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Information Builders  (IBI) was highest ranked vendor in Ventana Research’s Business Intelligence Value Index for 2012. The combination of data integration, business analytics, visual and data BI_VentanaResearch2012_HotVendordiscovery and performance management software in a single framework allows the company to address a range of both IT and business user needs and gives it a measure of advantage in an intensely competitive market. At the same time, emerging trends are disrupting the BI category, which seemed mature not long ago. The 2013 IBI user conference in Orlando showed how the company is addressing these industry trends. (For analysis of last year’s event, see my colleague Mark Smith’s comments).

At the core of the IBI strategy are its WebFocus 8.0 platform and iWay, its information management suite of software. Our benchmark research into Business Technology Innovation shows that data preparation and quality are critical challenges and time consuming activities impacting analysts in 42 percent of organizations, so information management must be part of any general discussion of business intelligence. The latest release, iWay 7, was announced at the conference. It can integrate more than 300 data sources using prebuilt adapters and handles data preparation and quality and multidomain master data management. Management spun off iWay into a separate operating company but brought it back into the core business recently as executives recognized the trend toward big data and what we call information optimization. The combination of data integration with business intelligence is a critical factor for business intelligence companies in large part because big data integration is essential to big data analytics. The ability to denormalize data and combine diverse data into a wide single view of an analytical data set is an important aspect of big data analytics. Information Builders uses the iWay and a columnar database called Hyperstage running on commodity servers to handle these big data challenges.

The picture of how WebFocus 8 addresses emerging BI trends is becoming clearer. The first of these trends is the necessity for self-vr_ngbi_br_importance_of_bi_technology_considerationsservice and ease of use in business intelligence tools. Our next-generation business intelligence benchmark research shows that usability is the most critical buying criterion for nearly two-thirds (63%) of organizations. IBI has prepared its applications for a broad user base through capabilities that enable the Web-based WebFocus to deliver features normally associated with desktop software. Additional functionality provided through InfoAssist, a component of WebFocus 8, helps power users explore data, define metrics and publish information without coding. Additionally, the suite now includes Visual Discovery, which has data mashup and discovery capabilities that enable analysts to look at data without a predefined schema and find relationships that may not have been apparent previously. Location analytics technology from ESRI, a long-time leader in location intelligence, can be incorporated into the analysis as well. Location analytics has not been given a lot of attention, but it is gaining more recognition, according to our recent location analytics benchmark research. Finally, Magnify offers a search capability for both structured and unstructured data, which helps users find business content across the enterprise. While Magnify presents a valuable search tool for analysts, the product appears to be suffering from lack of awareness. In a session on self-service BI, few attendees had even heard of it.

Analytics applied to social media is another hot topic in business, and IBI has made significant advancements with its Social Media Integration application, also part of WebFocus 8. It enables users to examine posts, blogs and other social data to detect patterns in customer opinions. Sentiment algorithms that interpret and quantify the inherent complexities of language are provided as a third-party Web service or a REST adapter. Users can search via the Magnify tool and receive a robust contextual inquiry experience with tag clouds, quantitative information around mentions, and sentiment on a scale from very negative to very positive. Users can assign thresholds based on numeric value and assign appropriate stakeholders to follow up. Many marketing departments are using ad-hoc tools to drive these types of initiatives, but ultimately it makes more sense to place these queries within the context of their business intelligence initiatives; social information alone has limited value, but when married with internal metrics such as customer lifetime value, it has much more impact.

On another increasingly important front, mobile business intelligence ranks as a business priority among the six areas of technology innovation that Ventana Research studies. IBI takes a hybrid HTML5vr_ngbi_br_what_capabilities_matter_for_mobile_bi approach to mobile intelligence and analytics. That is, a user downloads a native shell from an online store associated with a particular device, and then the content is rendered through the browser via HTML5. Seeing the mobile trend early, IBI completely rewrote its charting engine to support HTML5 and Mobile Favs on the native side. This method exploits native gestures, while at the same time designers benefit from a develop once, deploy anywhere strategy. While our research shows that mobile users still prefer native applications over HTML5, the pendulum may be swinging. In December 2012 W3C, the body that oversees the HTML5 standard, agreed on candidate recommendations, which means that important companies such as Apple, Google and Microsoft have accepted standards to be implemented by the larger development community. This will help HTML5 vendors including IBI. IBI’s Mobile strategy provides robust dashboard and portal access which is a high priority for 36% of users, however IBI should work to make improvements that leverage prescriptive analytics and operational capabilities to drive proactive alerts and notifications which are the top capabilities mentioned by 42% of mobile BI users.

IBI’s cloud initiative is in the form of platform as a service (PaaS). As opposed to infrastructure as a service or software as a service, PaaS provides both infrastructure and a development environment for BI applications. IBI’s product encompasses service level agreements for testing, validation and production environments with performance tuning, database provisioning and network management. The company has 10 international data centers, which helps to overcome regulatory challenges associated with international data movement. IBI does not have a “pay as you go” usage model but treats it more as a professional service based on assessment. This matches the company’s intended brand image as a service-oriented provider. In the bigger picture of cloud computing, BI is a laggard with only a few percent of participants in our research actually having adopted cloud-based BI. Security and data movement are the biggest perceived obstacles among those organizations.

In the area of predictive analytics, IBI has embedded RStat, which uses the open source R statistical language and can be accessed vr_predanalytics_predictive_analytics_obstacleswithin Developer Studio or as part of its WebFocus BI product. While the customers I spoke with are still building their models outside the IBI system, they suggested that the models will be translated back into RStat and scored within the IBI system. Predictive analytics is a challenge for many business intelligence vendors, which until now have dealt in historical data and simple descriptive statistics. Traditional relational databases are able to provide basic descriptive functions such as min, max, sum and mean, but more advanced functions have been beyond their scope. Our benchmark research on predictive analytics shows that the difficulty of integrating predictive analytics into a current information architecture is the biggest obstacle to predictive analytics for more than half (55%) of organizations.

In a broader analytics discussion with its product leaders Kevin Quinn and Rado Katorov, an interesting analytic concept that bears on data discovery was revealed. Simpson’s Paradox is the idea that a trend that appears in a single group disappears, and often reverses itself, when combined with other data. For instance, in 1973, the University of California Berkeley was sued for discrimination against women based on the fact that 44 percent of male applicants were admitted but only 35 percent of women were admitted. While the difference is indeed significant, when the data is looked at on a departmental basis, an interesting causal variable emerges. That is, men were applying to the easier programs and women were applying to the more difficult programs. Thus it was concluded that the disparity was not due to discrimination but rather to men who applied to the university that year may simply have been a bit lazier than the women applying. The point for analytics is that many discovery tools in the market today often rely on people to make discoveries based on single groupings of variables, and such discoveries may be misleading or worse. IBI’s approach to this issue is to use data reduction techniques such as cluster analysis that allow the data to group itself in an a-priori manner, thus making it easier for the analyst to recognize important patterns among groups of variables rather than just single variables. In the Berkeley admission example, for instance, IBI’s system presumably would have linked the difficulty of the program with gender, and that insight could perhaps have prevented the lawsuit from even being filed.

In sum, IBI has a strong base in large and midsize companies due to itsVR_leadershipwinner posture as more than a BI company. Our recent recognition of Scott Franzel at OFS Brands with the 2013 Overall IT Leadership Award for their use of Information Builders is another example of its business intelligence software helping organizations and individuals be successful. Its success in extending BI to a broader base of stakeholders in both B2B and B2C markets allows the company to keep up with current trends and is at the center of the company’s big data and analytics initiatives. Companies that have already deployed WebFocus should look at the extended capabilities of version 8 and in particular the opportunity to brand information as a service throughout the organization. On a broader basis, any business group or IT department that is trying to take a customer-driven approach to business intelligence should consider IBI.

Regards,

Tony Cosentino

VP and Research Director


IBM hosted the Big Data and Analytics Analyst Insights conference in Toronto recently to emphasize the strategic importance of this topic to the company and to highlight recent and forthcoming advancements in its big data and analytics software. Our firm followed the presentations with interest. My colleagues Mark Smith and Tony Cosentino have commented on IBM’s execution of its big data strategy and its approach to analytics. As well, Ventana Research has conducted benchmark research on challenges in big data.

The perennial challenge for the IT industry observer is to be skeptical enough to avoid being taken in by overblown claims (often, the next big thing isn’t) without missing turning points in the evolution of technology. “Big data” has always been with us – it’s the amount that constitutes “big” that has changed. A megabyte was considered big data in the 1970s but is a trivial amount today. There’s no question that the hype around big data is excessive, but beneath the noise is considerable substance. Big data is of particular interest today because the scope and depth of the data that can be analyzed rapidly and turned into useful information are so large as to enable transformations in business management. The effect will be to raise computing – especially business computing – to a higher level.

The IBM event demonstrated that the technology for handling big datavr_bigdata_obstacles_to_big_data_analytics analytics to support more effective business computing is falling into place. It’s not all there yet but should improve strongly over the next several years. Yet while technological barriers are falling, there are other issues organizations will need to resolve. For example, the conversation in the conference sessions frequently turned to the people element of successfully deploying big data and analytics. These discussions confirmed an important finding in our big data research, shown in the chart, that more than three-fourths of organizations find staffing and training people for big data roles to be a challenge. It’s useful to remind ourselves that there will be the usual lag between what technology makes possible and the diffusion of new information-driven management techniques.

The conference focused mostly on the technology supporting big data and analytics but included examples of conceivable use cases for that technology. For example, there was a session on better management of customer profitability in the banking industry. Attempts to use software to optimize customer profitability go back to the 1980s, but results have been mixed at best, and a great many users continue to perform analysis in spreadsheets using data stored in business silos. IBM speakers described a generic offering incorporating Cognos TM1 to automate the fusion of multiple bank data sources that are incorporated in a range of profitability-related analytic applications. The aim is to enable more precise pricing and price-related decisions related to rates and fees, among other factors. This application enables consistent costing methodologies, including activity-based ones for indirect and shared expenses, to promote a more accurate assessment of the economic profitability of offers to customers. A good deal of the value in this offering is that it puts the necessary data in one place, giving executives and managers a consistent and more complete data set than they typically have. As well, the product’s use of in-memory analytic processing enables much faster calculations. Faster processing of a more complete data set enables more iterative, what-if analyses that can be used to explore the impact of different strategies in setting objectives for a new product or service or examining alternatives to exploit market opportunities or address threats.

As the Internet did, big data will change business models and drive the creation of new products and services. The dramatic drop in the cost of instrumenting machinery of all types and connecting them to a data network (part of the concept of the Internet of Things) is already changing how companies manage their productive assets, for example, by optimizing maintenance using sensors and telematics to enhance uptime while minimizing repair expense. Decades ago, companies monitored production parameters to ensure quality. More recent technologies can extend the speed and scope of what’s monitored and provide greater visibility into production conditions and trends. Representatives from BMW were on hand at the conference to talk about their experience in improving operations with predictive maintenance. Centrally monitoring the in-service performance of equipment and capital assets is old hat for airlines and jet engine manufacturers. For them, the economic benefits of optimizing maintenance to maximize the availability and uptime were significant enough to warrant the large investment they started making decades ago. The same basic techniques can be used to for early detection of warranty issues, such as identifying specific vehicles subject to “lemon law” provisions. From IBM’s perspective, these new technologies will enhance the value of Maximo, its asset management software, by extending its functionality to incorporate monitoring and analytics that help users explore options and optimize responses to specific conditions.

IBM Watson is the company’s poster child for the transformative capabilitiesVR_2012_TechAward_Winner_Logo of big data and analytics on how organizations operate. The company’s objective is to enable customers to achieve better performance and outcomes by having systems that learn through interactions, providing evidence-based responses to queries. My colleagues Mark Smith and Richard Snow have written about Watson in the contexts of cognitive computing and its application to customer service. And we awarded IBM Watson Engagement Advisor our 2012 Technology Innovation Award. Conference presenters gave an extensive review of progress to date with Watson, featuring innovative ways to use it for diagnosis and treatment in medicine as well as to provide customer support.

Although this was not directly related to big data, IBM also used the conference to announce the availability of Cognos Disclosure Management (CDM) 10.2.1, which will be available both on-premises and in a cloud-based SaaS version. CDM facilitates the creation, editing and publishing of highly structured enterprise documents that combine text and numbers and are created repeatedly and collaboratively, including ones that incorporate eXtensible Business Reporting Language (XBRL) tags. The new version offers improvements in scalability and tagging over the earlier FSR offering. The SaaS version, available on a per-seat subscription basis, will make this technology feasible for midsize companies, enabling them to save the time of highly skilled individuals as well as enhance the accuracy and consistency of, for example, regulatory filings and board books. A SaaS option also will help IBM address the requirements of larger companies that prefer a cloud option.

Most of the use cases presented at the conference were extensions and enhancements of well-worn uses for information technology. However, when it comes to business, the bottom line is what matters, not novelty. Adoption of technology occurs fastest when “new” elements of its use are kept to a minimum. The rapid embrace of the Internet in North America and other developed regions of the world was a function of the substantial investment that had been made over the previous decades in personal computers and local- and wide-area communications networks as well as the training and familiarity of people with these technologies. Big data and the analytics that enable us to apply it have a similar base of preparation. Over the next five years we can expect advances in practical use that benefit businesses and their customers.

Regards,

Robert Kugel – SVP Research

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