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SPS-201 - IBM SPSS MBPDA - IBM SPSS Modeler Business - BrainDump Information

Vendor Name : IBM
Exam Code : SPS-201
Exam Name : IBM SPSS MBPDA - IBM SPSS Modeler Business
Questions and Answers : 25 Q & A
Updated On : January 22, 2019
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SPS-201 exam Dumps Source : IBM SPSS MBPDA - IBM SPSS Modeler Business

Test Code : SPS-201
Test Name : IBM SPSS MBPDA - IBM SPSS Modeler Business
Vendor Name : IBM
Q&A : 25 Real Questions

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IBM IBM SPSS MBPDA -

Monster Merger: IBM Buys SPSS For Approx. $1.2 Billion In cash Deal | killexams.com Real Questions and Pass4sure dumps

IBM is buying analytics software and options provider SPSS in an all money transaction at a value of $50/share – a 42 p.c premium to Monday’s closing expense of $35.09 on Nasdaq – leading to a complete money consideration within the merger of approximately $1.2 billion. The acquisition is discipline to SPSS shareholder approval, regulatory clearances and other closing circumstances, and is anticipated to close later in the second half of 2009.

massive Blue observed the acquisition of the publicly-held Chicago business become expected to strengthen its counsel-agenda initiative, which helps corporations take assistance and turn it into a strategic asset. IBM shares fell 67 cent to $116.96 in pre-market buying and selling, while SPSS shares jumped 41 % to $forty nine.59.

A message from SPSS Chairman, CEO & President Jack Noonan:

We and IBM view this as a extremely-complementary flow from both a technology and a market position standpoint.

We’re enthusiastic about this settlement and the effective possibilities that SPSS and IBM can achieve together in continuing to lead an industry that we helped shape. The depth and breadth of IBM’s materials, its client and market attain can best enhance our capability to address the turning out to be marketplace for Predictive Analytics.

In becoming a member of with IBM, we will improve Predictive Analytics as a aggressive talents for organizations and groups international. We see this as a transformative experience so as to accelerate the adoption of Predictive Analytics.

Between now and the acquisition close, we can proceed to guide our purchasers and partners in typical fashion. present contracts will remain in effect, account relationships and aid infrastructure will continue to be unchanged.

Predictive analytics software captures and analyzes facts about americans’s attributes, attitudes and behaviors to benefit a full understanding of predicted future behaviors, so agencies could make smarter selections for enhanced company results.

IBM says it'll proceed to support and enhance SPSS technologies while allowing purchasers to take expertise of its own product portfolio. SPSS will become a part of the information administration division inside the application neighborhood company unit, led by using Ambuj Goyal, time-honored supervisor, IBM tips management.

replace – In other news: IBM also bought Ounce Labs, a issuer of enterprise supply code safety testing and intends to combine it into its Rational utility company. financial phrases were no longer disclosed.


IBM is dashing up SPSS with Apache Spark | killexams.com Real Questions and Pass4sure dumps

IBM these days announced that it's bettering a couple of of its latest utility items with the Apache Spark open-source records processing engine, together with the SPSS predictive analytics utility.

IBM bought SPSS for $1.2 billion in 2009. SPSS itself begun at Stanford college in 1968 and is widespread in information courses at universities.

IBM is above all incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman informed VentureBeat in an electronic mail.

Spark is additionally being utilized to IBM’s BigInsights, Streams, and DataWorks application, in accordance with an announcement.

The Sparkification of IBM comes after large Blue announced a huge dedication to the open-source utility in June. at the time, IBM introduced Spark as a carrier on its Bluemix cloud; that carrier is now commonly accessible.

IBM has made greater than 60 contributions to Spark since the June announcement, in line with the observation.


IBM Wins a 2018 crimson Dot Design Award for SPSS statistics | killexams.com Real Questions and Pass4sure dumps

The IBM Hybrid Cloud crew is returned at it with yet yet another win for design. I’m excited to announce that our design team has been awarded the 2018 purple Dot: communication Design Award for IBM SPSS records in the Interface Design class. This award is a continuation of the design achievements we've viewed this previous year, together with the A’Design Awards, IF Design Awards, and others. i'm overjoyed to see the complicated work of our designers and IBM Design proceed to shine and make a change in commercial enterprise software.

First developed in the 1990’s, the pink Dot Award has been the revered foreign seal of amazing design quality. Designers, groups, and organizations from 45 distinct nations took half during this yr’s competition, totaling over eight,600 entries that underwent a 24 member jury.

“All people who progress in the course of the tough adjudication manner to garner a pink Dot have each motive to be pleased with themselves, as the jury supplies our award best to creations of high design satisfactory. This makes me all the more delighted to congratulate the laureates basically on their well-merited success.” — Professor Dr. Peter Zec, founder and CEO of the pink Dot Award

Receiving this award was enormously interesting for our crew and we are honored to be among the winners. here is a huge fulfillment for our designers who labored on this product, and that they faced a fascinating and difficult event in engaged on this product.

what is IBM SPSS?

IBM SPSS data is a magnificent records analysis device that is likely one of the most typical facts applications. on account that its inception in 1968, SPSS statistics has been revamped and redeveloped diverse instances. Now the design group at IBM has taken on the project of creating a very clean consumer event.

during this newest redesign of IBM SPSS records, we applied design thinking concepts by way of working carefully with our users and making sure this modernized edition of SPSS information aligns with their needs. Our top-rated aim changed into to create an impressive device that is not best handy and intuitive to use, however that our clients can savor.

Our team and Design approach

The IBM SPSS design team is part of the IBM Design Studios in Boeblingen, Germany. The team is composed of a various group, with many contributors originating from different countries and cultures. Some contributors of the crew had some history with data while others had been working during this container for the primary time.

Following the principles of IBM Design considering (study > replicate > Make), our team applied a remodel that brings a higher focal point on clients for SPSS records. The design team performed intensive analysis on the user base of SPSS records in an effort to see how the utility can more suitable meet their wants. The existing user base levels from less skilled clients similar to students to more expert users such as statistics scientists or company gurus. A key perception from the group’s analysis changed into that less skilled users have been intimidated both by the math work and the complexity of the utility.

the brand new designs focused on simplifying workflows, decreasing the usual complexity of the UI and interactions, and proposing newbies a straightforward on-boarding to data and to the product. one more critical characteristic in the redecorate become a practicing guide led by way of a personality named Simon, who serves as an in-utility ebook, assisting amateur users be aware distinct services and achieve their goals sooner.

The team confronted some wonderful challenges in redesigning a manufactured from such complexity, and one which has additionally been around for so many years. a big success of the designers become making the product obtainable and engaging to new users devoid of alienating decade-long, experienced clients.

a look Into the Future

The preview version of our new IBM SPSS records experience changed into launched in March 2018, and made attainable to the public as a trial on the IBM believe convention is Las Vegas, and since June 26 , the new UI is frequently available to all SPSS information subscribers. This preview is just the preliminary step, providing probably the most used statistical analyses, and fundamental capabilities for data guidance, for presentation and for reporting consequences. Over right here months the crew could be working so as to add extra aspects and capabilities to be able to meet adventure needs of all of our consumer companies.

no longer simply Updating — Redesigning

i am so thrilled to look an extra Hybrid Cloud design crew receive a world award for their work. IBM SPSS information is yet yet another illustration of how design is making a huge change in the success of our items. As we continue to use design to create extra relatable and productive items, we're capable of provide our users the experiences that they want and want. I’m delighted and proud to monitor the change that our design team is making on earth of enterprise application, and that i can’t wait to see how we continue to have an effect on the lives of our clients.

Award Winners:
  • Design supervisor: Caroline legislations
  • Design Leads: Dirk Willuhn and Eva Cochet-Weinandt
  • Design team: Christian Fritsche, Dimitri Hoffmann, Jaehee (Chloe) Lee, Oleksandr Sabov, Stephan Feger
  • because of these contributing designers: Katrin Ellice Heintze, Leila Johannesen, Marion Bruells, Phil Brucker, Robin Auer, Sammy Schuckert, Stefan Schwarz
  • Design interns: Mengzhu Deng, Nathalie Mader, Ting-Hao (Howard) Huang, Vanessa Ng



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    SPS-201 exam Dumps Source : IBM SPSS MBPDA - IBM SPSS Modeler Business

    Test Code : SPS-201
    Test Name : IBM SPSS MBPDA - IBM SPSS Modeler Business
    Vendor Name : IBM
    Q&A : 25 Real Questions

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    Comparing the leading big data analytics software options | killexams.com real questions and Pass4sure dumps

    There are many vendors selling products classified as big data analytics software. However, it's challenging to...

    differentiate these products based on functionality alone, as many of the tools share similar features and capabilities. Additionally, some of the tools exhibit extremely subtle differences.

    That being said, your key differentiating factors will likely focus on balancing ease of use, algorithmic sophistication and price in relation to your organization's capability and level of maturity in analytics.

    In this article, we examine products from nine big data analytics software vendors: Alteryx Inc., IBM, KNIME AG, Microsoft, Oracle, RapidMiner Inc., SAP, SAS Institute Inc. and Teradata Corp. Some of these vendors provide more than one tool. See the "Leading vendors of big data analytics software" sidebar below for more details about their specific product offerings.

    These vendors represent different facets of the big data analytics market. Let's compare and contrast the ways that these products meet the business needs of user organizations.

    Analyst expertise and skills 

    Some data analytics tools are targeted to novice users, some are targeted to expert data analysts and some are engineered to appeal to both types of users.

    Products such as IBM SPSS Modeler, RapidMiner's tools, Oracle Advanced Analytics and the Automated Analytics version of SAP BusinessObjects Predictive Analytics are generally designed to enable users with a limited background in statistics or data analysis to analyze data, develop analytical models and design analytics workflows with little or no coding.

    While each vendor wraps its core analytics components with an intuitive user interface to guide the analyst's progress in data preparation, analysis, and then model design and validation, the approach taken may differ, especially when comparing a stand-alone product, such as RapidMiner, with one that's a component of a larger suite, such as the Oracle product.

    Tools such as IBM SPSS Statistics, KNIME Analytics Platform, the Expert Analytics module of SAP BusinessObjects Predictive Analytics, Microsoft R and the Teradata Aster Analytics platform provide the more sophisticated functionality that expert users expect. Oracle R Advanced Analytics for Hadoop (ORAAH), one of the components in the Oracle Big Data Software Connectors suite, provides an R interface for manipulating Hadoop Distributed File System data and writing mapper and reducer functions in R. This flexibility may be appealing to more advanced data scientists.

    Alteryx and SAS Enterprise Miner offer functionality adapted to the user's level of expertise, and essentially fall into both categories. Alteryx has added improvements to data profiling to help data scientists better understand their data sources. Overall, SAS Enterprise Miner and IBM's SPSS tools stand out when it comes to supporting more advanced analytical techniques and model scoring, as well as a broader array of analysis functions, including neural networks, association analysis and visualization capabilities.

    Analytical diversity

    Depending on the use case and application, your organization's users will be required to support different types of analytics capabilities that will use specific types of modeling, such as regression, clustering, segmentation, behavior modeling and decision trees.

    While this has resulted in broad support for the various forms of analytical modeling at a high level, some vendors have invested decades of work into tweaking different versions of their algorithms and adding more sophisticated functionality. It's important to understand which models are most relevant to your business problems and to evaluate the products in terms of how they best serve your users' business needs.

    It's important to understand which models are most relevant to your business problems and to evaluate the products in terms of how they best serve your users' business needs.

    The more mature and higher-end -- and, accordingly, higher-priced -- tools will exhibit the greatest analytical breadth. Oracle Data Miner includes an array of well-known machine learning approaches to support clustering, predictive mining and text mining. Both editions of IBM's SPSS product provide a diverse set of analytical techniques and models. And SAS Enterprise Miner supports many algorithms and techniques, including decision trees, time series, neural networks, linear and logistic regression, sequence and web path analysis, market basket analysis, and link analysis.

    The newer generation -- and, in some cases, lower-priced -- products support different models, but perhaps with a narrower range of algorithmic sophistication.

    The model inventory in Alteryx Analytics Gallery includes such capabilities as regression analysis, decision trees, association rule analysis, classification and time series analysis. KNIME includes methods for text mining, image mining and time series analysis, and also integrates machine learning algorithms from other open source projects, such as Weka and JFreeChart.

    Another aspect of analytical diversity is integration with programming languages and statistical tools, such as R, for incorporating existing libraries, as well as user-defined functionality. In fact, integration with R could be considered an increasingly critical differentiator.

    Alteryx Designer, Microsoft R, SAS Enterprise Miner, Teradata Aster Analytics, Oracle's ORAAH and KNIME's Analytics Platform all interface and support integration with R. Several of the vendors, including IBM, Oracle, Microsoft, RapidMiner and SAP, provide a growing library of extensions to R and Python, enabling users to take advantage of free libraries.

    Scope of the data to be analyzed

    There are multiple facets of the scope of the data to be analyzed, including the issue of structured vs. unstructured information, as well as access to conventional on-premises databases and data warehouses, cloud-based data sources, and data managed in big data platforms, such as Hadoop.

    However, there are varying degrees of support for data managed within less-conventional data lakes -- either managed within Hadoop or in another NoSQL data management system intended to provide horizontal scaling. The factors for distinguishing among the products must be based on your organization's specific requirements for accessing and processing data volumes and data variety.

    In recognition of the growing diversity of input sources and the variety of underlying systems used to house those data sets, another set of emerging features that is being adopted by these vendors involves data accessibility. IBM, RapidMiner, Alteryx, Oracle and Microsoft have all improved their tools' data import, export and connectivity capabilities. These enhancements should enable users to access a more comprehensive list of data sources while simplifying and speeding up the process of loading data into the products.

    Support for scalability and high performance

    The need for scalable performance is driven by your organization's data volumes and appetite for analysis. Smaller organizations with less data may be able to tolerate products that don't have performance characteristics that scale with the available resources, such as the entry-level versions of the lower-end tools, including RapidMiner, KNIME, Microsoft R Open and Alteryx Designer, which can run on desktop systems and don't require additional server components.

    Larger organizations are more likely to have a greater inventory of data sets to analyze, as well as broader communities of users. This introduces two additional requirements -- high performance and facilitation of collaboration. The adaptability of a product to high-performance architectures is a good indication of scalability, and most of the products can be adapted to the parallelism of Hadoop or employ some other means of achieving faster computation.

    All of the products do have some support for Hadoop, including IBM SPSS Modeler and SPSS Statistics; RapidMiner's commercial component Radoop, which connects the Studio front end and Server analysis engine to data stored in Hadoop; Oracle's Big Data Discovery and ORAAH tools; and KNIME's Big Data Extensions and Cluster Execution add-ins.

    IBM SPSS now also provides enhanced support for a number of multithreaded analytical algorithms that may speed performance. Teradata Aster Analytics addresses high-performance requirements through its Massively Parallel Processing architecture. SAP's Expert Analytics version of SAP BusinessObjects Predictive Analytics can execute in-memory data mining for handling large-volume data analysis efficiently. Microsoft R Server leverages its ScaleR module, a comprehensive library of big data analytics algorithms that support parallelization. Scoring algorithms implemented using SAS Enterprise Miner can be deployed and executed within a Hadoop environment.

    In addition, integration with Apache Spark appears to be of growing importance. SPSS, KNIME, Oracle, RapidMiner and SAP all provide access to Apache Spark libraries to support analytics applications that need to scale with exploding data volumes. This enables developed applications to take advantage of a high-performance cluster platform to distribute the workflow across the cluster.

    Collaboration

    As noted, the larger the organization, the more likely there will be a need to share analyses, models and applications across different groups and among many analysts. Organizations that have many analysts distributed across the enterprise may look for increased means to share models and collaborate regarding the interpretation of results.

    IBM's SPSS Modeler Gold edition provides collaboration capabilities, and RapidMiner's Server product provides support for sharing and collaboration. Alteryx Analytics Gallery provides a mechanism for sharing sophisticated analytics applications in the cloud with members of an extended organization. KNIME offers commercial extensions to support team collaboration, as well as extensions supporting operational collaboration, such as remote-scheduled execution, report generation, shared data space and a workflow repository. SAS Enterprise Miner's client-server architecture enables business users and data analysts to work collaboratively by sharing models and other work products.

    Alteryx, KNIME and Teradata Aster have added capabilities to help manage analytical workflows. Also, some of the vendors have started to look at ways to enable their tools to integrate with others that may have complementary functional sweet spots. For example, Teradata Aster now has an extension to integrate with KNIME that enables users to leverage the KNIME workflow editor and incorporate Aster Analytics functions into those workflows.

    Vendor size and product integration

    Vendors can be compared in terms of their size. One might compare and contrast what could be referred to as the mega-vendors, whose big data analytics tools are just one product among a massive portfolio of tools. If you work for a larger organization that typically negotiates site-wide, enterprise licenses for the full suite of a vendor's tools from a mega-vendor such as IBM, SAS, SAP or Oracle may be a reasonable choice.

    The large vendors sell big data analytics tools that are a part of a much larger tool ecosystem. Presumably, the products from a mega-vendor will be at least somewhat integrated and intended to work together. In addition, some people feel more comfortable with bigger vendors, with an expectation of stability and consistent customer service. On the other hand, you may only be able to acquire these big data analytics tools as part of a much larger software licensing arrangement.

    Smaller vendors, such as KNIME, Alteryx and RapidMiner, have revenues that are generally based on licensing and support for a small number of big data analytics products. A smaller vendor may provide closer contact with their product management and innovation teams, and you may be able to influence the direction of the product roadmap or enhanced functionality.

    A smaller vendor might also be more flexible in terms of price and the features included in the licensing arrangement. You must realize, however, that working with a smaller vendor does present some risk in terms of stability, the resources available for support and the possibility that the company may be acquired, which can impact the customer relationship.

    The larger vendors are clearly responsive to user needs for integration with other systems, although that often centers on other products within each vendor's inventory. For example, SAP Predictive Analytics has improved integration with SAP HANA and BusinessObjects Cloud. SAS Enterprise Miner has added nodes to execute code in a SAS open, cloud-ready, in-memory Viya environment. Microsoft offers SQL Server R Services, an R installation that runs alongside SQL Server and enables users to integrate Microsoft R Server data with SQL Server and Microsoft's other business intelligence tools.

    Budget for licensing and maintenance

    Almost all of the vendors sell different versions or editions of their products, with a range of costs for acquisition and total cost of operation. IBM, Oracle, RapidMiner, Teradata and Microsoft sell editions at different tiers, with the license cost proportional to the features, capabilities and freedom from limitations in terms of the volumes of data to be analyzed or the number of processing nodes the product can use.

    KNIME and RapidMiner provide free and open source versions of their products, either charging for support services or for editions supporting enterprise-class applications. KNIME, RapidMiner and Alteryx have relatively low licensing costs for a smaller number of users. If you're considering SAS or SAP, you must contact them for pricing alternatives.

    The marketplace for big data analytics software can be a confusing place, but hopefully this article has helped you understand the benefits big data analytics software can provide your organization, and assisted you in differentiating between the specific tools examined here.


    Splunk: Paving The Way For IBM | killexams.com real questions and Pass4sure dumps

    No result found, try new keyword!With increased competition, Splunk will have to up marketing even more to differentiate its product. Valuation (Source: Own Calculations) (Source: Own Calculations) The key assumptions underlying the ...

    Using Big Data to Fight Fraud | killexams.com real questions and Pass4sure dumps

    Posted April 18, 2013 By Drew Robb     Feedback

    While Big Data can be useful in fighting fraud, traditional business intelligence tools aren't much help.

    If it was easy to detect fraud, there wouldn’t be much of it around. But these days, the sheer volume of information being generated makes it more difficult than ever to detect.

    “Organizations today typically have a fragmented approach to dealing with security and fraud risks, which leaves them vulnerable to even more attacks as criminals are quick to find and exploit any points of weakness,” said Ellen Joyner-Roberson, global marketing principal at SAS Security Intelligence Solutions. “As a result, security risks and financial crimes are increasing dramatically due to a weak global economy, growth in organized crime and the increasing sophistication of fraudulent schemes.”

    Not Your Usual Analytics

    Unfortunately, many current analytics systems do not support robust analytical modeling, making it difficult to process and analyze information fast enough to make a difference. Different departments often have disparate data sources and processes, so it’s tough to spot suspicious activity across the enterprise.

    In addition, there is a constant struggle to balance tougher anti-fraud efforts with a good customer experience. If vendors stop more transactions that are suspicious in an attempt to reduce fraud losses, they may anger customers trying to complete legitimate transactions.

    Banking, insurance, government, health care and retail are obvious targets for con artists. The banking industry, for example, has a big problem with identity theft and account takeover. There, it is critical that decisions are made in real time at the point of sale, which requires a monitoring and analytics approach that can instantly model for behavior changes and stop that transaction without getting it wrong too many times.

    Due to such demands, the consensus is that traditional business intelligence (BI) isn’t sophisticated enough. It needs to be backed up by domain-embedded rules and adaptive analytics that can be tailored to different types of fraud or financial crime. Context also plays a vital part, and integrating contextual information from mobile apps and other channels can heighten success. And geospatial and velocity data capture at transaction origination should be backed up by decision making in real time.

    “To fight fraud before it causes financial losses, companies are using real-time interdiction (definition: to forbid or debar) capabilities wherever they can, and with raw response times under 200 milliseconds,” said Joyner-Roberson.

    Her company developed SAS Security Intelligence to address this market. It comprises various SAS products to address fraud and improper payments, regulatory compliance and security. This includes SAS Fraud Management, SAS Network Analytics, SAS Financial Crimes Suite and SAS Fraud Framework.

    SAS Security Intelligence takes an enterprise approach to data management and consolidation, combining data integration, data quality and master data management into a unified environment that brings together cross-channel enterprise data on a single platform. It has the ability to score and decision all transactions in real-time and intercept suspect deals.

    Haystacks, Needles and Big Data

    Big Data only magnifies the problems inherent in tracking down wrongdoers. Just how do you deal with capturing, processing, and storing massive amounts of real-time data so you can learn and adapt to changing behavior patterns?

    “The needle in the haystack analogy is particularly appropriate for fraud detection due to the small amount of fraud compared to the whole of legitimate Web traffic,” said Jesse McKenna, head of Threat Research for Silver Tail Systems, RSA. “However, as fraud patterns constantly shift and evolve, so does the needle you are looking for. Today it may be a needle, tomorrow a pushpin or a nail.”  

    The challenge is that you don’t know exactly what you are looking for other than it will be different than the rest of the pile. While Big Data means you may be able to derive more insights, finding the meaningful data and building detection algorithms that aren't brittle and won't quickly become irrelevant takes a deep understanding of fraud.

    That’s where behavioral analytics and adaptive detection methods become important. You need ways to isolate what is different that you may have never seen before.

    Analytics apps also have to be of value to fraud investigators, who are the ones with the experience of the real world. Machines can burn through the data mountain to locate unusual occurrences, but they are nowhere near as good as people at evaluating and understanding context. Therefore, the app has to facilitate the people or fraud units it serves, not burden them with delays or lengthy procedures.

    RSA’s Silver Tail Systems product suite (consisting of Forensics, Mitigator, and Profile Analyzer tools) provides behavioral analytics, real-time custom rules and mitigation capabilities, as well as a forensics interface for performing investigations.

    For example, by monitoring the details of every HTTP/S request that is sent to a website, it learns what normal behavior looks like for the website and can then identify criminal behavior and alert the appropriate teams for investigation. Alternately, it can communicate with other network devices to redirect or terminate the Web session in real-time.

    User-based behavioral analytics (in addition to the population-based ones) helps identify when a particular user is deviating from their normal behavior, as is the case with many fraud and account compromise scenarios.  

    “Silver Tail was created by ex-eBay and PayPal fraud professionals who lived the battle against fraud at a time when they were protecting the two most targeted brands on the Internet,” said McKenna. “Their experiences motivated them to keep up with shifting Web fraud attacks instead of playing catch-up.”

    Visual Aids

    IBM is also involved in fraud analytics via a combination of IBM SPSS Modeler, SPSS Statistics, SPSS Collaboration and Deployment Services, SPSS Data Collection, SPSS Decision Management, IBM Cognos BI, Cognos Express, Cognos Insight, Cognos Disclosure Management and Cognos Financial Statement Reporting.

    IBM believes that visualization is a vital part in making sense of analytics information, especially when it comes to facilitating the work of professionals in fraud detection.

    “As data volumes continue to go up, BI tasks can benefit greatly from the use of information visualization techniques,” said Don Campbell, CTO, IBM Business Analytics. “Good visual representations can help individuals intuitively understand complex data. Visualization can be a powerful presentation and information consumption interface. But it doesn't stand on its own.”

    Graphical representation is also a focus of 21CT, a provider of analytics for investigation and pattern detection. It has released enhanced graph search for its LYNXeon product to help spot cyber attackers and document criminal behavior. The rationale behind the product is that machine -based attacks can be caught by machines, but human-based attacks require specialized tools to help people detect them before the damage is done.

    “Health care fraud, notably Medicare, is an enormous problem, costing billions a year,” said Kyle Flaherty, vice president of Marketing at 21CT. “The challenge is not only the enormous amounts of data, but also in the disparate connections within the data. Additionally, fraud has been rampant in this arena and the malicious entities have become sophisticated.”

    While regular BI tools provide insight into one slice of the overall information, Flaherty said, they are not good enough for fraud detection, which depends on operational insight and the ability for a human to hunt through visualized data, discover links, and detect overall patterns of activity.

    “Fraud detection is a human versus human battle, and BI tools can spit out more data and graphs to help illustrate the problem,” said Flaherty. “In the end you need to be able to hop around the data and investigate.”

    Drew Robb is a freelance writer specializing in technology and engineering. Currently living in California, he is originally from Scotland, where he received a degree in geology and geography from the University of Strathclyde. He is the author of Server Disk Management in a Windows Environment (CRC Press).



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