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Predictive analytics in an inherently difficult task and often takes specialized skills. While not easy, the business results of predictive analytics can be significant. 68% of companies say they use predictive analytics to create competitive advantage while 55% say that they increase revenue. KXEN is a software company that specializes in making predictive analytics easier to use by automating predictive analytic processes and some data preparation tasks. Like other predictive analytics companies, KXEN targets uses cases in risk and fraud prevention, operations and customer service, but given its end-user focus, it is natural that the company seems to be finding a niche on the customer-facing side of business in areas such as sales operations and marketing.

vr_predanalytics_benifits_of_predictive_analyticsThe key to KXEN’s strategy is what might be called a commodity or good enough approach to modeling. That is, end users do not need to know advanced statistics to use InfiniteInsight, the company’s flagship platform. The user feeds data into the KXEN engine, and the system dynamically creates and validate models. One selling point for the company is that the engine is able to ingest hundreds and even thousands of variables and automatically sort through the data set to find the right predictor variables. In a traditional analytic process, by contrast, a trained statistician would define the variables to be used in the model and often go through a data reduction process prior to building a predictive model. InfiniteInsight, avoids this step entirely. To test the model, it uses only half of the data to build the model, and the other half of the data to validate it. It avoids over-fitting by cross-validating the original training set with the validation set.

The variables with the highest predictive power according to the KXEN algorithms are the ones that are subsequently used in the actual production system. For instance, consider a next-best-offer prediction that includes opening a new checking account, offering a new credit card or offering a home equity line of credit. Each product offer would be modeled separately and may have different drivers. When a customer reaches the company through the call center or a website, each product will be scored according to the customer-related variables that are most predictive of that offer. If a person has just been married, that may be a better predictor for opening a new checking account than if the person just bought a new house, which may suggest a home equity line of credit. InfiniteInsight integrates with third-party business rules engines that are a necessity for almost any type of real-time operational analytic system.

As demand for analytics becomes more important to organizations, application vendors can choose to build analytics into their applications or strike partnerships with companies such as KXEN to provide the necessary intelligence. Such partnerships represent a big opportunity for BI and analytics vendors since emerging cloud-based companies often focus on applications themselves and not analytics. For example, KXEN partners with to provide predictive applications on the AppExchange. At the same time, KXEN has its own Cloud Prediction platform that offers applications for predictive offers (also called next best offer), lead scoring, retention and case routing. This hedge is the smart play. Our benchmark research into next-generation business intelligence shows that companies are split on how they will deploy their next generation of intelligence systems: 38% said it will be part of a specific business intelligence system, 36% said it will be driven through Microsoft Office, and 34% said it will be embedded into the application. With the rise in mobile intelligence and the importance of operational intelligence in today’s organizations, it will be interesting to see how these numbers change in our next generation business analytics research, which we will conduct later in 2013.

The latest release of KXEN’s flagship InfiniteInsight, version 6, which became generally available in March, adds capabilities for social intelligence as well as campaign intelligence for marketers. For social intelligence, version 6 provides capabilities to explore social graphs to identify connections among people and find top influencers in a category. It can also put social attributes into a predictive model for purposes such as predicting social media paths to increase the effectiveness of a viral campaign. Another product called Genius enables point-and-click campaign modeling so marketers can run analytics on just about any size of campaign. This is becoming important in the world of digital marketing since smaller, more targeted campaigns are needed to lessen to the noise in the consumer’s digital environment. It used to be that only large direct mail campaigns would get a unique model, and that model had to be built and interpreted by trained statisticians. Once the model was optimized, it would then be translated into database language, the database would be scored and the target prospects selected. This took time and high-priced talent. Today, many models are needed in a much shorter timeframe. Commodity modeling approaches such as Genius help marketers quickly optimize their campaigns without having to involve a statistician. Such time-to-value is a key buying criterion is today’s fast-paced markets and for KXEN’s client base of more than 500 companies of various sizes.

This week the company announced location intelligence as a native feature of its InfiniteInsight platform. Location awareness enables the system to understand the location of a particular person and use this information to help predict the most relevant offers to that person at that time and place. Using the location technology, the company also offers co-location and geographic path analysis techniques by which the location intelligence can look at similar events occurring within a certain area or look at a time sequence of events occurring in multiple places. Such techniques can help, for example, to root out crime or provide real-time route optimization during heavy traffic times. Our benchmark research on location analytics, that we are completing, suggests that location information has been an underappreciated source of intelligence, and while it is beginning to gain some early traction, people’s lack of location analytic skills is still an obstacle.

Predictive analytic models are only as good as the quality of the input and therefore data pre-processing is a key consideration for predictive analytics. 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. KXEN has basic tools for data preparation such as checking for missing variables, classifying variable type, encoding of continuous variables and outlier detection and handling. Its social graph capabilities can also link people with many identities, though their ability to clean the data set and merge these identities automatically is still unclear. Data preparation is an area where other tools still may be needed since they often include more advanced data preparation capabilities.

vr_bti_br_technology_innovation_prioritiesAnalytics was ranked as the top technology innovation priority by 39% of participants in that research, more than twice as many cited the second and third highest priorities of collaboration and mobile technology. In addition the most critical capability to satisfy business analytics is applying predictive analytics in almost half (49%) of organizations. Analytics is a broad category, and predictive analytics is perhaps the most complicated in terms of systems and organizational integration.  KXEN has developed an approach that automates much of this complex world of predictive analytics. Its advantages include providing organizations with a common language framework for understanding predictive analytics.

The primary arguments against KXEN’s approach are that the quality of its models may not be as strong as those done by a trained statistician and that the breadth of use is not as wide as some of its competitors attain. While these arguments have validity in certain circumstances, we note that lack of skills is the primary barrier to dissemination of predictive analytics. In many situations, commodity models that address this skills gap at the front line of the organization are better than current approach of randomness and gut-feel.


Tony Cosentino

VP & Research Director

Users of big data analytics are finally going public. At the Hadoop Summit last June, many vendors were still speaking of a large retailer or a big bank as users but could not publically disclose their partnerships. Companies experimenting with big data analytics felt that their proof of concept was so innovative that once it moved into production, it would yield a competitive advantage to the early mover. Now many companies are speaking openly about what they have been up to in their business laboratories. I look forward to attending the 2013 Hadoop Summit in San Jose to see how much things have changed in just a single year for Hadoop centered big data analytics.

Our benchmark research into operational intelligence, which I argue is another name for real-time big data analytics, shows diversity in big data analytics use cases by industry. The goals of operational intelligence are an interesting mix as the research shows relative parity among managing performance (59%), detecting fraud and security (59%), complying with regulations (58%) and managing risk (58%), but when we drill down into different industries there are some interesting nuances. For instance, healthcare and banking are driven much more by risk and regulatory compliance, services such as retail are driven more by performance, and manufacturing is driven more by cost reduction. All of these make sense given the nature of the businesses. Let’s look at them in more detail.

vr_oi_goals_of_using_operational_intelligenceThe retail industry, driven by market forces and facing discontinuous change, is adopting big data analytics out of competitive necessity. The discontinuity comes in the form of online shopping and the need for traditional retailers to supplement their brick-and-mortar locations. JCPenney and Macy’s provide a sharp contrast in how two retailers approached this challenge. A few years ago, the two companies eyed a similar competitive space, but since that time, Macy’s has implemented systems based on big data analytics and is now sourcing locally for online transactions and can optimize pricing of its more than 70 million SKUs in just one hour using SAS High Performance Analytics. The Macy’s approach has, in Sun-Tzu like fashion, made the “showroom floor” disadvantage into a customer experience advantage. JCPenney, on the other hand, used gut-feel management decisions based on classic brand merchandising strategies and ended up alienating its customers and generating law suits and a well-publicized apology to its customers. Other companies including Sears are doing similarly innovative work with suppliers such as Teradata and innovative startups like Datameer in data hub architectures build around Hadoop.

Healthcare is another interesting market for big data, but the dynamics that drive it are less about market forces and more about government intervention and compliance issues. Laws around HIPPA, the recent Healthcare Affordability Act, OC-10 and the HITECH Act of 2009 all have implications for how these organizations implement technology and analytics. Our recent benchmark research on governance, risk and compliance indicates that many companies have significant concerns about compliance issues: 53 percent of participants said they are concerned about them, and 42 percent said they are very concerned. Electronic health records (EHRs) are moving them to more patient-centric systems, and one goal of the Affordable Care Act is to use technology to produce better outcomes through what it calls meaningful use standards.  Facing this title wave of change, companies including IBM analyze historical patterns and link it with real-time monitoring, helping hospitals save the lives of at-risk babies. This use case was made into a now-famous commercial by advertising firm Ogilvy about the so-called data babies. IBM has also shown how cognitive question-and-answer systems such as Watson assist doctors in diagnosis and treatment of patients.

Data blending, the ability to mash together different data sources without having to manipulate the underlying data models, is another analytical technique gaining significant traction. Kaiser Permanente is able to use tools from Alteryx, which I have assessed, to consolidate diverse data sources, including unstructured data, to streamline operations to improve customer service. The two organizations made a joint presentation similar to the one here at Alteryx’s user conference in March.

vr_grc_worried_about_grcFinancial services, which my colleague Robert Kugel covers, is being driven by a combination of regulatory forces and competitive market forces on the sales end. Regulations produce a lag in the adoption of certain big data technologies, such as cloud computing, but areas such as fraud and risk management are being revolutionized by the ability, provided through in-memory systems, to look at every transaction rather than only a sampling of transactions through traditional audit processes. Furthermore, the ability to pair advanced analytical algorithms with in-memory real-time rules engines helps detect fraud as it occurs, and thus criminal activity may be stopped at the point of transaction. On a broader scale, new risk management frameworks are becoming the strategic and operational backbone for decision-making in financial services.

On the retail banking side, copious amounts of historical customer data from multiple banking channels combined with government data and social media data are providing banks the opportunity to do microsegmentation and create unprecedented customer intimacy. Big data approaches to micro-targetting and pricing algorithms, which Rob recently discussed in his blog on Nomis, enable banks and retailers alike to target individuals and customize pricing based on an individual’s propensity to act. While partnerships in the financial services arena are still held close to the vest, the universal financial services providers – Bank of America, Citigroup, JPMorgan Chase and Wells Fargo – are making considerable investments into all of the above-mentioned areas of big data analytics.

Industries other than retail, healthcare and banking are also seeing tangible value in big data analytics. Governments are using it to provide proactive monitoring and responses to catastrophic events. Product and design companies are leveraging big data analytics for everything from advertising attribution to crowdsourcing of new product innovation. Manufacturers are preventing downtime by studying interactions within systems and predicting machine failures before they occur. Airlines are recalibrating their flight routing systems in real time to avoid bad weather. From hospitality to telecommunications to entertainment and gaming, companies are publicizing their big data-related success stories.

Our research shows that until now, big data analytics has primarily been the domain of larger, digitally advanced enterprises. However, as use cases make their way through business and their tangible value is accepted, I anticipate that the activity around big data analytics will increase with companies that reside in the small and midsize business market. At this point, just about any company that is not considering how big data analytics may impact its business faces an unknown and uneasy future. What a difference a year makes, indeed.


Tony Cosentino

VP and Research Director

Information Builders  (IBI) was as the highest ranked vendor in Ventana Research’s Business Intelligence Value Index for 2012. The combination of data integration, business analytics, visual and dataBI_VentanaResearch2012_HotVendor discovery 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. IBI’s 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.


Tony Cosentino

VP and Research Director

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