and that several of its team are joining Twitter’s revenue engineering department suggests this is exactly what the acquisition aims to accomplish.

As it stands, companies use Twitter as a way to track how people are talking about them and maybe, if they’re really advanced, do some sentiment analysis. If they’re willing to pay a third party, Datasift and Gnip are more than happy to broaden marketers’ views to encompass the entirety of Twitter’s data, both real-time and historical. What companies really can’t do, though, is run their own advanced analytics about topics straight from the Twitter platform.

One view of the Lucky Sort dashboard

The value proposition from such a product should be obvious at this point. Facebook, Google and Yahoo all collect a lot of data about how people are using their platforms and what topics are trending, and they all offer it up via a variety of products targeting marketing types and the public at large. If Twitter wants to be taken seriously as a venue for advertising budgets and a platform for measuring the pulse of the nation, people need to be able to ask questions of its data without relying on an intermediary or the occasional Twitter blog post.

As a journalist, I’d love to have access to this type of tool to track trending topics in real time and spot possible stories as they’re happening. The appeal to marketers should be obvious. As IBM’s Erick Brethenoux told me recently, “[Marketers] talk a good game about social data. Very few actually leverage it effectively today.”

At Twitter, though, data is a slightly different beast than at other web companies. Twitter’s value lies largely in real-time data — topics can be peak, crest and all but vanish within a 48-hour window. This situation has hampered some of Twitter’s efforts to surface optimal search results, and it has spurred the decision to buy companies such as Backtype (for its streaming-processing Storm technology) and parallel-processing startup Ubalo.

The latter move, which happened last week, should help Twitter’s development team create new features without worrying about the intricacies of making them run — and run fast — across a cluster of machines. (You can learn a lot more about how companies such as Google, Facebook and Box are rethinking infrastructure to handle their unique data needs at our Structure conference next month in San Francisco.)

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Why the next front in big data might be psychological
• Finding the Value in Social Media Data
• Listening platforms: finding the value in social media data

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.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• The importance of putting the U and I in visualization
• Cloud computing and trickle-down analytics
• A near-term outlook for big data

Taken alone, ScraperWiki isn’t that big of a deal, but it’s part of a huge revolution that has been called the democratization of data. More data is becoming available all the time — whether from the government, corportations or even our own lives — only it’s not of much use unless you’re able to do something with it. ScraperWiki is now one of a growing list of tools dedicated to helping everyone, not just expert data analysts or coders, analyze — and, in its case, generate — the data that matters to them.

After noticing a particularly large numbers of tweets in my stream about flight delays yesterday, I thought I’d test out ScraperWiki’s new Twitter search function by gathering a bunch of tweets directed to @United. The results — from 1,697 tweets dating back to May 3 — are pretty fun to play with, if not that surprising. (Also, I have no idea how far back the tweet search will go or how long it will take using the free account, which is limited to 30 minutes of compute time a day. I just stopped at some point so I could start digging in.)

First things first, I ran my query. Here’s what the data looks like viewed in a table in the ScraperWiki app.

Next, it’s a matter of analyzing it. ScraperWiki lets you view it in a table (like above), export it to Excel or query it using SQL, and will also summarize it for you. This being Twitter data, the natural thing to do seemed to be analyzing it for sentiment. One simple way to do this right inside the ScraperWiki table is to search for a particular term that might suggest joy or anger. I chose a certain four-letter word that begins with f.

Surprisingly, I only found eight instances. Here’s my favorite: “Your Customer Service is better than a hooker. I paid a bunch of money and you’re still…” (You probably get the idea.)

But if you read my “data for dummies” post from January, you know that we mere mortals have tools at our disposal for dealing with text data in a more refined way. IBM’s Many Eyes service won’t let me score tweets for sentiment, but I can get a pretty good idea overall by looking at how words are used. For this job, though, a simple word cloud won’t work, even after filtering out common words, @united and other obvious terms. Think of how “thanks” can be used sarcastically and you can see why.

Using the customized word tree, you can see that “thanks” sometimes means “thanks.” Other times, not so much. I know it’s easy to dwell on the negative, but consider this: “worst” had 28 hits while “best” had 15. One of those was referring to Tito’s vodka and at least three were referring to skyline views. (Click here to access it and search by whatever word you want.)

Here’s a phrase net filtering the results by phrases where the word “for” connects two words.

Anyhow, this was just a fast, simple and fairly crude example of what ScraperWiki now allows users to do, and how that resulting data can be combined with other tools to analyze and visualize it. Obviously, it’s more powerful if you can code, but new tools are supposedly on the way (remember, this is just a beta version) that should make it easier to scrape data from even more sources.

In the long term, though, services like ScraperWiki should become a lot more valuable as tools for helping us generate and analyze data rather than just believe what we’re told. Want to improve your small business, put your life in context or perhaps just write the best book report your teacher has ever seen? It’s getting easier every day.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• AWS Storage Gateway jolts cloud-storage ecosystem
• 4 iPad apps to help wrangle data
• Will Hadoop Vendors Profit from Banks’ Big Data Woes?