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Private PaaS: the next generation platform for enterprises

Tue, 18 Jun 2013 06:55:59 +0000

How do you make PaaS work for your enterprise? The answer lies in understanding new models of delivery, such as private PaaS. An enterprise needs a good understanding of its requirements, and this paper aims to help business managers find the best path to understanding and deploying this technology.

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Cloud security market landscape, 2013–2017

Keren Elazari — Tue, 11 Jun 2013 06:55:57 +0000

Cyber threats are now a critical issue affecting the national security of nation countries worldwide. At the same time, the IT world is witnessing a wave of new innovation, and there are numerous business opportunities for technologies built around the emerging market sectors of mobile and cloud computing. These technologies and the companies creating with them will form the future of cloud security over the next several years.

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How big data analytics drives competitive advantage

Editor — Mon, 20 May 2013 06:55:26 +0000

Few companies today have the time or the analytics expertise to apply statistics and complex data modeling to regular or even daily business decisions and operations. Now, however, an ecosystem of companies is emerging to fill this need.

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Tableau prices its stock at $31 per share for Friday’s IPO

Derrick Harris — Fri, 17 May 2013 00:03:48 +0000

Tableau Software has priced shares for its initial public offering on Friday at $31. The company is offering up 5 million shares, while stockholders are offering 3.2 million shares. Tableau co-founder and CEO Christian Chabot will ring the opening bell on the New York Stock Exchange, where the company will list under the symbol “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.

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SAP renames Visual Intelligence “Lumira” and sticks it in the cloud

David Meyer — Mon, 13 May 2013 10:53:10 +0000

SAP really is pushing hard on this cloud thing. Days after the German business software giant announced plans to put its HANA in-memory database into the cloud, it has done the same with its Visual Intelligence product, now renamed “Lumira” (SAP dearly loves renaming its products, and this time it’s gone for “a more human-friendly yet Google-ready name”).

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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How data warehousing is now a cost-effective solution for businesses

nraden — Mon, 13 May 2013 06:55:34 +0000

The new economics of data warehousing provide attractive alternatives in both costs and benefits. While big data gets most of the attention, evolved data warehousing will play an important role for the foreseeable future. In order to be relevant, data-warehouse design and operation need to be simplified, taking advantage of greatly improved hardware, software, and methods.

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Why retailers should forget showrooming and turn to in-store Wi-Fi

laurielamberth — Mon, 06 May 2013 06:55:36 +0000

Showrooming. Everybody’s heard of it by now — conniving shoppers checking out a product in a retail store, then buying it online later for less. But today it makes more sense for retailers to shift their focus away from this practice and focus on harnessing the shopper’s in-store wireless experience for themselves and their brands. In addition to making mobile-data-hungry customers happy, customer-facing in-store Wi-Fi networks will bring the retailers themselves much value.

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How to manage big data without breaking the bank

George Gilbert — Tue, 30 Apr 2013 15:55:18 +0000

In the tsunami of experimentation, investment, and deployment of systems that analyze big data, vendors have seemingly been trying approaches at two extremes—either embracing the Hadoop ecosystem or building increasingly sophisticated query capabilities into database management system (DBMS) engines.For some use cases, there appears to be room for a third approach that lies between the extremes and borrows from the best of each.

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Forget in-memory — SiSense raises $10M for in-chip analytics

Derrick Harris — Wed, 03 Apr 2013 11:30:40 +0000

While the rest of the world is agog about big data and in-memory analytics, SiSense is taking a different tack. It’s rethinking business intelligence with higher-speed analysis on smaller (relatively speaking) data sets by taking advantage of multicore, 64-bit processors. The approach has been paying off with some impressive customer uptake, and on Wednesday SiSense announced a $10 million series B funding round from Battery Ventures along with Opus Capital and Genesis Partners.

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

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

Source: SiSense

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

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

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

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

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

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

Feature image courtesy of Shutterstock user Iscatel.

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DataRPM scores $250K, introduces Google-like big data searching

Jordan Novet — Tue, 02 Apr 2013 18:40:24 +0000

More companies are realizing that analyzing their big data can lead to insights that increase revenue and produce other business breakthroughs. But getting good answers isn’t always easy, often requiring IT administrators to take charge and leaving all but a handful of business executives equipped to use software. One startup has a nice and simple idea for big data analytics: Google-like search.

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

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

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

Feature image courtesy of Shutterstock user anaken2012.

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