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IBMSPSSMBPDA exam Dumps Source : IBM SPSS Modeler - Business(R) ally Data Analyst Associate

Test Code : IBMSPSSMBPDA
Test title : IBM SPSS Modeler - Business(R) ally Data Analyst Associate
Vendor title : IBM
: 25 existent Questions

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IBM IBM SPSS Modeler -

IBM Watson Studio is Coming to Your desktop | killexams.com existent Questions and Pass4sure dumps

illustration of a Modeler flux operating on Watson Studio laptop

IBM Watson Studio is an extremely efficient statistics science providing. if you’re now not generic, Watson Studio is an integrated atmosphere designed to profile it facile to enhance, teach, and control models. It includes a big option of tools used for every thing from information prep and blend to graphic attention. And now Watson Studio is on its approach to your windows or Mac computer computer.

Overview of Watson Studio

Watson Studio includes a number of tackle utter within one built-in environment. essentially the most regular tackle of Watson Studio are:

  • SPSS Modeler for records prep, blend, and modeling devoid of coding
  • records Refinery, a spreadsheet fashion tool, that makes it possible for speedy, self-carrier records guidance
  • Python, R, and Scala Jupyter Notebooks to create and share documents that hold reside code, equations, visualizations, and explanatory textual content
  • Neural network Modeler for designing TensorFlow, Keras, PyTorch and Caffe buildings without coding
  • Interactive Dashboards to bring sharable are animate stories so the conclusion users can determine their own insights
  • attainable Deployment alternatives

    Watson Studio will quickly be obtainable as three deployment options:

  • Watson Studio on the IBM Cloud is the simplest passage to procure utter started. It offers 3 tiers from a free starter tier with pay-as-you-go compute utter of the approach to an traffic version.
  • Watson Studio local is their in the back of-the-firewall solution providing a relaxed manner to add Watson Studio capabilities to your business. comprises buy alternatives for a handful of clients or the complete organization.
  • And coming quickly: Watson Studio computer
  • Introducing Watson Studio computer

    Watson Studio laptop is their current third deployment option inside the Watson Studio family unit. It takes the most typical portions of what's attainable inside Watson Studio on the cloud and ports it to your home windows or Mac desktop for offline use, for evaluation of data information you don’t wish to upload, and for snappy performance. utter with a hasty and straightforward computer setting up.

    Watson Studio on the desktop will scale from an individual consumer utter of the passage as much as a big crew, and will be an outstanding addition to the Watson Studio household along with their cloud and endemic flavors.

    The prefatory main features of Watson Studio computing device should be a SPSS Modeler canvas along with the records refinery spreadsheet editor. this may consist of backwards compatibility for SPSS Modeler streams.

    merits of Watson Studio laptop
  • It’s quickly, specifically for endemic information. raise individual productivity the employ of a in the neighborhood retain in client.
  • It provides offline use. are looking to build a mannequin on a aircraft without WiFi? Watson Studio computer will travail with out an online connection for weeks at a time.
  • Adhere to security and governance mandates with your data. Watson Studio computer enables your entire information to continue to be to your laptop.
  • unlimited modeling without overages. using Watson Studio computing device, that you may develop models with None incremental charge. This allows you to build models iteratively in your computer, then circulation it to Watson Studio on the Cloud to set up.
  • It’s one Watson Studio, so no re-gaining scholarship of required. With the identical designs because the cloud and local versions, switching between deployment alternate options is seamless and not using a unique working towards.
  • And identical to the ease of Watson Studio, any individual — coders or non-coders — can be capable of retain together, mix, discover, and mannequin their records in barely minutes.
  • Watson Studio computing device’s potent graphing skillsJoin the Beta

    The Beta for Watson Studio is birth now with two tools already ported to the desktop: a refreshed edition of SPSS Modeler & data Refinery. it's going to besides include initiatives to prepare your work, information assets corporation, and a handy investigate for updates feature.

    be fraction of the beta to deliver remarks on how you love these equipment, and recommend which other tools they convey to the desktop. The Beta is fraction of the IBM SPSS Modeler Subscription Trial, so once you register for that you’ll beget entry to each downloads.

    After you register, search for the “IBM SPSS Modeler Subscription Trial” card, click on down load, then you’ll observe the Watson Studio computing device Beta downloads on the right hand aspect of the web page.

    join the dialogue on the beta group

    After registering for the beta, be a fraction of in the dialogue on the Watson Studio laptop beta neighborhood. here you can examine questions about the beta, give ideas for future aspects, procure forum support, and occupy fraction in constructive surveys to repercussion product course. be fraction of the discussion board here through logging in with your IBMid.

    stay up for future bulletins here about this wonderful addition for the Watson Studio family.

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    IBM is speeding up SPSS with Apache Spark | killexams.com existent Questions and Pass4sure dumps

    IBM nowadays introduced that it's bettering a pair of of its present software items with the Apache Spark open-supply records processing engine, together with the SPSS predictive analytics utility.

    IBM bought SPSS for $1.2 billion in 2009. SPSS itself utter started at Stanford institution in 1968 and is widely used in records classes at universities.

    IBM is in particular incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman informed VentureBeat in an e mail.

    Spark is besides being utilized to IBM’s BigInsights, Streams, and DataWorks software, in accordance with a press release.

    The Sparkification of IBM comes after huge Blue introduced a major dedication to the open-supply application in June. on the time, IBM introduced Spark as a carrier on its Bluemix cloud; that carrier is now often attainable.

    IBM has made greater than 60 contributions to Spark considering the June announcement, in accordance with the remark.


    IBM continues to first-rate-tune DSX in concert with Hortonworks | killexams.com existent Questions and Pass4sure dumps

    virtually one year in the past, IBM Corp. announced plans to blend its statistics science platform with Hortonworks Inc.’s software for data administration. What IBM dropped at the partnership changed into its records Science journey, or DSX, solution, designed to let builders create analytics fashions and computing device getting to know tools right away.

    considering that then, IBM has been nice-tuning its information science model via adding tackle for DSX clients to access advice kept inside the Hortonworks information Platform information lakes.

    “DSX and Hortonworks are integrated to the aspect the space you could manage your Hadoop cluster and you can travail for your Python fashions within DSX, after which propel it remotely to be finished the space your information is,” referred to Piotr Mierzejewski (pictured), software director of construction DSX, statistics science and machine discovering at IBM. “That’s one of the crucial massive value propositions.”

    Mierzejewski spoke with James Kobielus (@jameskobielus), host of theCUBE, SiliconANGLE Media’s cellular livestreaming studio, on the DataWorks Summit in Berlin, Germany. They mentioned latest and shortly-to-be added points for DSX and using records analytics tackle in a variety of enterprise environments. (* Disclosure below.)

    The newest version of DSX comprises a few key features for builders, including IBM’s SPSS Modeler that offers predictive analytics to unearth information patterns, support resolution making, and profit more forecasting accuracy. “As of DSX 1.2, they now beget integrated an SPSS modeler, redesigned and rebranded,” Mierzejewski observed. “here is magnificent expertise from IBM that has been round for a while.”

    within the subsequent two months, IBM plans so as to add natural language processing along with text and sentiment evaluation to DSX, based on Mierzejewski. The purpose is to present customers full-scale flexibility, whether the facts resides on-premises or within the cloud.

    “With DSX, they designed it to install, race and scale in every lone place,” Mierzejewski referred to. “Now you may beget this extendable collaborative platform that can deploy and race in data centers without wanting to access the information superhighway. That’s important.”

    Watch the comprehensive video interview under, and be positive to try greater of SiliconANGLE’s and theCUBE’s coverage of the DataWorks apex eu. (* Disclosure: Hortonworks Inc. subsidized this section of theCUBE. Neither Hortonworks nor different sponsors beget editorial manage over content on theCUBE or SiliconANGLE.)

    photograph: SiliconANGLE in view that you’re here … … We’d love to uncover you about their mission and how which you can aid us fulfill it. SiliconANGLE Media Inc.’s traffic model is in line with the intrinsic value of the content material, no longer advertising. unlike many on-line publications, they don’t beget a paywall or race banner promoting, as a result of they wish to maintain their journalism open, with out repercussion or the should chase site visitors.

    The journalism, reporting and commentary on SiliconANGLE — together with live, unscripted video from their Silicon Valley studio and globe-trotting video groups at theCUBE — occupy loads of difficult work, time and funds. protecting the exceptional extravagant requires the aid of sponsors who're aligned with their vision of ad-free journalism content.

    if you love the reporting, video interviews and other advert-free content here, please occupy a flash to try a pattern of the video content supported via their sponsors, tweet your assist, and hold coming lower back to SiliconANGLE.




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    IBMSPSSMBPDA exam Dumps Source : IBM SPSS Modeler - Business(R) ally Data Analyst Associate

    Test Code : IBMSPSSMBPDA
    Test title : IBM SPSS Modeler - Business(R) ally Data Analyst Associate
    Vendor title : IBM
    : 25 existent Questions

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    Virginia “Ginni” Rometty, the ninth chief executive officer in IBM’s 103-year history, is making her passage down a long corridor at the company’s headquarters in Armonk, N.Y. To her left, a train of large, gold-framed oil paintings depicting her predecessors—men love Samuel Palmisano, Louis Gerstner, John Akers, and founder Thomas Watson—looms over her, shadows of IBM’s storied past. Dressed in crisp shaded suits and ties, the former CEOs ogle at Rometty, the first woman to race the $100-billion-a-year tech giant, as she passes by.

    Her ascension to the top job at considerable Blue may beget made history, but Rometty, 57, doesn’t dissipate time waxing on about the significance of her glass-ceiling-smashing career. In addition to being the first woman to head IBM, she is one of just 24 female CEOs in the Fortune 500 (for the third year in a row, she besides tops Fortune’s list of Most Powerful Women). In fact, Rometty doesn’t dissipate much time on anything not viewed as essential to “transforming” the company to which she has devoted much of her adult life.

    Ginni’s Rules1. Don’t protect the past.2. Never be defined by your product.3. Always transform yourself.

    An IBMer since the early ’80s, Rometty assumed the top job in 2012 and has since made a train of sweeping changes. She snapped up cloud-services provider SoftLayer Technologies for $2 billion ultimate year and has pledged to invest $1 billion toward the progress and commercialization of Watson, a so-called cognitive-computing system capable of sifting through millions of scientific papers in seconds. She has besides sold off some of IBM’s lower-margin businesses, made cuts in its still-massive employee base, and even simplified contracts for clients (bringing down the typical number of pages from 30 to just four).

    More recently she has brought the dowdy centenarian IT company into head-turning relationships with some peppery California celebrities. Witness its unprecedented alliance with its frail compete Apple, announced in July, which will bring IBM services to the iPhone maker’s iOS platform.

    “I reflect she’s wicked smart,” Apple CEO Tim Cook says of Rometty (more on this truly yin-and-yang partnership later). “She has an incredible competence to ally and can profile tough decisions and conclude so decisively. And she sees things as they really are.”

    The passage things really are can be summed up with one word: challenging. Despite Rometty’s propel into explosively growing areas love cloud, mobile, and Watson, IBM’s revenue has shrunk for nine quarters in a row. In its most recent quarter the company reported sales of $24.4 billion, down 2% from the year before. (Annual sales of $99.8 billion in fiscal 2013, meanwhile, were down nearly 5% year-over-year.) Revenues from its three core businesses—services, software, and hardware—have been sluggish or, worse, in a downward spiral. Perhaps most disruptive is that corporate customers are changing their buying habits. More and more of them are opting for a software-as-a-service model instead of investing in clunky, costly hardware that requires armies of consultants just to procure up and running.

    To complicate things even more, while Rometty is arduous at travail trying to ramp up growth areas to offset the declines in the company’s core products, she is simultaneously hampered by an aggressive profit road map passed down by her predecessor, Palmisano, who pledged that earnings per share would attain $20 by 2015. (IBM expects $18 in EPS this year.)

    “They created a model which the street loved until it hated,” says one Wall Street investor who does not wish to be named. Indeed, since January 2012, when Rometty became CEO, shares of IBM beget inched up only 4%, compared with the 58% ascend of the benchmark S&P 500 index.

    OLD RIVALS, current PARTNERS: ROMETTY AND APLE'S TIME COOK TALK THE FUTURE IN CUPERTINO, CALIF. SAYS COOK: "I reflect SHE'S WICKED SMART."

    OLD RIVALS, current PARTNERS: ROMETTY AND APPLE’S TIM COOK TALK THE FUTURE IN CUPERTINO, CALIF. SAYS COOK: “I reflect SHE’S WICKED SMART.”PHOTO: PAUL SAKUMA—AP

    Add to these everyday challenges an existential one: the need to bring a starched-shirt company through a massive cultural transformation. Yes, IBM has reinvented itself in the past. But this time around, there isn’t just one technology shift to contend with—as in affecting from mainframes to client-server systems—but rather multiple transitions hitting at the selfsame time. There’s the movement to cloud computing, an increasing need for big-data software, and fast-growing exact for mobile and social tools in the workplace. What’s more, IBM now finds itself competing with a slew of current and agile rivals, from Amazon (an online retailer) to Google (a search engine). “Agile” isn’t exactly the word that pops to intellect when thinking of IBM, whose leadership team and board of directors beget gone largely unchanged for years. (Fun fact: The middling age on its 13-person board is 64.)

    But if anyone can haul off this metamorphosis, insist many insiders and outsiders, it’s Rometty. In wide-ranging interviews with current and former IBMers as well as company observers, analysts, and investors, nearly everyone agreed that Rometty has the right energy—and perhaps more important, the right leadership style—for the job. “She has this unique competence to profile you comfortable in the change while still being uncomfortable,” says John Kelly, senior vice president of IBM Research. “It’s really arduous to describe.”

    Rometty has besides shown that she can strain fast. She has already realigned the company’s strategic investments, for example. But to succeed, she needs to profile even bolder, faster moves. “What’s different from the past—because we’ve lived through every one of these transitions—is the hurry at which this is happening,” Rometty says during a recent interview at IBM headquarters. “We beget to sustain affecting at that speed.” Not an facile thing to conclude when your employee population is bigger than Cleveland.

    “This is the time for Rometty to prove wrong the thesis that big enterprises beget no haphazard of surviving through innovation.”—Jeffrey Sonnenfeld, senior associate dean, Yale School of Management

    Everything about IBM is big. The hardware, software, and consulting empire, founded as a maker of commercial scales and punch-card tabulators in 1911, employs more than 431,000 workers across the globe. Today the company that Lou Gerstner famously likened to an elephant sells everything from disk storage systems to collaboration software to cloud infrastructure offerings (and thousands of other products). It operates in 170 countries. Its global services arm—which primarily advises companies on how to employ their technology—happens to be one of the largest consulting firms in the world. And at 368 acres, even its sprawling, tree-lined campus north of Manhattan is a testament to its size and breadth.

    Dressed in a acute black jacket and skirt, her perfectly coiffed blond hair tucked behind a bejeweled headband (Steve Jobs had the turtleneck; Rometty owns the headband), the CEO sits down to breakfast on a recent Wednesday morning in a room immediate to her Armonk office. When asked whether IBM does too much, Rometty demurs: “Maybe it’s because I’m used to the scale,” she says, “but I feel it’s very focused, actually—broad, but focused.” fraction of that assessment is a reflection of her own reinvention design for IBM, which involves trying to procure the company to concentrate on three core areas—big data, cloud, and what she calls, simply, “engagement” (that is to say, mobile and social technologies).

    Many relate Rometty as collaborative and insist she is a pellucid communicator. Tall and striking, she gives off an almost regal air when she walks into a room. She is besides surprisingly approachable. “She’s extraordinarily confident and very warm at the selfsame time,” says Ann Winblad, managing director of venture capital arduous Hummer Winblad Venture Partners. “That’s very rare.”

    Rometty, who started at IBM as a systems engineer 33 years ago, spent decades climbing through the ranks before being picked to race the company in January 2012. “About a year before the transition [from former CEO Palmisano], they brought Ginni out center stage,” says Winblad. “It was very pellucid she was emerging as the current leader of IBM, and it was attractive for me to witness the handoff and watch her just step forward.”

    Step forward—and sharply to the West. Barely weeks into the job, she flew utter 17 of her SVPs out to Silicon Valley to meet with top venture capital firms love NEA and Accel Partners—a first for IBM. “You saw the two worlds together, and that was Ginni’s passage to insist this is really important,” says Claudia Fan Munce, managing director of IBM’s internal venture capital group. “Not only conclude they need to be buying [startups]—as leaders they need to be very tuned in to what is going on.”

    Clear communication inside IBM has besides been a considerable focus for Rometty. At her very first staff meeting after taking over the CEO job, she gathered her SVPs in a company boardroom and told each of them three things he or she did well (and in some cases what each could ameliorate on). The executives were caught off-guard by the unprecedented open airing of feedback. “I had never seen this happen,” recalls Kelly, the head of IBM Research. “She was sending a signal to everybody in the room that you’re fraction of my team, and, you know, you’re utter Good at positive things.”

    A computer science major in college, Rometty wasted no time reaching out to the company’s legions of engineers too. One of her first orders of traffic was to deliver a webcast to the company’s 3,000 researchers across the world. “We utter listened,” says Dharmendra Modha, an IBM fellow and chief scientist for “brain-inspired computing” who works out of the company’s R&D center in California’s Almaden Valley. “And you know, the first words out of her mouth were, ‘This is a current era of computing—cognitive computing.’” Modha and his colleagues were thrilled. The message was one they hadn’t heard in a while: IBM was going to be a cutting-edge technology company again, not just an army of buttoned-down consultants.

    That outreach and vote of self-confidence probably helped when, several months later, Rometty initiated a reorg of the entire research division for the first time in nearly two decades. IBM’s labs had been categorized under the older, traditional traffic units love hardware, software, and services. Under the current strategy they are realigned around her three technological pillars—big data, cloud, and engagement.

    Rometty has devoted so much time to communication that it’s a phenomenon she has time for anything else. ultimate year, for example, she launched an ambitious online education program called reflect Academy, available to IBMers and the company’s partners. Topics for monthly classes sweep from the current era of data security to changing cloud platforms to the infrastructure challenges of Africa—many of which Rometty introduces or teaches herself. And if there’s a reflect Academy interview with, say, a CEO of a company that’s using IBM software, import on the interlocutor to be Rometty as well.

    “I said, ‘Look, as the company goes through a transformation one more time, every IBMer needs to understand [we] must attain together with one lone voice for an entire company,’” says Rometty. “When you’re on a scale love they are in 170 countries and hundreds of thousands of people, you beget a lone point of view.”

    Increasingly these days, that point of view is being shaped at trillions of operations per second.

    Inside the gleaming glass edifice at 51 Astor Place—located in the heart of current York City’s Silicon Alley—workers in white hardhats are diligent putting the final touches on the four floors that will soon house the newly formed Watson group.

    Tables and chairs in the open workspace are on wheels so that staffers can huddle anywhere. Instead of fixed desks, employees will procure bright-blue lockers to hold their belongings. The current Watson team digs, which will be ready in October and eventually hold upwards of 600 employees, feels more love a big startup’s headquarters than an IBM office—and that’s the point. IBMers will not only be neighbors to staffers at Facebook (across the street) and Twitter and Google (a few blocks away), but besides coexist in what Michael Rhodin, SVP of the Watson group, hopes will be a thriving, dynamic ecosystem of advanced technology and fresh ideas.

    “We need to beget the obstruct parties and the networking events and utter of those things,” says Rhodin. “And they designed the space intentionally so that they could participate that way. I reflect that’s going to be attractive because that’s not typical for us as a company. It’s another illustration of how we’re trying to be a puny bit different with Watson.”

    MICHAEL RHODIN (LEFT), HEAD OF THE WATSON GROUP, AND ROMETTY parley WITH DAVID ALDOUS OF SANOFI, A WATSON CLIENT, IN AUGUST.

    MICHAEL RHODIN (LEFT), HEAD OF THE WATSON GROUP, AND ROMETTY parley WITH DAVID ALDOUS OF SANOFI, A WATSON CLIENT, IN AUGUST.PHOTO BY trail PETERSON FOR FORTUNE

    It’s been more than three years since Watson—the result of decades of research on artificial intelligence and natural-language processing in IBM labs—beat human opponents on an episode of Jeopardy! And since Rometty became CEO, she has made it a priority to transform the question-answering supercomputer from a game-show gimmick to a bona fide business. That has meant testing Watson in real-work environments, such as the current York Genome Center, and opening it up as a platform for which third-party developers can create applications. Earlier this year, Rometty not only made the Watson group a standalone traffic unit but besides announced that she was dedicating $1 billion to its progress and commercialization—a sum that includes a $100 million investment fund for startups that develop applications for the powerful computing system.

    IBM had spun out current traffic units in the past, as it did with its global services division, but the exercise was rare. With the creation of the Watson group, Rometty oversaw the largest lone movement of IBM Research personnel in the company’s history—some 2,000 employees in all—as squadrons of specialists and consulting and sales teams shifted to the current cognitive-computing operation. “Watson is just an early glimpse of what these systems are going to do,” says Kelly. “They’re going to procure smarter. They’re going to conclude deeper reasoning. They’re going to beget more senses than just reading. They’re going to see. They’re going to touch. They’re going to feel. And they observe no fundamental limits to that capability. None.”

    For now, Watson is still being tried out in a handful of different industries, love health care. At Memorial Sloan Kettering Cancer center in current York City, doctors are training the system to rapidly sift through medical journals and patient data in an endeavor to wait on medical professionals pick efficient treatment options for cancers. “Watson’s competence to learn on the flit is really unique,” says Craig Thompson, CEO of the hospital and research institution.

    But for utter its buzz—and its undeniable potential—this current artificial brain of a computing system has yet to strain the needle for IBM. Memorial Sloan Kettering, for example, is a partner, not a paying customer. Rhodin says Watson has “dozens” of paying customers, including the Mayo Clinic and USAA, the insurance and fiscal planning arduous for military service members and veterans. “Our internal metric is they study to token one to two current customers a week,” he says. “That keeps us going, keeps us busy.”

    Rometty points out that IBM already ended ultimate year with $16 billion in data analytics sales. (The company doesn’t crash down the exact sources of those revenues.) And, she says, Watson is a pot for the long term. In the meantime, she closely manages the current traffic group’s progress via monthly meetings with its senior leadership team. “It [Watson] will reinvent careers and reinvent industries,” says Rometty. “It is the third era of technology.”

    Rometty realizes that despite IBM’s size and technological know-how, it can’t conclude everything alone. That’s why ultimate July, following about eight months of behind-the-scenes deliberations, she announced that IBM and Apple would ally to develop services optimized for iOS, the iPhone maker’s mobile operating system. As fraction of the deal, Apple will provide 24/7 support for devices purchased by IBM customers.

    “We’re 100% complementary,” says Cook, Apple’s CEO. “Usually you beget overlaps and differences of opinion. But they both brought different assets to it. The veracity is that neither of us could conclude this on their own.”

    According to Cook, his talks with Rometty were originally focused on a different kindly of partnership (he declined to insist what was initially on the table). Those plans never materialized, but along the passage the two CEOs realized that a partnership to promote mobility in the enterprise made the most sense.

    “Watson is just an early glimpse of what these systems are going to do. They’re going to see. They’re going to touch. They’re going to feel. They observe no fundamental limits. None.”

    “We had tons of meetings, and the Good thing about it was the meetings were pretty broad-based—our engineering teams, their engineering teams, their industry leaders, their marketing and developers—as they thought through with us how they were thinking about various apps,” says Bridget van Kralingen, SVP of IBM’s global traffic services (and No. 33 on Fortune’s list of Most Powerful Women). According to van Kralingen, several big customers beget already signed up to co-develop mobile apps along with IBM and Apple. The two companies’ sales teams, meanwhile, beget been co-training to profile positive they are well-equipped to cross-sell each other’s products. “Most mobile devices are [used for] 60% email,” says Rometty. “That’s why when they did this Apple partnership, they said they could reimagine work. These devices can conclude passage more.”

    Perhaps Rometty’s most significant—and necessary—move to date was shelling out $2 billion on cloud-computing player SoftLayer ultimate year, though critics insist it’s an acquisition IBM should beget made even earlier.

    Just months before acquiring SoftLayer, whose technology is now the underpinning of utter of IBM’s cloud offerings, the company suffered an embarrassing loss to compete Amazon. In 2013 it was revealed that the e-retailer, not the enterprise giant, had won the bid for a lucrative contract to build and operate a private cloud facility for the CIA. IBM protested the award partly on the grounds that its own bid was $54 million lower than Amazon’s. Again, IBM lost.

    “Amazon beat the precursor to SoftLayer, which was what they muster SmartCloud Enterprise,” admits Lance Crosby, CEO of SoftLayer, which is still race as an independent company inside IBM. “They simply had a better product at that time.” Rometty, for her part, mostly agrees, conceding that the company could beget been better equipped to fight for the CIA contract. “I would insist this was a learning experience,” she says. “And in some of the capabilities, I reflect they had a very Good response, but I reflect some of them would beget been even better with SoftLayer.”

    Since the CIA contract debacle, however, IBM has invested massively in cloud—not just in acquiring SoftLayer but besides in spending $1.2 billion to open 40 current cloud-powered data centers across the globe, including two current secure centers designed specifically for the U.S. government. “If you were to study at IBM’s cloud endeavor a year ago, it was a nice endeavor for a traditional IT vendor but not where it needed to be,” says open Gens, chief analyst at research arduous IDC. “In my intellect that utter changed when IBM decided to buy SoftLayer and build its cloud deployment on top of world-class cloud infrastructure—one that can actually compete toe-to-toe with other providers.”

    Today, IBM has not only SoftLayer’s public cloud capabilities but besides a current platform called BlueMix for developers who want to race utter sorts of cloud-based apps. “Ginni really understood that they beget utter of these customers worldwide that beget massive investments either in their own data centers or in IBM’s data centers and they want to strain to cloud,” says Crosby. “But it’s going to be a journey to procure there.”

    The company’s cloud revenue is growing, certainly—indeed, at a rate of 69% from 2012 to 2013. But at $4.4 billion, cloud sales are still a minute fraction of IBM’s nearly $100 billion in annual revenue. And it’s that scale—in a sense, the challenge of competing against frail IBM—that makes Rometty’s job so damn challenging.

    Her investments in Watson and the partnerships she has inked on the mobile front beget borne fruit, but nowhere near enough to offset the declining revenue across IBM’s larger businesses. At least not yet. Africa, another pot for Rometty (in late 2013 she opened a current IBM research center in Nairobi, the first on the continent) is most likely years, if not decades, away from any recrudesce on investment.

    In the meantime, it’s unclear when IBM will recrudesce to revenue growth (a question Rometty declined to answer). ultimate April, in an interview with CNBC, investor Warren Buffett—who now owns more than 6.5% of IBM—addressed the company’s decreasing revenue. “It did not strike me as a considerable surprise—what they reported,” Buffett told his interviewer. “Now, it may be a dumbfound a year from now, or two years from now.”

    How does IBM profile money? considerable Blue's revenue dipped 5% ultimate year, but the tech giant still raked in nearly $100 billion annually. Here's where most of that money came from.

    How does IBM profile money? considerable Blue’s revenue dipped 5% ultimate year, but the tech giant still raked in nearly $100 billion annually. Here’s where most of that money came from.Graphic Source: IBM’s 2013 Annual Report

    For utter the talk about IBM’s challenges, it’s worth giving at least a nod to the obvious: that wherever the 103-year-old company is heading—either to transformation or irrelevance—it’s not likely to arrive there anytime soon. It still ranks as No. 23 on the Fortune 500 list and is well embedded in the IT infrastructures of many corporations. Its technology, in fact, powers 90% of banks and 80% of airlines across the globe. Moreover, 70% of enterprise data flux through IBM’s systems in some way.

    In the age of disruption, however, nobody—and least of utter Ginni Rometty—is taking any leadership position, even a bedrock-entrenched one, for granted. Yeah, breaking up with considerable Blue is arduous to do, but it’s not impossible. And for newer companies—especially those conceived in Silicon Valley—the need for IBM has been replaced by the likes of Amazon’s Web Services and countless smaller developers of cloud-based applications.

    The acquisition of SoftLayer—at the time of its purchase, the company had 20,000 clients, many of them startups—should wait on mitigate that problem to some extent. So should Rometty’s outreach to Silicon Valley via its venture capital arm and its fledgling Watson ecosystem. But some insist Rometty could and should conclude even more. “There are two perceptions of IBM in the Valley,” says Manoj Saxena, a former common manager of IBM and a current venture capitalist with the Entrepreneurs’ Fund. “The first is that they want more from them, and faster. The second is that they didn’t hope [Watson and other emerging technologies] from IBM; they expected it from Google.”

    Why should Rometty care about what Silicon Valley thinks? Because that’s where some of the biggest future buyers of enterprise technologies—and potential acquisition targets, customers, and partners—are sprouting up these days. If IBM becomes immaterial to startups and developers, Rometty’s bets on mobile, cloud, and Watson, which utter depend on edifice out an ecosystem, would be even more challenging to execute quickly. If she can prove that she can play with younger, nimbler companies, she may well succeed. One passage or another, this is the corporate juncture that MBA students will study back at and study one day: muster it “the Rometty crossroads.”

    “This is a considerable time for more repositioning,” says Jeffrey Sonnenfeld, a senior associate dean at the Yale School of Management. “This is the time for Rometty to prove wrong the thesis that big enterprises beget no haphazard of surviving through innovation.”

    Back at IBM’s headquarters in Armonk, Rometty remains confident that she now has the right pieces in space to execute a transformation. “You know, if I beget learned nothing else in utter my years here, my biggest lesson is you beget to constantly reinvent this company,” says Rometty. “That’s how you procure to be 103 years old.”

    Whether that centenarian will dance or stumble under its own elephantine weight will be the climax of one of the considerable traffic stories of their age.

    This tale is from the October 6, 2014 issue of Fortune.


    Text Analytics Opportunities and Challenges for 2010 | killexams.com existent questions and Pass4sure dumps

    My year-ago item, Perspectives on Text Analytics in 2009, was my second-most-viewed BeyeNETWORK article ultimate year. On the assumption that those readers weren’t utter employees of the vendors I quoted, given reader interest, I thought I’d revisit the topic. After all, users benefit by knowing what vendor executives are seeing and planning for the year ahead. Given the nature of the text-analytics market – solutions are not standardized, much less commoditized – customers invest much more than money when they token with a vendor, and they want to remain positive they’ll invest well.

    No lone solution provider dominates text analytics. No lone provider dominates any significant text-analytics market segment: not customer experience, media and publishing, fiscal services and insurance, intelligence, life sciences, or e-discovery. This is Good advice for current and prospective users. Each provider, to sojourn competitive, has to innovate, to sojourn ahead of evolving customer requirements. Further Good news, judging from provider fiscal results and from my discussions with immediate users, is that most vendors are on target. They beget done a Good job tracking and meeting emerging needs. They need to sojourn on track in 2010.So I posed to vendor executives the selfsame question as ultimate year, just updated by a year:

    What conclude you observe as the 3 (or fewer) most primary text-analytics technology, solution, or market challenges [or opportunities] in 2010?

    I’ll again relay responses with minimal editing, condensed in just a few cases. conclude sustain in intellect that the vendors quoted doubtless observe the greatest opportunities and challenges as residing in areas their solutions address. This focus is completely understandable; it simply reflects what’s most primary to them and their companies’ commercial success.Text Analytics in 2010 Let’s start with Ian Hersey, Global Chief Technology Officer and EVP, Products of the Attensity Group. Ian cofounded Inxight and later joined traffic Objects and then SAP in a succession of acquisitions a pair of years back. According to Ian, there are two considerable occasion areas that will be front and center in 2010:
  • Moving text analytics from a standalone application into a component of other applications. As with its larger cousin, the traditional BI solution, as a platform you beget a limited set of very specialized users. But there's a need for analytics about textual content to be a fraction of many applications that deal with textual content, for example, applications that wait on automate customer service in the contact center should provide analytics about customer issues back to the business, not only to ameliorate operational efficiency back to the muster center management but besides to share more existent time views into product or service issues with the ease of the business.
  • Incorporating social media into text analytics in ways that better benefit businesses. Today they observe a number of players out there focused on simple social media aggregation and lightweight analytics (e.g., buzz analysis, media monitoring), but they observe customers wanting to incorporate the social media into the selfsame analytical models as they employ for their internal data and, more important, plug that social media into traffic processes. An illustration again in the zone of service is to be able to monitor forums for technical solutions to user issues and to vet and potentially incorporate those solutions into your own web self-service portal.
  • According to Ian, the biggest challenges in realizing these opportunities include:
  • The need to pre-build "out of the box" analytical models for the various applications. Today, high-end text analytics deployments involve definition of both the dimensions you want to employ in your analysis as well as the tuning of the back-end text analysis componentry in order to achieve lofty levels of accuracy. Making that more self-service while still achieving confidence in the data you create is perhaps the industry's biggest overall challenge.
  • The "language" of social media is different, and often context is fairly limited, which makes deriving value from much of it quite difficult. There is besides a LOT of it, so being able to effectively filter the data that's really valuable up front is very primary and not easy.
  • Breck Balwin of Alias-i is co-author of LingPipe natural-language processing software. The first of his two points takes us 180 degrees away from the need Ian Hersey sees to “pre-build ‘out of the box’ analytical models” –We are focusing on customizing text analytics solutions quickly and cheaply for customers. For example, most commercial sentiment classifiers are generic and performance suffers accordingly. They are focused on creating customized models at similar price points with a combination of dynamic learning approaches (this maximizes the value of a human annotators efforts) and better user interfaces for getting training data.

    We beget besides started to employ throng sourcing as a means to create training data quickly [for employ with in model edifice via machine learning]. A nice illustration of this is their autism project which uses non-specialist volunteers to verify whether a highlighted phrase is a gene or a protein. beget a study at http://bionotate.hms.harvard.edu/autism/index.html. This project combines throng sourcing with machine learning and supports the lofty degree of customization necessary for lofty quality analysis of the autism literature.

    Breck’s a considerable guy for lending Ian Hersey one of his “3 (or fewer)” points (even though Breck didn't know he was doing that). Thanks Breck! One of Ian’s points related to affecting text analytics from a standalone application into a solution component, and others of his and Breck’s points focus on adapting the technology for users.

    Claire Thomas, text analytics lead at SAP, (independently) shares these views. Claire sees as notable 2010 text-analytics challenges –

  • Adoption by mainstream traffic users remains the biggest challenge for their industry. They beget made strides in showing their relevance for current user groups, with marketing departments interested in customer feedback analytics, and sentiment analysis being broadly applicable to various industries and initiatives. However, the vast majority of traffic users still view text analytics as a specialized, emerging technology.
  • One considerable blocker to mainstream adoption is integrating text analytics into solutions that attain beyond the minute number of specialists using simple text analytics applications. This requires text analytics to be accessible for the masses, but that can’t be at the expense of compelling results. That’s not an facile poise to achieve, especially given the common view that modern technology should provide instant value.
  • We still need to procure the traffic world to observe textual data as a valuable asset to be managed. The issue is that while most structured data is inherently valuable, with unstructured data, there’s no control on the input, so it’s often arduous to uncover which fraction of it is primary to store and analyze, not to mention what should be trusted. Additionally, unstructured data requires an extra step for sorting and sifting before any profiling and reporting can be done, posing yet another barrier to mainstream adoption.
  • Aaron B. Brown’s responses resound Claire’s and others I’ve cited to this point. Perhaps SAP and IBM, Aaron’s employer (IBM traffic Analytics & Process Optimization Software), beget been passing around a pitcher of mega-vendor Kool Aid. Aaron wrote me:Market momentum has turned the spotlight onto three key challenges that countenance text analytics as it begins to inhabit [a] current mainstream role:
  • Meeting the growing solution need. Text analytics has traditionally establish its niche in focused solutions for voice-of-customer insight, intelligence, and, more recently, e-discovery. As these solutions are becoming mainstream, the innovation frontier is affecting rapidly outward, and forward-thinking organizations are looking for current text analytics solutions that address a broader set of problems, including healthcare optimization, fraud mitigation, proactive quality management, and more. This explosion of solution exact will challenge text analytics vendors and their technologies, in both the current demands that these applications retain on the technology and the current requirements they impose for sophisticated analytical processing and seamless integration with broader information management capabilities (such as BI, BPM, collaboration, master data management, and predictive modeling) – both elements of creating complete solutions that fully address the traffic need.
  • Quantifying the traffic case for text analytics. Quantifying the ROI and creating the traffic case for text analytics projects is still mostly an knack – particularly when the buyer knows there is insight trapped in unstructured information, but the exact value of that insight is unknown until well into the execution of the project. Organizations (and vendors) are still learning how to navigate these challenges, and this will remain a fundamental challenge to text analytics growth in 2010.
  • Closing the value loop by operationalizing the insight of text analytics. Historically, text analytics has served one of two usage patterns – exploratory text analytics to determine current insights from poorly understood content sets and operational text analytics to automate routine extraction of text for employ in BI and reporting environments. To fully address its potential as a lever for traffic optimization, text analytics has to evolve to integrate these two patterns and in doing so deliver staunch closed-loop performance management of content-rich traffic processes. With exploratory text analytics identifying previously invisible process problems and bottlenecks, and operational text analytics enabling ongoing oversight of those problems through continuous delivery of text-based insight to reporting and predictive modeling tools, organizations can finally retain a performance management lens on content-rich processes. Leading-edge organizations are already starting to conclude this to ameliorate healthcare delivery, reduce fraud and crime, proactively avoid product quality issues, and more; the challenge in 2010 is to profile this transformative capability a mainstream reality.
  • A Focus on Sentiment Analysis All three respondents I’ve quoted so far beget cited sentiment analysis. You’ll observe that text-analytics application recur in additional responses. Next, the response of Olivier Jouve, vice president of corporate progress at SPSS, an IBM company. Olivier relates two challenges that IBM and SPSS beget anticipated through their solutions:
  • Multi-lingual, multi-source sentiment analysis: Twitter, Facebook and other Web 2.0 media are the current captious sources for marketing. They present a variety of challenges for text analytics: they are voluminous, cryptic, multi-lingual, and deeply interconnected. Text analytics solutions require sophisticated data collection mechanisms, advanced multi-lingual analysis and the infrastructure to manage daily terabytes of data.
  • Associate buzz and sentiment to operational data: For a CMO, extraction of buzz and sentiment has puny value if it can't be connected to operational data love marketing spend, basket analysis, shopper cart, or marketing campaigns. Predictive analytics, when associated with text analytics, allows [users] to detect early signals, measure or even anticipate crusade effectiveness, and act upon buzz measurement, for instance, to spin detractors into influencers.
  • David Seuss, CEO of Northern Light, says that vendors need imagination, however, in making the case to current and prospective sentiment-analysis users:The most primary challenge for the text analytics industry is developing applications beyond sentiment scoring that beget a commercial market. There is puny money being earned... in any zone other than reputation management based on sentiment scoring. The industry has demonstrated a stunning need of... current ideas that educate the market as to the potential text analytics has in traffic research, sales occasion discovery, competitive intelligence, strategic planning, product development, purchasing evaluations, and technology research.

    This is not an facile task, as witnessed by the fiscal Times's recent shuttering of the highly innovative Newssift.com [site, a fiscal advice portal featuring faceted navigation and document-level sentiment analysis].

    If the text analytics industry is going to procure itself out of the sentiment-scoring corner it has painted itself into, compelling applications... beget to be developed, deployed, used, and publicized. Here is a fascinating statistic from one of their clients: At one big IT products and services firm, there are fewer than 50 staff members in the analyst relations/marketing communications department that might need sentiment scoring support. This selfsame arduous has 70,000 users in marketing, sales, competitive intelligence, strategic planning, product development, and technology research that can utter benefit from text analytics applications applied to their needs.

    More sentiment analysis – I warned you – from Mattias Tyrberg, founder and CEO of Saplo, a Swedish start-up, but with a search-oriented twist:I believe that next year will focus on more advanced and personalized existent time filters. The increased amount of information makes it impossible to read everything, but users conclude not want to miss anything attractive or important. The filters needs to be personalized because it's only then you can confidence them and deliver time by not reading utter texts.

    In the selfsame way, sentiment analysis needs to be developed to be able to adapt to each individual’s (or company’s) opinions. I believe that in 2010 they will be able to employ better and more accurate sentiment analysis models that learn and adapt from users.

    Market Growth Lexalytics CEO Jeff Catlin affirms other respondents’ themes. He sees search as a particular growth zone and makes other points regarding text-analytics market growth:
  • Text analytics [TA] will become a mainstream feature set in enterprise search applications (though not by name). We’ve seen a equable march toward this in 2009, and it’s most notable in how accepted TA features are by the common public. When I’m at a party now and uncover someone what they do, they insist “Oh yeah, I read something about that sort of stuff ultimate month” as opposed to the “Huh???” that I used to get. The outcome of this is that there are a lot more opportunities for TA in enterprise applications, and I suspect it will carry weight that one or two of the players may procure picked up by a considerable company.
  • Sentiment will complete its transition to a “checklist” feature that everyone who works in this space will beget to provide. utter of the vendors (big and small) will pretense to beget sentiment. The consumers of this technology will besides procure a bit more educated – we’re seeing this in RFP requests for particular capabilities of sentiment – which will wait on part the wheat from the chaff. Unfortunately for us, sentiment won’t be a totally differentiating feature that you can hang a traffic on anymore, as there will be lots of competition on the sentiment front.
  • The [differentiation between] larger TA players and the niche players will become even more obvious. The bigger players will integrate a number of useful and useable semantic features into their engines which will wait on with things love ad hoc classification, concept roll-up, and relationship [extraction].
  • On the traffic side, they hope 2010 to be a “Home Run” year for utter the TA vendors with growth rates of 75% to 200% not out of the norm. This is partly due to the mainstreaming of the technology, which is opening up a lot of additional verticals.
  • Technical Matters Of utter my respondents, only Manya Mayes, SAS chief text Mining strategist, chose to respond with technical challenges. I’m going to conclude that Manya is quite confident about SAS’s competence to compete, hence her concern with further improving solution capabilities. According to Manya:
  • Handling data (or at least text) volumes is increasingly important. Semi-supervised learning could carry weight that rather than letting the model becoming dated and having to retrain the entire model, the current data could be added incrementally to the training data without requiring an entire rerun.
  • Text mining/text analytics will become captious enabling technologies to solutions such as risk management and customer intelligence.
  • Text cleansing is more primary than ever with Web data being so messy (acronyms, synonyms, misspellings etc.).
  • An Ecosystem Finally, responses from Sid Banerjee, CEO of Clarabridge, who relayed his thoughts and then expanded on them a bit in his blog. Sid’s third point, about a ally ecosystem, is particularly attractive and matches something I’ve observed myself as a market observer. Sid’s responses:
  • Social media analysis will strain from "fad" to "function": Sentiment analysis is key, and it's finally Good enough to find existent insights... social media [provides] a captious early-warning (before a customer buys) and customer-support (when a customer is having issues) indicator of customer experience… There are stakeholders in the Fortune 1000 now, with budget and interest, that didn't exist 2 years ago.
  • Enterprise expansion of text analytics will drive… application requirements: Systems will scale to support multi-terabyte solutions, enterprise security, [and] authentication, shielding [personal] data… Capital expenditure budgets will grow and more customers will initiate deploying in-house text analytics solutions (though SaaS deployments will besides continue to grow).
  • Text Analytics will FINALLY be embraced by a rich ally ecosystem in 2010. In 2009 there was some ally activity (text analytics vendors partnering with marketing services providers, database platform vendors selling text analytics solutions), but partnership activity did not drive lofty revenue growth for either side... They hope to observe strong and productive partnerships developing among text analytics vendors and data warehousing, CRM, and muster center [vendors] and systems integrators.
  • Learning More I’ll conclude with a suggestion how you can learn more, whether you’re a current or experienced user or still evaluating solutions, whether your role is in traffic or in information technology. For the broad set of text-analytics technologies and solutions, there’s no better venue than the Text Analytics Summit. This year’s summit, the 6th Boston summit, is slated for May 25-26. I hope to reprise my role as chair.

    And given violent interest in sentiment analysis – in opinions, attitudes, mood, and other subjective information in social media, news, and enterprise feedback – I’ve organized a current conference, the 2010 Sentiment Analysis Symposium, April 13 in current York. The symposium will beget a practical, solutions focus, bridging technology and traffic concerns.

    I hope to observe many readers at these events. Whether you can profile it or not, if you’re a current or prospective text-analytics user and would love to uncover me about challenges (and opportunities) you countenance or hope to countenance – about your text-analytics plans or experiences – gratify conclude procure in finger (grimes@altaplana.com or 301-270-0795).

  • Seth GrimesSeth Grimes

    Seth is a traffic intelligence and conclusion systems expert. He is founding chair of the Text Analytics apex and principal consultant at Washington, D.C., based Alta Plana Corporation. Seth consults, writes, and speaks on information-systems strategy, data management and analysis systems, IT industry trends, and emerging analytical technologies. Seth chairs the Sentiment Analysis Symposium and the Text Analytics Summit.

    Editor’s Note: More articles and resources are available in Seth's BeyeNETWORK Expert Channel. be positive to visit today!

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