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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Put simply, Precog’s technology lets users ask questions of their unstructured data (e.g., stuff sitting in Hadoop, MongoDB or any other non-relational data store) in whatever format it was created — JSON, logfile, XML, what have you. This is different from the standard operating procedure of querying unstructured data — including the current SQL-on-Hadoop craze — which usually involves somehow transforming data into a format that a relational engine can read before beginning the analysis. Precog also features visualizations, charts and reports designed with these new types of data, and presumably larger datasets, in mind.

CEO John De Goes and COO Jeff Carr

However, Founder and CEO John De Goes told me, the company came to realize over the past several months that as much as what it’s doing might fall under the “data science” umbrella, that’s the wrong messaging. Outside of Silicon Valley, he said, “a lot of companies don’t have the technological sophistication to understand the whole data science thing” — they just want to know that they can ask deeper questions of the new data types they’re storing in their NoSQL databases without having to perform ETL operations on it or write a lot of complicated code.

And the bigger those companies are, Precog COO Jeff Carr said, the less likely they are to want a cloud service like Precog initially offered.

So the company took both lessons to heart and is rolling out a line of appliances (physical or virtual) that complement its flagship cloud service, each targeting specific use cases. The first three are social media, web analytics and application data, and the appliances are equipped with baked-in capabilities important to each of those fields. The social media one, for example, will feature advanced sentiment analysis and natural language processing, while the web analytics one will focus on features such as behavioral clustering.

Under the covers, though, each appliance still runs on the broader Precog platform, Carr noted, and someone who buys one just to get started in a specific area can pretty easily (i.e., without reaching “super-coder” status) turn it toward other data types and other types of analysis. But right now, De Goes added, no one really knows what it means to have an analytics product designed for unstructured data, so the appliance approach should make it easier for large enterprises and non-tech companies to digest.

An example of web analytics in Precog.

It’s a “baby steps” situation, explained Carr: “Don’t sit there and try to think about how to solve every problem all at once. Let’s try to sit there and think about data types you know you’re having problems with [now].”

Analyzing data in its native format has advantages beyond just omitting an extra transformation step, though, and the Precog team thinks companies will get hip to these advantages as they begin to understand the analytic aspects of non-relational databases as well as they do the operational aspects. Often times, these will be new use cases, which is why Precog considers itself more complementary to than competitive with traditional data warehouses, SQL-on-Hadoop tools and BI software.

One early customer is using Precog to match up résumé data — often enhanced résumé data — with job openings, which is a tricky proposition in a relational format because résumés can include so much personalized information or content that doesn’t fit into a schema at all, really. Another user, a large telco, is trying to build new data products for its customers by mashing together all sorts of internal and third-party data in numerous formats.

Carr compared the shift to the shift from just flat files to relational data decades ago. “It’s happening again,” he said. “It has to happen again … people are not going to abandon JSON because it does’t fit neatly inside a table.”

Precog is telling the right story around why unstructured analytics matters, but one has to assume there will be a major shakeout in the big data analytics space over the next few years. There are only so many new technologies companies can absorb at once — Hadoop, NoSQL, SQL on Hadoop, unstructured analytics, Platfora, in-memory, stream processing, next-gen analytic databases, etc. — and it’s hard to predict which messages and capabilities will win out.

However, unless Hadoop really does become the lone dumping ground for all non-operational data — regardless the source — technologies like Precog that can act as the analytics layer across numerous data stores would seem to have an advantage.

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Sush.io is pitching itself as “Mint.com meets IFTTT” and — as the list of services that can be plugged into the app attests — it’s very much targeting tech startups at this early stage.

Here’s how co-founder Thomas Guillaumin explained the focus of the app to me:

“The problem is collecting all these services and having a top down view of your finances – how much cash you have, what’s your burn rate… that can really help you run a business better.”

Another indicator of Sush.io’s tech startup focus is the fact that it’s launching with an OS X desktop app first. But, as Guillaumin told me, versions for other desktop and mobile platforms will be out by the end of the year.

The investors in this seed round include Kima Ventures, Jacques-Antoine Granjon (the founder of online flash sale pioneer Vente-privee), the 50 Partners accelerator and Mediastay co-founder Jonathan Zisermann. According to Guillaumin, the cash will be used for the launch and also to hire three more staff members, taking the total (including founders) to five.

Guillaumin said the Sush.io service will operate on a freemium model, with the paid subscription kicking in depending on the number of services you want to add. This will cost between £30-£50 ($46-$76) a month – cheaper than paying a CFO, certainly. Right now the company is hawking its wares in the European startup hubs of Paris, London and Berlin, but in June it intends to push into the U.S., too.

That said, Guillaumin sounds quite wary about the American market due to potential competitors there – namely the Geckoboard-Zapier partnership and even IFTTT itself, on the chance that IFTTT integrates an analytics dashboard at some point.

He added that, once it’s gotten off the ground, Sush.io may develop into a more fully-fledged business intelligence product that adds more KPIs (key performance indicators) to its current financial focus.

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Unlike legacy BI applications that generally connect to Hadoop but otherwise retain their old-school performance limitations, Platfora and its ilk have big data at their core. Platfora is built on Hadoop for scale, but the company also has its own IP around in-memory processing to improve the speed of slicing and dicing through data, and its HTML5 interface provides an easy way to navigate through lots of data points.

Werther (far left) talking SQL on Hadoop at Structure: Data 2013 along with representatives from Qubole, MapR, Hadapt and Facebook.

I’ve compared this general family of products — in which I’d also include ClearStory, Precog, SiSense and Birst, among others I’m sure — to Tableau, albeit slightly (sometimes significantly) rethought and then jacked up on steroids to handle big data scale and/or speed. The big difference with Platfora, though, is that it’s built on top of Hadoop and is therefore part of an even bigger movement around that open source platform and a quest to build native SQL queries into a system designed for MapReduce.

We have been covering Platfora since its inception, from stealth mode to launch, and then a whopping $20 million VC investment in November.

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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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Although the company’s still trying to keep mum about the details, I had a demo recently and can vouch for the cloud-based service in its current state. More impressive than the actual visualizations and analytical process is the variety of data sources the platform can handle. Users can integrate data from nearly any public or private source, and ClearStory will also surface relevant datasets from third-party sources. It’s like a data marketplace baked right into a business intelligence system.

Sharmila Shahani-Mulligan

Co-founder and CEO Sharmila Shahani-Mulligan explained to me how it might work in the case of a video game launch. A company might track activity on its Facebook launch page as it’s happening, as well as on Twitter and from Omniture or Google Analytics. Once players actually start playing, the company could work in data about who’s playing it and where from the Xbox Live service. Over the next couple days or weeks, it might start analyzing its own sales data, marketing spend, reviews from gaming web sites and perhaps data from a service such as Nielsen on consumer awareness.

Shahani-Mulligan said the company’s early users are stoked about being able to analyze and access all the data sources they want from a single platform. “Every customer we’re in front of … every single one of them has a huge appetite for that,” she said.

That’s not to say the analytics experience is subpar, though. Once users choose their data, ClearStory automatically “harmonizes” it for them — which is a fancy way of saying it finds the common metrics (e.g., time and place) so the datasets can be combined without manual labor. At that point, it’s similar in feel to Platfora’s Hadoop-based BI software or a friendlier version of Tableau — pretty visualizations and lots of dragging, dropping and collaboration.

However, just because it might have struck an impressive first blow with its methods for data support and discovery, ClearStory is going to fight the same uphill battle as its well-funded (or, at least well-conceived) peers such as Platfora, Datahero and Chartio. Business users might love them, but legacy BI vendors have large sales teams and track records. And the “next-generation” space is currently filled by the likes of Tableau, QlikView, Jaspersoft and Pentaho, as well as a suite of specialized services for areas such as marketing, media and log file analysis.

But then again, that’s what makes the cloud computing era so great. Business users need better analytics tools, and now they have a plethora of options that should help them become more productive at a price point that even their corporate credit cards can handle. It’s a bottom-up world: Services that really work will become indispensable with their users and ultimately find themselves as a line item on corporate budgets for years to come.

Feature image courtesy of Shutterstock user sellingpix.

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BeyondCore isn’t your average analytics startup. Although the company is just emerging from stealth mode on Tuesday, it has been around since 2004. It hasn’t yet raised a round of venture capital, although it does have some impressive beta customers — including 11 of the Fortune 100. According to Founder and CEO Arijit Sengupta, the company arose from a Harvard Business School project he did with faculty adviser Clayton Christensen focused on how to remove humans from IT processes.

Sengupta wanted to make business analytics a push-button affair, and after eight years he finally thinks he and his team of mathematicians have accomplished that goal. Instead of making users find the needle in the haystack, he wanted to create software that can find the needle (and maybe a few other tiny household items) and present it to the user without ever being told what it’s looking for.

Humans are bad computers

Although Sengupta is quick to point out that the company’s flagship product, Lucid, is not machine learning software, the underlying thesis is similar to that of any company employing machine learning techniques: It doesn’t take long before human beings are overwhelmed by datasets and can’t possibly find all the relevant patterns and correlations. Humans don’t scale, he said during a recent phone call, so ”you cannot solve the big data … problem if humans are core to the process.”

This is the same reason Sengupta is not a big fan of most current BI tools or big data technologies, which he equates to trying to solve an exponential problem with a linear solution. The way most analysts work is they have to create dashboards and PowerPoints and try to prove there’s value in what they’ve found. And, of course, they’re responsible for actually uncovering those insights among the vast expanse of names, numbers and other values sitting in front of them. But if we can remove the human limitation from the equation — at least in the analysis stage — computers can just take over and solve the problem, Sengupta explained.

Once that’s done, he added, “[We can] get to a point where a business user feels like someone’s walking through the analytics and data and helping them find what’s valuable.” That’s exactly what BeyondCore claims Lucid can do.

Sit back and let this avatar take over

At about 1:51 into this video on Lucid, you can see what Sengupta is talking about. Once the user chooses from a pulldown menu what variable against which he wants to analyze the rest of the data, the software takes over and begins analyzing every combination of variables and then calculates which ones have the most-significant effect on the chosen value. At that point, the user has four options for how to proceed, although it’s the one called “Analyst Overview” that really shows what Lucid can do.

A screenshot of the Analyst Overview

A feature I suspect is either awesome or creepy depending on your personality, Analyst Overview actually brings up a presentation in which an animated analyst walks and talks users through the key findings on the analysis. It shows charts, highlights strong correlations and outliers, and generally gives the user a good idea of how to proceed and where to investigate.

After the presentation (or if they decide to skip it), Lucid users can enter Analyst Mode to begin looking at and experimenting with the software’s results. They can add or remove variables that might have led to false positives, maybe test a hypothesis (although, Sengupta is quick to point out, if there is any statistical significance to a hypothesis, Lucid will find it) and create new types of charts. They can add comments to data points or entire analyses, flagging key points or perhaps things that need a second set of eyes.

Statistician Mode

Anyone wanting to test Lucid’s math can enter Statistician Mode, where they’ll see scatter plots, R scores and other information showing how the software came to the results it did. “This is almost like showing our homework,” Sengupta joked, because there’s always someone in the room who doesn’t believe you’ve done the work.

But can it scale?

Update: Curiously, Lucid is one of the few big data products just now hitting the market that isn’t built atop Hadoop. It doesn’t even support Hadoop as a data source out of the gate,  except through partners, which might be a problem as more companies choose Hadoop as their primary data store for massive datasets. But Sengupta doesn’t think this limits Lucid’s effectiveness.

For one, he said, BeyondCore can add native Hadoop support if and when it’s necessary. And regardless where data is stored, as long Lucid can at least read it as key-value pairs, it can analyze it. The processing engine behind Lucid is also massively parallelized, Sengupta explained, so it can easily churn through large datasets like Hadoop MapReduce can. Lucid is primarily available as a cloud service hosted on the HP Cloud , but there’s also a laptop edition capable of handling up to 100 million rows of data.

At any rate, Sengupta noted, analyst activity in Lucid is plenty fast once the initial analysis is done, because all the calculations have already been done. At that point, whenever a user adds, subtracts or otherwise manipulates the results, the system is just pulling up the calculations it has already carried out rather than doing them anew.

One large BeyondCore beta customer in the IT industry used Lucid to analyze 58,000 customer invoices for discrepancies. It took just minutes to perform more than 500,000 calculations across 21,000 variable combinations, Sengupta said, and the software — without having any prior knowledge about what an “invoice discrepancy” was — discovered 30 critical insights that human analysts had never even considered.

In another instance, a large hospital company was interested in figuring out why some patients remained longer at some hospitals than they did at others. Lucid performed more than 900,000 calculations across about 534,000 variable combinations (city, procedure, insurer, line of services, etc.) and uncovered and 247,000 possible patient outcomes to uncover 35 critical insights. It also highlighted the outliers: For one procedure, patients in one city were 9.3 days later than average, while patients in another city were leaving 5.4 days earlier.

Is business ready for the future?

Assuming Lucid lives up to BeyondCore’s claims, an automated solution based on “pure math” is certain to turn a few heads from companies concerned with trimming the fat from their analytics efforts and making sure they’re not leaving anything behind. But getting them to abandon decades of decision-making process, job descriptions and IT investment won’t be easy. At least, it hasn’t been for other companies and whole industries promising world-changing technology products.

Still, Sengupta is understandably optimistic about the future of BeyondCore and how he thinks it can transform the analytics market. He even goes so far as to envision the possibility of combining Lucid with something like Siri to enable deep data analysis using nothing but a smartphone and the human voice. That’s an inspiring vision that seems entirely plausible in the very near future. But in the world of enterprise IT, at least, he might be getting ahead of himself.

Feature image courtesy of Shutterstock user phipatbig.

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Platfora finally took the wraps off its product in October. The product combines Hadoop scale, in-memory speed and HTML5 flexibility, resulting in something that shows Hadoop’s promise as a technology that can be accessible by everyday business users and that can do more than perform batch processing. Essentially, says Founder and CEO Ben Werther, it’s a better way to do data warehousing and business intelligence than what legacy technologies can provide.

Platfora’s focus on disrupting the BI incumbents is actually somewhat unique in the Hadoop space, where most well-funded companies provide the core Hadoop software and actually partner with those legacy vendors in order to position themselves as complementary technologies. Even Impala, Cloudera’s new interactive query engine, is still too slow for true interactivity like what Platfora can provide, Werther said, and just “seems like a lifeline” for the incumbent companies who are now “on the wrong side of history.”

A screenshot of Platfora in action.

If Werther sounds overconfident, maybe he is. Or maybe Platfora really has struck a nerve with the IT buyers and data analysts whose devotion the company will require if it’s going to live up to Werther’s plans of becoming a billion-dollar public company. Platfora has several dozen customers queued up that it just hasn’t had time to engage with yet, he said, and he and others have received standing ovations after presenting the technology to potential users.

Investors seem to get it, too — a good thing considering Platfora’s big plans. Werther said this funding round went from earnest discussions to signed term sheets in three weeks and was generally a stress-free experience. That probably has something to do with the consensus the company has seen among venture capitalists, who project Hadoop will take about 20 percent of a $30 billion legacy BI market and are looking for the startups with the vision to win that business.

However, although Platfora’s legacy BI competition might be many things, they’re not underfunded. “We’re going to be going effectively head to head with some very large incumbents,” Werther said. And it takes the kind of money Platfora is raising in order to build a company that scale to compete with the like of Oracle or Teradata in terms of developing its product, spreading its message and supporting its customers. If those companies want a piece of the big data market — and they do — perhaps we shouldn’t be so fast to dismiss their chances of getting it.

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Ben Werther

Werther, who spent time at DataStax and Greenplum before starting Platfora, describes the current state of business intelligence as being like a double-edged sword. Traditional data warehouses are mature, he explained, but limited because strict processes for adding data and changing schema essentially force analysts to “live inside the constraints of whatever’s available to them.” Hadoop, on the other hand, is irresistible but flawed — it stores lots of raw data without fixed schema, but tools such as Hive that try to add a familiar facade “don’t in any way make it interactive or suitable for general use.”

The state of affairs has Fortune 500 companies “literally screaming out for an answer,” Werther said.

Big, fast data

With its eponymous software product, Platfora thinks it has that answer. It uses Hadoop as a scalable data store from which users can grab data sets and manipulate which variables are shown and how that data set relates to others. Platfora calls this data-management process building a “lens.”

What makes the product really hum, though, is how users can interact with data once it’s visualized into a graph. Like with Tableau, they can drag and drop new variables into a graph and watch it automatically account for them. However, Platfora also utilizes its own massively parallel in-memory database to store more than a terabyte of related metadata, which is the underpinning of what the company calls its “Fractal Cache” technology. It means users can change their minds about what data to include or how it’s related, and then start analyzing it anew without a hitch.

Based on what I saw in a demonstration, Platfora’s HTML5 canvas rendering makes the process as visually rich as it is fast in terms of processing speed. You can highlight a portion of a graph and drill down into just the data points included in that zone, and then drill down even further or just start a whole new analysis using the smaller data set as a the starting point. Using concepts from the “grammar of graphics,” Platfora VP of of Products and Marketing Peter Schlampp said users can create just about any type of visualization they can imagine.

Essentially, Werther said, Platfora has turned Hadoop into a sub-second interactive engine that operates much faster than any Hadoop-to-data-warehouse connector or Hive-based approach could ever hope to do. (Hive is the SQL-like query language developed for Hadoop that companies such as Facebook use to turn Hadoop into a data warehouse for unstructured data.) “At the point where you can synthesize on the fly,” he said, “[legacy BI tools] start to look like relics of another age.”

If legacy dies, who wins?

As proof that its approach works, Werther points to the tens of well-known customers taking part in Platfora’s private beta and the hundreds it hopes to let in now that it has officially taken the lid off the product. One customer, he said, was able to get up and running on a petabyte of data in just four hours. Another, a large media company, is using Platfora to analyze 2.4 petabytes and more than 2 billion user records. In that deployment, Platfora replaced a BI strategy that included Hadoop, Hive, Vertica and Tableau, and that required users to request a new Vertica database every time they wanted to change the data underlying their analyses.

However, there are many similar data environments out there, and getting companies to make the switch to something new won’t necessarily be easy. Even when it’s relatively slow and cumbersome, a combination of Hadoop, a data warehouse and a BI tool generally serves its purpose, even if it’s an old-world stack, Werther acknowledges. And it’s not as if legacy companies such as Informatica or MicroStrategy are going gently into that good night and just letting the big data revolution pass them by.

Even if companies really are looking to make a change, they might also look at more-modern alternatives from vendors including Hadapt, ClearStory, Birst and even Teradata. Although they’re more batch-oriented and less interactive BI, established Hadoop startups such as Datameer and Karmasphere are offering some impressive products as well. Datameer, in particular, has put a focus on letting business users create top-notch graphics to illustrate their data, and we’ll see if young startups such as Datahero decide to tackle big data in the future.

Despite some stiff competition, though, Werther isn’t sweating Platfora’s future. He knows his company has promise, and he knows every one of his competitors is part of a larger movement to disrupt a lucrative market for BI products that analysts estimate at $12 billion a year and growing. “We all win if we can accelerate Hadoop usage,” Werther said.

That’s true even if some companies in the Hadoop distribution space are happy propping up legacy BI vendors for as long as companies still want to buy those products. However, he cautioned, “The old guys never win, they never survive the change.”

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There certainly are a number of companies trying to prove this contention, this week alone. On Tuesday, it was Hadapt improving on its native Hadoop-plus-SQL architecture and adding advanced analytic functions and tight Tableau integration. On Wednesday, it’s Teradata, Birst and startup Splice Machine getting into the act.

Birst: Birst has been making a concerted effort to establish itself as a legitimate BI company by building a suite of on-premise offerings, but it’s moving back to the cloud with a new big data service built atop Hadoop. Essentially, Birst Big Data Services lets companies store unstructured and semi-structured data and then analyze it on the fly using packaged functions that don’t require knowledge of MapReduce or other complex methods. Because it’s Birst, the new service connects to structured relational data stored within Birst’s flagship service and brings visualization tools to new data types.

Splice Machine: Splice Machine, a San Francisco startup that built a SQL database atop the Hadoop Distributed File System on Wednesday announced $4 million in first-round funding from Mohr Davidow Ventures. Like fellow startup Drawn to Scale, Splice Machine promises the best of both worlds — SQL functions and transactions on top of a distributed foundation of HDFS and HBase. That’s a lovely story if it works, as companies can expect flexible schema for unstructured data, massive scalability, as well as a continued bond with their favorite SQL BI products.

Teradata: Teradata has finally done something people have expected it to do for a long time by building an appliance — the aptly named Big Analytics Appliance — that packages Hadoop with the company’s Aster Data database. Teradata bought Aster Data a couple years ago to capitalize on the unstructured data that’s at the core of the big data movement but that doesn’t comport with Teradata’s core data warehouse and analytics business. Aster Data’s claim to fame is its SQL-MapReduce software, which lets users run MapReduce jobs using standard SQL. The whole Aster-Hadoop shebang ties into data stored in Teradata’s flagship database via a connector called SQL-H. The secret sauce is SQL-H, which lets users access Hadoop data, join it with data in Aster and then analyze it.

As impressive as these new offerings sound, we probably haven’t seen anything yet. The rest of the Hadoop ecosystem isn’t blind to what’s happening. At the O’Reilly Strata conference and Hadoop World next week, we should expect to see how some of the bigger players are thinking about answering the very important question of how closely we can align Hadoop with business users’ skills.

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