Jaspersoft 4.7 Shows Big Data Opportunity and Mobile BI Challenges


Jaspersoft Business Intelligence Suite competes in the open source and broader BI market. Its customer base is mostly in the small and midsized business and OEMs and SaaS providers who can embed Jaspersoft code directly into their offerings. Earlier this summer, the company introduced Jaspersoft 4.7, which features advancements in interactive reporting, big data access and mobile business intelligence for Android. The 4.7 release brings interactive features such as segmenting and filtering, though these are not as user-friendly as those found in some of the other tools in the market. As many of our benchmark research reports show, usability is becoming more important in the tools environment as business users become more proactive in the selection and use of these tools. Interactive reporting is quickly becoming table stakes, but we’re still not seeing the advancements that will lead to mass business user adoption, as my colleague Mark Smith  recently noted.

To handle more big data, Jaspersoft adopted MongoDB, a real-time-oriented NoSQL database. Users can now display log data directly on a dashboard, and developers can build reports.  This addition augments Jaspersoft’s native access to other big-data NoSQL approaches such as Hadoop and Cassandra. Jaspersoft also partners with big-data companies including IBM Netezza, Datastax, HP Vertica, 10Gen and Google BigQuery. The importance of connecting with a variety of data sources is highlighted by our recent big data benchmark research, where companies say customer data (68%) and transactional data (60%) top the list of critical data sources.

Release 4.7 lets users build applications and view reports natively on Android mobile devices, adding to the native support already available for Apple iOS. An SDK for Android allows developers to embed directly at the device level. I went ahead and tried out the mobile version on my Apple iPhone 4S and found that it lacked the auto-sizing and interactivity found in other approaches today.

I also decided to test out the trial version of the cloud version of the software and I was advised that it would take an hour to provision the resources. While I waited I went ahead and downloaded the 64-bit version of Jaspersoft 4.7 for Windows. The install process was relatively seamless with the only challenge being that there was no clear user name and password available. Fortunately, there was a live operator for me to call and resolve the issue immediately. The trial included sample data to play with which was nice, but the tool really lacked many of the features such as search and collaboration that we are seeing from other available tools in the market. I can see how Jaspersoft may be a nice addition to a SaaS offering or for OEM, but as far as a standalone BI tool, Jaspersoft has an uphill climb. When the cloud version arrived as promised, the demo was essentially the same with minor hang times as opposed to the download version.

While Jaspersoft 4.7 helps move the needle in the right direction, I’d like to see further development in ease of use (especially in the mobile area) as well as in areas such search and collaboration. As our NextGen BI benchmark research will reveal, expectations for mobile and collaborative BI systems are high, but actual time-to-value is still wanting. Overall, however, the ability to embed Jaspersoft in OEM applications along with the rise in cloud computing and SaaS should give the company ample space for growth, and the advancements in the latest release in reporting, big data and mobile areas should help it take advantage of the hottest trends. Companies looking for a low cost BI option or looking to embed basic BI functionality into their own applications should consider Jaspersoft.

Regards,

Ventana Research

Gaining Time-to-Value From Big Data


I had a refreshing call this morning with a vendor that did not revolve around integration of systems, types of data, and the intricacies of NoSQL approaches. Instead, the discussion was about how its business users analyze an important and complex problem and how the company’s software enables that analysis. The topic of big data never came up, and it was not needed, because the conversation was business-driven and issue-specific.

By contrast, we get a lot of briefings that start with big data’s impact on business, but devolve into details about how data is accessed and the technology architecture. Data access and integration are important, but when we talk about big data analytics, focusing on the business issues is even more critical. Our benchmark research into big data shows that companies employ storage (95%) and reporting (94%) of big data, but very few use it for data mining (55%) and what-if scenario modeling (49%). That must change. Descriptive analysis on big data is quickly turning into table stakes; the real competitive value of big data analytics is in the latter two categories.

Not every big data vendor drowns its message in technospeak. IBM, for instance, stokes the imagination with analytical systems such as Watson and does a good job of bringing its business-focused story to a diverse audience through its Global Business Services arm. Some newer players paint compelling pictures as well. Companies such as PivotLink, PlanView and SuccessFactors (now part of SAP) deliver analytics stories from different organizational perspectives. Part of their advantage is that they start from a cloud and application perspective, but they also tell the analytics story in context of business, not in context of technology.

Providing that business perspective is a more difficult task for BI companies that have been pitching their software to IT departments for years, but even some of these have managed to buck this trend.  Alteryx, for instance, differentiates itself by putting forward compelling industry-specific use cases, and espousing the concept of the data artisan. This right-brain/left-brain approach appeals to both the technical and business sides of the house. Datameer also does a good job of producing solid business use cases. Its recent advancements in visualization help the company paint the analytical picture from a business perspective. Unfortunately, other examples seem few and far between. Most companies are still caught pitching technology-centric solutions, despite the fact that, in the new world of analytics, it’s about business solutions, not features on a specification sheet.

This focus on business issues over technology is important because the business side of the house today controls more and more of the technology spending. While business managers understand business and often have a firm grasp of analytics, they don’t always understand or care about the intricacies of different processing techniques and data models. In our upcoming benchmark research on next-generation BI systems, the data from which I’m currently analyzing, we see this power shift clearly. While IT still has veto power, decisions are being driven by business users and being ratified at the top of the organization.

The Ventana Research Maturity Model from our business analytics benchmark research shows that the analytics category is still immature, with only 15 percent of companies reaching the innovative level. So how do we begin to change this dialog from a technology-driven discussion to a business-driven discussion? From the client perspective, it starts with a blue sky approach, since the technological limitations that drove the old world of analytics no longer exist. This blank canvas may be framed by metrics such as revenue, profit and share of wallet, but the frame is now extending itself into less tangible and forward-looking areas such as customer churn and brand equity. If these output metrics are the frame, it’s the people, process, information and tools that are our brushes with which we paint. The focal point of the piece is always the customer.

If a business has a hard time thinking in terms of a blank canvass, it can examine a number of existing cases that show the value of utilizing big data analytics to help illuminate customer behavior, web usage, security, location, fraud, regulation and compliance. Some of the bigger ones are briefly discussed in my recent blog entry on predictive analytics.

The big data industry, if we can call it that, is quickly moving from a focus on the technology stack to a focus on tangible business outcomes and time-to-value (TTV). The innovations of the last few years have enabled companies to take a blue sky perspective and do things that they have never thought possible. The key is to start with the business problem you are looking to solve; the technology will work itself out from there.

Regards,

Ventana Research