The Intelligence Inside: Jaspersoft 5

For more than three years, Jaspersoft has envisioned the capabilities we’ve just announced in our v5 platform. Because we’ve always intentionally constrained ourselves by exclusively delivering client (end-user) reporting and analysis functionality inside the web browser, our quest for v5 took longer than we would have wanted. But, we believe that the strengths and advantages of maintaining our simple, pure, web-server-based approach to advanced business intelligence is superior to relying on desktop-specific code or even browser plug-ins, which must be installed and maintained on every computer, preventing the scale and cost advantages Jaspersoft can offer.

So the interface techniques and features we deliver are constrained based on key web client technologies, especially HTML. The trade-offs we’ve lived with in the past, though, are now essentially eliminated, as a new generation of HTML5 ushers in the consistent, advanced visualization and interaction we’ve long-wanted, while allowing us to maintain our pure web-based client delivery model. Satisfaction. Jaspersoft 5 is more than a new pretty face. We have delivered a completely new HTML5 visualization engine that allows a new-level of rich graphics and interaction, but we’re also providing a host of new and more advanced back-end services that make Jaspersoft 5 more surely the intelligence inside apps and business processes. In total, Jaspersoft 5 includes six major new features.

1. Data Exploration 
To enable everyone to become a more capable analyst, the Jaspersoft 5 platform includes stunning HTML5 charts, a new dimensional zoom tool (for exploring data at more or less levels of detail), and the ability to simply change or customize charts and tables to suit a particular type of thought or analysis.

2. Data Virtualization 
Some reporting and analysis applications are best delivered without moving or aggregating data. Instead, the query engine should virtualize those data views and enable reports, dashboards and analytic views to include data from all necessary sources. Jaspersoft 5 includes an advanced data virtualization engine so that building advanced analysis using practically any data source is straightforward, including Big Data sources.

3. Columnar In-Memory Engine 
The JasperReports server has supported in-memory operations for several years. Jaspersoft 5 takes this to a new level with improved performance, features, and now with support for up to a full Terabyte of in-memory data. This means that billions of rows of data can be explored at memory speeds with our new Server.

4. Enhanced Analytics 
To give the power user analyst another reason to use Jaspersoft, we’re now including greater analytic performance, new analytic features (e.g., conditional formatting, relative date filtering, and cross-tab sorting), consistently rich visualization (see #1 above) and broadened access to multi-dimensional data sources. By supporting the latest XML/A standard, we gain certified access to Microsoft SQL Analysis Services (MSAS) data sources in addition to the traditional Mondrian. More power and greater choice equals greater usage.

5. Improved Administration and Monitoring 
To make the lives easier of those who administer and manage a JasperReports Server, we’re now using our own tool to make our Server smarter and simpler. We’ve designed a set of best-practice, interactive reports that display system health and report on the most important elements of usage. Then, we streamlined the installation and upgrade process, so that getting started and staying up-to-date has never been easier. Together, these improvements are good for our customers and our technical team who supports them.

6. PHP Support 
Scripting tools are now the most popular for web application development. The PHP community needs more advanced reporting and analysis tools to make their applications more data-driven. By extending the JasperReports Server API to now include PHP support (via RESTful web service wrappers), we’ve taken an important first step toward supporting this fast-growing world beyond Java. Welcome to Jaspersoft.

Jaspersoft 5 is poised to deliver self-service BI to help many more users answer their own questions, not just because of the beautiful new HTML5 graphing and interaction engine, but because it is designed to be highly embeddable (into apps and business processes) and, maybe most importantly, because it scales so powerfully and affordably. Putting reporting and analytics into the hands of far more users requires this fundamental reset of the BI formula. This is Jaspersoft 5.

I invite you to learn more about Jaspersoft 5 here. And, I look forward to your comments and questions. 

Brian Gentile 
Chief Executive Officer 
Jaspersoft

Big Data: Approaches, Myths & Skills

Last month, my 18-year old daughter asked me about Big Data. This is my first sure sign that a technology has reached a fever pitch in the hype cycle.  Ironically, I found that as I explained this

enterprise IT topic to my daughter, our conversation and the questions she asked did not vary greatly from many conversations I’ve had with other CEOs, journalists, financial analysts and industry colleagues.  Despite how widely Big Data is being covered these days, it appears to me that Big Data is a big mystery to many.

Trying not to be labeled a cynic, I have three big worries about Big Data:

1. My biggest worry is the poor percentage of successful Big Data projects that will emerge as we too quickly throw these new technologies at a wide variety of prospective projects in the enterprise

2. The low success rate of Big Data projects will be amplified by the current hype and subsequent misconceptions about Big Data technologies, and

3. This low project success rate could stay challenged over time because of the relative dearth of

knowledgeable, data-savvy technology and business professionals ready for a world where data are plentiful and analytic skills are not.

Successful Big Data Projects

As organizations race to evaluate and pilot Big Data tools and technologies, in search of an answer to a Big Data opportunity, I’ve seen evidence that architectural steps are being skipped in favor of speed.  Sometimes, speed is good.  In the case of Big Data, building the right data and platform architecture is critical to actually solving the business problem, which means the right amount of thoughtful planning should occur in advance.  Many missteps could be avoided by simply being clear up-front on the business problem (or opportunity) to be solved and how quickly the data must be used to enable a solution (i.e., how much latency is acceptable?).

Recently, I’ve tried to do my part to help explain successful Big Data (technical) architectures by starting with three simple, latency-driven approaches.  The specifics, including an architectural diagram, are described in my recent E-Commerce Times article, entitled “Match the Big Data Job to the Big Data Solution.” We’ve also posted additional graphics and explanation to the Big Data section of the Jaspersoft website.

Big Data Misconceptions (or Myths)

To reduce the hype, first we must overcome the misconceptions. My many conversations on the topic of Big Data yield equally many misconceptions and misunderstanding. Some examples of the most common myths: Big Data is all unstructured, Big Data means Hadoop and Big Data is just for sentiment analysis. Or course, each of these myths is only partially true and requires a deeper understanding of the technologies and their potential uses to gain real clarity.


I’ve recently offered a brief article that seeks to dispel the “Top 5 Myths About Big Data.” Published last month on Mashable. The article has garnered some great comments with the most completewritten by IBM’s James Kobielus. James improves and amplifies several of my major points. I hope you’ll join the conversation.

Analytic Skills Shortage

Worldwide digital content will grow 48% in 2012 (according to IDC), reaching 2.7 zettabytes by the end of the year.   As a result, big data expertise is fast-becoming the “must-have” expertise in every organization.  At the same time, in its 2011 research report, titled “Big data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey offered the following grim statistic:

“By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”

Without the solid analytic skills needed to support a growing array of Big Data projects, the risk potential grows rapidly.  Anyone in or near data science should take the coming skills shortage as a call-to-arms.  Every college and university should be building data analytics coursework into compulsory classes across a wide variety of disciplines and subject areas. Because of its importance, I’ll save this Big Data skills topic as the thesis for a future post.

Despite these primary worries, I remain hopeful (even energized) by the enormous Big Data opportunity ahead of us.  My hope is that, armed with good information and good technology, more Big Data customers and projects will become more quickly successful.

Brian Gentile

Chief Executive Officer

Jaspersoft Corporation

Cloud BI Progress & Pitfalls

In my on-going effort to uncover and discuss key BI industry trends, I recently authored a new article for my TDWI column (called “The BI Revolution”), under the same headline as this post. In that article, I focused on the big market that will emerge for BI in the cloud. Even more importantly, I shed light on the definitional and technological pitfalls that are confusing this market as it seeks to deliver more efficient cloud-based business intelligence.

Rather than address my main points here, I encourage you to read my post at the TDWI website and then add your comments and thoughts here.

Cloud BI = BI for SaaS + BI for PaaS
I note that the cloud as a transformational infrastructure will drive big use of BI for SaaS (on-demand analytical applications) and BI for PaaS (application development and deployment in the cloud). I am less bullish on SaaS BI (on-demand, general-purpose BI in the cloud) because I believe growth will continue to be fueled by BI embedded in data-driven applications, rather than delivered in any standalone use.

We’re constantly tuning the Jaspersoft website on this topic, building out content that seeks to explain, educate and amplify the technological and business benefits of BI in the Cloud. One important point left out of my TDWI post describes Jaspersoft’s focus on and success in BI for PaaS (platform-as-a-service).

Recently, Jaspersoft has been very active in BI for PaaS. We are working with all the major PaaS providers to ensure our BI platform is available within these new cloud-based development and deployment environments. Just last month, Jaspersoft announced an important partnership with Red Hat, making our BI server available immediately in the OpenShift (public cloud) and CloudForms (private cloud) environments. Then, Jaspersoft produced a blog post and video to highlight its support of VMWare’s CloudFoundry PaaS environment, with a more formal announcement pending. Overall, our head of Product & Alliances summed it up best:

“Jaspersoft’s intention is to be the de facto standard in BI for PaaS, enabling the broadest community of software developers to use our tools in their favorite cloud environment,” said Karl Van den Bergh, Vice President of Product & Alliances at Jaspersoft. “We are uniquely positioned to capitalize on this shift of application development to the cloud with our modern architecture, the world’s largest BI community building data-driven applications, and our open source model.”

Through my recent TDWI article and this post, my goal is to clarify the cloudy definitions around Cloud BI, the important pitfalls already witnessed, and the progress we can point to as a sense of optimism for what will be a bright Cloud BI future.

Brian Gentile
Chief Executive Officer
Jaspersoft

Got Big Data?

If competing based on time and information really will drive the next major economic era, then Big Data is real and represents a huge opportunity. If you’re a business analyst or technologist responsible for mapping data to decisions, then the variety, velocity, and volume of data available to you today has never been richer. And, your responsibility has never been greater.

I’ve previously discussed the different classes of data source technologies that can legitimately be used to harness (or tame) big data. Hadoop is one of those technologies, as the most popular software framework associated with this rising trend. Others include NoSQL databases, MPP data stores and even ETL/Data Integration approaches (for moving Big Data by the batch into some more usable format). Each of these technologies align with an appropriate use-case that makes more understandable the variety of products emerging in this world of Big Data.

For simplicity, I like to talk about three popular approaches to connecting to and making use of Big Data for business intelligence reporting and analysis.

Interactive Exploration – the most dynamic because it involves native connectivity directly from the BI tool to the Big Data source and can offer results in near-real-time. Hadoop HBase, Hadoop HDFS, and MongoDB are just three of the most popular data sources to which direct connection would be an advantage.

Direct Batch Reporting – an important and mainstream approach (especially in this early market of Big Data) that relies on tried-and-true SQL access to Big Data. Hadoop Hive is the best known example, but Cassandra offers CQL access that delivers similar results and functionality.

Batch ETL – using extract, transform and load techniques to create a more usable subset of the Big Data is also popular, especially when the insight being sought is less urgent, probably in the order of hours or days after data capture. Most every ETL tool has now been improved to connect to and transform Big Data. Some even integrate nicely with underlying Hadoop technologies (like Pig), making the data steward’s life potentially simpler.

Sometime last year, it occurred to me that Jaspersoft is in a unique position with regard to Big Data. Because of Jaspersoft’s data-agnostic architecture, we’ve quickly offered a broad variety of native Big Data connectors, many of which have been available for more than one year (for free download) . . . and because of our large, growing community of developers (we have more than 260,000 registered community members, growing at about 6,000/month at the time of this writing), we have important data about Big Data. This realization led us to the Big Data Index.

Big Data Index

We’ve tracked the downloads of our Big Data connectors over the last year, charting the ups and downs with each, corresponding to the relative rise and fall of their popularity. Over this time, we’ve seen more than 15,000 downloads, so our view is pretty good. Here’s a static version of the latest data for the four most popular Big Data connector downloads:



During the course of the past year, the Hadoop technologies (HBase & Hive combined) proved the most popular. The fastest growing and the leader at the moment is MongoDB (from 10gen). Cassandra holds a solid and consistent fourth position (which should benefit DataStax, the commercial company behind Cassandra). Many other Big Data connectors are tracked as well, with a dynamic chart updated monthly.

As interest in Big Data grows, so will the potential uses for these technologies that are designed to map this data to decisions and insights. At the moment, I’m just content knowing I have a front-row seat via the Big Data Index.

We’re at the very beginning of this era, which will surely be reliant on more data than we could barely fathom just ten years ago. This is why your thoughts and comments on this topic are appreciated.

Brian Gentile
Chief Executive Officer
Jaspersoft

The New Factors of Production and The Rise of Data-Driven Applications

For the last ten years, I’ve been partially obsessed with the notion that the formula for creating economic value needs to be updated. I’ve worked in the technology industry for 26 years and I’ve seen information systems radically change the landscape of competition and value creation. My most recent article on this topic appears in Forbes under the same title as this post.

Because this article represents just a fraction of my thoughts on this matter, I’d like to revisit the basic premise, which is captured in the excerpt below, and then describe how some of my current experiences at Jaspersoft corroborate this newly posited IT-driven economic theory.

“Classical economic theory describes three primary factors, or inputs, to the production of any good or service: land, labor, and capital. These factors facilitate production, but do not become part of the end product (as a raw material would). While these three factors have been much discussed and extended at different points in economic evolution, I believe that they, in any of the advanced economies of the world today, are vastly antiquated.

Sometime even prior to this new millennium, the primary factors of production have now assuredly become: Time, Information and Capital. I submit that the primary relevance of land and labor has diminished, not completely but measurably, from their prominence during agrarian and industrial economic times. In a sense, owning land and employing lots of people no longer highly correlate to a valuable and successful enterprise. Although in certain industries these two factors will remain prominent (think mining and energy production, for example). By and large, land and labor have yielded to two more important factors – time and information.”

I was very pleased when Silicon Angle asked to speak with me about my background and the thoughts that led to this newly posited IT-driven economic theory as well as the contributions Jaspersoft is making to this new economic landscape. I discussed how Jaspersoft’s mission is precisely to help its customers compete on the basis of time and information.

“From its very start, Jaspersoft was determined to build and advance the industry’s most modern, flexible, and scalable Business Intelligence (BI) software. To do this, we consciously chose the open source model of development and distribution, believing that the power and principles of community involvement and broad usage would prove continually more valuable (and it has). We knew time would be important to our business model to rapidly compete in a crowded software category.”

Jaspersoft focuses on delivering its modern BI software to those who are best suited to create value from it. We call these individuals “BI Builders” because they possess a powerful confluence of knowledge about data, analytics, and business (process, function, industry, etc.) that truly yields new value from insight. The result is thousands of commercially available software applications that include Jaspersoft technology. These software applications power the world and deliver faster, more effective insight into data. Jaspersoft’s open source model affords these applications very high quality reporting and analytic capabilities at a very low cost, so our customers create new economic value, arguably, where it could not have been created in the past.

In many ways, the BI Builder is the real hero in the equation that determines how companies can compete more effectively based on time and information. Jaspersoft simply becomes their partner and enabler.

Here’s a chance to continue this dialog at the intersection of economic theory and information technology. I offer an open invitation for comments. Your thoughts are appreciated.

Brian Gentile
Chief Executive Officer
Jaspersoft