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M2020-229 | IBM SPSS Predictive Analytics Sales Mastery v1

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M2020-229 - IBM SPSS Predictive Analytics Sales Mastery v1 - braindump

Vendor IBM
Exam Number M2020-229
Exam Name IBM SPSS Predictive Analytics Sales Mastery v1
Questions 45 Q & A
Recent Update October 12, 2018
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M2020-229 exam Dumps Source : IBM SPSS Predictive Analytics Sales Mastery v1

Test Code : M2020-229
Test Name : IBM SPSS Predictive Analytics Sales Mastery v1
Vendor Name : IBM
Q&A : 45 Real Questions

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IBM IBM SPSS Predictive Analytics

Predictive Analytics in 2018: What’s possible, Who’s Doing It, and how | killexams.com Real Questions and Pass4sure dumps

In 2009, Netflix offered $1 million to any individual who might increase the best of its advice engine by using 10%. It took two years, but a team at last won. Netflix paid the bounty—then left out the code.

as it became out, the more suitable algorithms “didn't seem to justify the engineering effort necessary to bring them right into a construction ambiance.”

not best did the profitable prediction engine fail to scale economically, it additionally addressed an old-fashioned problem: The shift from mail to streaming all the way through that equal two-12 months window gave Netflix all of the statistics it crucial to improve newer, more advantageous algorithms.

Predictive analytics, in different phrases, wasn’t a panacea. Nor, within the decade due to the fact that, has it develop into one. however, in 2018, incremental positive aspects not cost $1 million either:

  • you've got more facts;
  • Storage is low-cost; and
  • Cloud computing is essentially infinitely scalable.
  • This post details these changes and suggests how a couple of companies—and never simply behemoths—have cultivated the predictive analytics landscape.

    What’s changed within the remaining decade? 1. more facts, extra storage, greater computing energy

    huge, cloud-primarily based repositories of customer interactions, commonly known as facts lakes, are the uncooked supply material for predictive analytics purposes.

    Many agencies have taken knowledge of inexpensive cloud storage to stow away records for years—devoid of even because its abilities use. (what number of omitted records facets do you have got in Google Analytics, Google ads, MailChimp, Marchex, Stripe, and equivalent services?)

    That twin increase in scale—of statistics accrued and accessibility to it—has solved two fundamental challenges of predictive analytics implementation.

    historically, raw computing vigour has been the other. As Andrew Pearson of Intelligencia notes, “devoid of gigantic hardware investments, predictive analytics courses either weren’t feasible or too sluggish to be constructive.”

    That, Pearson endured, has also changed: “Cloud-based analytics programs have added large laptop vigour into the combine.” increasingly potent techniques cracked open the door for actual-time predictive analytics.

    2. a world of precise-time predictions

    For some, the age of “precise-time” predictive analytics is right here. Judah Phillips, the co-founder and CTO of Vizadata and founder of SmartCurrent, explained:

    We already are living in an international of “precise-time” predictive analytics. an easy predictive analysis is your arrival time in Waze. A extra complicated precise-time prediction happens billions of times international every millisecond in matching definite sorts of digital advertising.

    additional, corporations like Mintigo and Versium now offer actual-time solutions for lead scoring, displaying that the transition is technically possible. viable, despite the fact, doesn’t imply superb. Sam Underwood, a vice chairman at Futurety, acknowledged the complexity of crucial integrations:

    chiefly in the mid-market world, the tools that accumulate statistics to develop into predictive modeling—CRM systems, social media aggregators, logistics, and purchasing systems—often wouldn't have pleasant APIs or other handy mechanisms with which to instantly collect and interpret information.

    That disconnect nonetheless thwarts even essentially the most basic enterprise cases for real-time predictive analytics. David Longstreet, the manager information scientist at FanThreeSixty, offered an example:

    In our world of activities and enjoyment, for instance, most activities groups don't know what number of americans are in a stadium for a game. groups comprehend what number of tickets had been distributed; although, they have no idea in “actual time” how many individuals are in the venue or stadium all the way through the experience.

    That competencies gap hampers efforts to group of workers and stock the stadium accurately. It’s additionally why interest in predictive analytics is practically familiar, although it vastly outpaces adoption.

    three. sluggish adoption but hovering interest

    So how many agencies are actively using predictive analytics? in accordance with analysis from Dresner Advisory services, about 23%, a determine just about unchanged from the prior 12 months.

    predictive analytics adoption

    lower than 1 / 4 of corporations are the usage of predictive analytics—notwithstanding almost all aspire to achieve this. (photo source)

    pastime, besides the fact that children, exceeds implementation. The identical analysis suggests that ninety% of organizations “attach, at minimum, some importance to superior and predictive analytics.”

    So which questions are these 23% answering with predictive analytics? Let’s take a glance.

    Which questions can entrepreneurs reply with predictive analytics?

    “They are looking to predict every thing,” in keeping with Underwood. And who wouldn’t wish to understand the accurate foot (or web) site visitors via month, day, and hour to streamline staffing (or allocate server supplies)?

    but, Underwood persevered, he tries to center of attention shoppers on “the one issue that, if we may predict it for you, would revolutionize your business.”

    In digital advertising, Phillips outlined myriad use circumstances for predictive analytics, together with the capability to predict:

  • which advertising should be most beneficial—although you outline positive.
  • which advertising campaigns, channels, touches, behaviors, and demographics are contributing to a enterprise influence, a kind of “desktop studying–based attribution.”
  • which segment, test, or personalization a consumer is certainly to respond to.
  • the likelihood of users to click on an ad, to down load a whitepaper, to respond to an e mail, to reply to a proposal, and different client response you define.
  • which leads will convert—despite the fact you define conversion.
  • which clients will purchase one or extra items for a cross-sell or upsell.
  • the variety of purchases or profits with a purpose to occur in the future.
  • which valued clientele could have excessive/medium/low lifetime value.
  • consumer churn.
  • the novel chance of predictive analytics, then, isn't what you could predict however the proven fact that you can predict. The old records you currently analyze can probably turn into a prediction.

    just be sure you have the records.

    What do you should get all started with predictive analytics?

    data, data, and data. “precedence 1A and 1B are information sources,” brought up Underwood. That’s proper whether you intend to license application or hire an outside organization. (each options are specific later.)

    All uses require practicing data. That practising records, in turn, is used to construct a predictive mannequin to apply to existing statistics. “The handiest challenge we’ve run into,” Phillips noted, “is an organization’s attainable facts for working towards.”

    How an awful lot facts is sufficient? in keeping with Phillips:

    a few thousand statistics with a sufficient amount of high quality and poor results can also be sufficient for advertising, revenue, and product prediction.

    no longer all facts is created (or kept) equally

    “You have to keep in mind—I grew up tearing tickets.”

    FanThreeSixty’s Longstreet has heard that same explanation from venue managers who have spent numerous hours counting stacks of stubs after games. It’s a explanation why vital statistics sources can also no longer be quite simply obtainable, or purchasable at all.

    In stadiums, Longstreet defined, element-of-sale machines and ticket scanners exist for a single goal—to comprehensive transactions at once and keep traces moving. those systems don't keep statistics efficaciously for extraction, nor can they deal with incessant server requests (except hungry fanatics don’t mind waiting).

    For Underwood, consumers are inclined to fall into one of two buckets, with half in each:

  • “The choicest customer has an interior database installation and able to go. We pull within the data, construct the model, and are off and running.”
  • The other half have a mixture of information sources, which inevitably include an offshore SQL database (or ten) managed via an external seller whom nobody can song down.
  • Stitching data sources together is a big construction assignment that might also require developing customized connectors, developing third-birthday party FTP drops, and other complicated however thankless initiatives. That work, besides the fact that children, is vital: models and their predictions are only as accurate because the information they’re developed upon.

    Don’t overlook exterior statistics sources

    not all information comes from inside sources, both. external records sources, like climate experiences, are sometimes a crucial addition to information lakes, specially for small companies. As Underwood explained:

    restaurants might also use analytics to trigger e-mail sends; for example, we can install the e-mail platform to sync with countrywide weather carrier information to ship an e mail about iced tea when the temperature in a given metro enviornment is above ninety levels.

    Likewise, we will trigger an email to ship to shoppers in a given city if the equipment detects wind gusts of forty+ MPH. both of these use cases attain consumers in a key moment of want, negating downstream ad spend and beating opponents to the punch.

    so you have a large, well-organized dataset. What do you do with it?

    How do you turn statistics into predictions?

    whereas the challenge of inadequate statistics has faded, another continues to be:

    groups require either a committed team of statistics scientists to parse through these units, or a utility suite powerful enough to achieve this all of a sudden. for many small and medium-sized groups, this always means settling for subpar software, or forgoing it fully.

    For businesses of all sizes, options department into two alternate options:

  • buy software and create predictions in-residence.
  • Pay an outdoor dealer to improve fashions and visualizations for you.
  • 1. Predictive analytics application

    The marketplace for predictive analytics application has ballooned: G2Crowd records ninety two effects within the category. Pricing varies radically in response to the variety of clients and, in some instances, volume of facts, but often begins around $1,000 per 12 months, even though it may well without problems scale into six figures.

    G2Crowd lists both IBM’s SPSS facts and SAS’s superior Analytics as market leaders on the business stage. along with RStudio, the pair are also tagged as leaders for mid-market groups; most effective IBM retains a spot in the “Leaders” quadrant for small corporations.

    historically, despite the fact, even business-main predictive analytics software hasn’t been a simple, start-right-in event. Take these two examples from IBM’s SPSS statistics and RapidMiner:

    ibm spss statistics screenshot

    rapid miner screenshot

    while these platforms are effective, clients need to structure data data, link nodes, and boost visualizations. studying the way to do this—and having the time to do it—is a really good, full-time job. (To agree with in any other case is to expect a Microsoft notice license to write down your brilliant American Novel.)

    not rather, the market is transferring. RapidMiner has rolled out a SaaS beta that, with just a little of manual adjustment, translates an Excel sheet filled with, say, employment information to a prediction of employee retention:

    rapid miner saas excel sheet

    A dataset in Excel—a place to begin through which most entrepreneurs are already relaxed. rapid miner intake

    RapidMiner parses the Excel file just before crunching the numbers. rapidminer predictive visualization

    The ensuing visualization shows the device’s prediction efficiency and correlations between datasets and retention.

    Some agencies, like Vizadata’s Phillips, see the person-pleasant SaaS model as the future:

    we're democratizing statistics science, in order that people with confined or no information science or engineering talents can predict. You comfortably add your records and click on subsequent. We do all of the heavy lifting.

    Our intelligence determines your based and impartial variables and the type of analysis to run. you can go together with our decisions or override them—from regression, the place we will do forecasting and optimization, to each binary and multiclass classification, the place we are able to predict the chance of effects.

    vizadata screenshot

    user-pleasant SaaS models make predictive analytics extra accessible to marketing groups devoid of information scientists.

    Like Vizadata, MIT’s Endor pursues this direction. The platform uses a question-builder to allow anybody to ask questions like “the place should still we open our next keep?” or “Who is likely to try product X?” It then mines targeted datasets to deliver answers, frequently in a depend of minutes.

    The inclusion of tangential datasets that fall backyard consideration—or feasibility—for human observers is a recurring expertise of predictive analytics. Endor’s creators offer an example:

    A advertising department for a financial institution asks, “Who is going to get a personal loan within the next six months?” computing device-getting to know engines might also detect a pool of, say, 5,000 consumers who've a financial institution credit card and a high credit score score, and are married—many of which can be false positives.

    Endor detects more selected clusters of, say, couples about to get married or going through a divorce, founders who currently sold their startups to facebook, or valued clientele who lately graduated from a native real-estate direction.

    Of route, if you are looking to outsource the system thoroughly, backyard vendors can prepare your facts, construct fashions, and visualize predictions for you.

    2. backyard proprietorsagencies offering bespoke solutions

    for many clients, Futurety starts by means of deciding upon the important thing business question—no longer a specific metric or visualization. valued clientele may are available for one-off tasks, annual re-runs of their statistics, or ongoing work.

    recommended for You

    Webcast, October 17th: how to resolve Your marketing statistics Nightmare

    “The fruits isn't always clear at first,” Underwood explained. “once we’re providing to somebody close to the effect, like a marketing manager, they’re customarily satisfied with the model, the finding, and the math at the back of it.”

    The “fruits” can be a couple of things:

  • Integration with a 3rd-celebration platform, like an e mail customer, to automate predictive messaging.
  • plain-text predictive solutions to ebook practitioners.
  • effective visualizations to show the process and cost to the C-Suite.
  • on the conclusion of each and every engagement, Futurety delivers the model again to the client for management and upkeep.

    Predictive analytics at work
  • Futurety has a small business customer that helps aspiring performing arts majors profit admission to their dream college. however few high schoolers have vast advantage of decent courses. greater regularly than not, they comprehend just one identify: Julliard.
  • Futurety informed its mannequin on three years of placement data. Then, the use of new pupil records entered into a typical portal, envisioned where students would get authorised and prevail academically.
  • The predictive analytics mannequin, which Futurety updates annually, grants an easy checklist of suggested colleges for college kids in keeping with factors like grades and exposure to different musical or artistic patterns.
  • The mannequin takes into consideration whether previous placements graduated or received awards.
  • All-in-one niche providers

    FanThreeSixty serves a slender market: wearing venues. because they work with a comparatively constant dataset—season ticket, concession, and keepsake income—they comprehend the latitude of enterprise questions, statistics results, and primary visualizations.

    This consistency incentivizes niche companies like FanThreeSixty to improve proprietary dashboards to roll out to all consumers.

    The interface allows Longstreet’s crew to hold information science within the history: “the secret of computing device researching is if you happen to’re being brought on behind the scenes.”

    Distilled thoroughly, FanThreeSixty’s goal (and Longstreet’s rationalization of his function at dinner parties) is to “support groups promote greater tickets and hot dogs.”

    Predictive analytics at work
  • FanThreeSixty mines ancient data to see which concessions are most commonly purchased with a sizzling dog at an enormous League Soccer venue.
  • If a client purchases a sizzling dog, concession staff are triggered to ask even if a consumer would like to add essentially the most widespread accompaniment. That suggestion—a prediction of fan want—adjustments based on other variables.
  • Predictions consider greater than 20 datasets—everything from the domestic region of season ticket holders to the climate—to tailor messaging before, during, and after suits.
  • all over bloodless-climate games, for instance, FanThreeSixty can automate push notifications with tailor-made coupons, like buy-three-get-one-free scorching chocolate for a family unit of four.
  • whether options are internally or externally managed, they‘ve lengthy been common in commercial enterprise groups.

    Predictive analytics use situations on the enterprise level

    marketing departments in tremendous businesses have used predictive analytics for years:

  • AutoTrader. AutoTrader uses information from its 40 million monthly friends to more suitable consider the sometimes prolonged customer event. They developed propensity models in line with search conduct and created excessive-cost lookalike audiences.
  • Editialis. The French writer uses predictive analytics in its e-mail campaigns to “assume engagement at someone degree.” consequently, they’ve viewed click on-via-quotes raise “dramatically.”
  • Predictive analytics can also coordinate offline and online interactions, with two clear use circumstances for marketers whose corporations have physical products or storefronts:

  • more suitable pricing. Smartphone records registers in-store shopping habits to enhance online or offline marketing concentrated on, approximating the merits enjoyed by way of ecommerce corporations.
  • stock administration. Full warehouses cost cash; empty cabinets can charge cash. Folding online statistics, such as search patterns, into income statistics can more suitable control stock, particularly at a regional and native stage.
  • apart from exterior marketing campaigns, predictive analytics additionally helps inside mission management. huge advertising campaigns have many moving materials—a brand new advert crusade wants new artistic, new copywriting, new landing pages, and many others.

    Coordinating the involvement of these teams and accurately estimating the time-to-launch is complex. Many fail to get it right, every now and then at great expense.

    whiteboard planning

    entrepreneurs and application companies may also use predictive analytics for inside project management besides exterior campaigns.

    Predictive algorithms, as McKinsey notes, use a wider lens that captures ancient patterns and wonderful task aspects in a single frame:

    while every development task is exciting, the underlying complexity drivers throughout projects are equivalent and may be quantified. If agencies remember the complexity involved in a brand new undertaking, they can estimate the effort and substances required to finished it.

    Predictive analytics models “have in mind not simplest the complexity of the assignment (each the purposeful and implementation points) however also the complexity of the group atmosphere.”

    Predictive analytics at work:

    extra correct internal project management, in an instance McKinsey offers, can have an enormous have an impact on:

  • an organization initially deliberate a product replace to take roughly 300 grownup-weeks of effort, an estimate in response to the confined number of adjustments between the existing product and a new design.
  • youngsters, that estimate did not take note of the proven fact that deliberate updates would affect various groups. Predictive analytics fashions did take it into account and estimated that the mission would take three to four times as long.
  • because of this, the company constrained the work to the common product team, enabling them to bring the update on time.
  • apart from assisting agencies remedy interior and exterior challenges, predictive analytics is also the basis for some companies.

    building a business on predictive analytics

    Ken Lazarus, CEO of the recruiting platform Scout change, has an competencies—the enterprise has been around for less than five years.

    That skill that the business’s statistics sources are already primed for extraction into its predictive models that pair businesses with the correct recruiter.

    scout exchange processScout change’s predictions pair businesses with the correct recruiter.

    the one finest predictor of job placement, Lazarus and his team have discovered, is the track record of job recruiters. In distinction, pairing the right job description with the right resume is still enormously problematic.

    “Job specs are horrible,” he lamented. “The information isn’t on the paper. CVs are pretty horrible, too.” (statistics augmentation, comparable to abilities trying out and video interview decoding, Lazarus referred to, offer knowledge advancements.)

    nonetheless, holes remain. Candidates will on no account reveal negatives on their resume, and significant information might invariably stay “non-statistics,” such as no matter if a candidate is a superb “tradition fit.”

    Scaling information gathering

    Scout alternate has honed its predictions with the aid of specializing in enterprise purchasers—its algorithms feast on a whole bunch or lots of openings from Fortune 500 purchasers.

    subsequently, the platform takes in roughly 1 million statistics elements month-to-month, with every new job posting yielding an additional 50 information points.

    scout exchange platform

    better business enterprise scores point out an company is extra responsive than his or her friends. Likewise, greater recruiter rankings suggest a recruiter is extra likely to prevail in submitting ideal candidates than his or her friends.

    nevertheless, human evaluation by using a recruiter—and their customer—is vital. Lazarus drew a parallel: “Would you let laptop discovering opt for your spouse? No. but would you let it decide on the right matchmaker to assist you find a companion? yes.”

    those that try to remedy essentially the most complicated human concerns aren’t even within the business world.

    Predictive analytics with life or dying consequences

    The optimum challenges for predictive analytics are people that take care of complicated, individualized human behavior, such because the chance that a affected person or disaster-line texter will commit suicide.

    as a result of success or failure is measured in human lives, these challenges are additionally essentially the most urgent. And while these tasks operate past the scope of advertising and marketing and enterprise, they suggest the competencies for predictive analytics as it evolves.

    “reach VET isn't about trying to locate the veteran who’s sitting within the automobile in a parking zone with a gun in his lap,” Aaron Eagan, Veteran Affairs deputy director for innovation instructed a Washington convention.

    “What we discovered,” Eagan continued, “is that veterans at maximum chance of suicide [also have] drastically multiplied charges of all-trigger mortality, accident morality, overdoses, violence, [and] opioids.” Proactive signals that trigger medical professional verify-ins have greater fundamental-care appointment attendance and decreased health center admissions for mental health issues.

    crisis text line

    The challenge is comparable to a collaboration between Periscope facts and disaster textual content Line, a text-primarily based suicide hotline.

    Leaning on natural language processing and predictive analytics, the program analyzed conversations, forecasted developments, and proficient greater than 13,000 volunteers. The outcomes?

  • Wait instances reduced to under 5 minutes, an operational aim.
  • ability accelerated with the aid of 10% right through top periods.
  • Responses had been prioritized in line with computer-recognized urgency.
  • Endor’s expertise has taken on in a similar way critical challenges. using 15 million information points from 50 favourite ISIS supporters, Endor recognized eighty lookalike debts in less than half an hour, with handiest 35 false positives—knowledgeable investigation changed into nevertheless vital yet feasible.

    In a collaborative undertaking with the U.S. defense superior analysis project agency, the platform additionally analyzed cellular facts to determine patterns to foretell future riots.

    Conclusion

    Predictive analytics is not immune to criticism: GDPR rebuffs probably the most equal collection methods that swell records lakes. and not all predictions, even the most correct, are well-obtained. (Famously, target unwittingly informed a father of his teenage daughter’s being pregnant in line with reputedly benign searching habits.)

    Predictive analytics experts aspect out that their algorithms seek patterns among values, now not the values themselves. Regardless, insufficient information is unlikely to dangle back the enlargement of the business—the IoT, wearables, and different information collectors already complement natural net and app analytics.

    user-friendly SaaS systems are nonetheless an rising probability. for many organizations, developing models and predictions from historic statistics nonetheless requires a dedicated worker to navigate complex application options or the outsourcing of that work to a vendor.

    For those suspending predictive analytics initiatives until the SaaS alternate options are more mature, you can be wise to keep filling your records lake.


    BMW the usage of IBM Predictive Analytics in Auto production, Repairs | killexams.com Real Questions and Pass4sure dumps

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

    IBM nowadays introduced that it's enhancing several of its existing application products with the Apache Spark open-source information processing engine, including the SPSS predictive analytics application.

    IBM purchased SPSS for $1.2 billion in 2009. SPSS itself begun at Stanford tuition in 1968 and is accepted in statistics courses at universities.

    IBM is especially incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman advised VentureBeat in an electronic mail.

    Spark is also being applied to IBM’s BigInsights, Streams, and DataWorks utility, in keeping with an announcement.

    The Sparkification of IBM comes after massive Blue introduced a huge commitment to the open-source software in June. on the time, IBM announced Spark as a provider on its Bluemix cloud; that service is now generally obtainable.

    IBM has made more than 60 contributions to Spark because the June announcement, in line with the remark.




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    Teradata dabbles a bit in the big-data arena with its partnership with Cloudera, a peddler of a commercially supported and extended version of the Apache Hadoop MapReduce and distributed file system that mimics an earlier style of computing done by search engine giant Google.

    However, for the moment, Teradata's data warehousing systems – be they large-scale Enterprise Data Warehouses or smaller appliances with various names – chew on structured data stored in the company's eponymous distributed, clustered database-management system, and are generating a widening stream of revenue.

    In the second quarter ended in June, Teradata's revenues were up 24 per cent to $581m, accelerating from the first quarter. Net income was up 39 per cent to $103m, and Teradata is bringing 18 per cent of revenues to the bottom line, which is not bad for a hardware company.

    Product sales rose by 21 per cent to $269m, and in a conference call with Wall Street analysts on Thursday, Teradata president and CEO Mike Koehler said that during the first half of the year, the company booked the highest number of new customers that it has seen in the past decade, with companies in the financial services and retailing businesses driving a lot of the revenue across both new and existing customers. Teradata doesn't break out product sales by line, but Koehler said that Teradata sold a "significant" number of its hybrid disk-flash appliances in the quarter.

    Maintenance services were up by 17 per cent to $135m, while consulting services rose a very decent 34 per cent to $177m. New account sales are, in part, driving those consulting sales.

    "In a new account, there is a higher level of consulting services in the mix compared to the hardware sale, versus an upgrade in an existing account," explained Koehler. He added that Teradata was building up its consulting practice and was seeing more companies coming to it to sort out their data-analytics needs because of its expertise, broader product line, and experience. Overall, services revenues in the quarter were up 26 per cent to $312m.

    Aprimo, a Web-based marketing services company that Teradata acquired in December 2010 for $550m and closed in April, contributed a little to the kitty this time around. It's also on track to contribute more, thanks to cross-selling, in the future. Koehler said that bookings for Aprimo were up 30 per cent year-on-year in the wake of the acquisition, and for the year, Aprimo is expected to add about 3 per cent to Teradata's top line.

    Aster Data, which created an SQL-MapReduce hybrid and which Teradata bought in March of this year for $263m, is not expected to add much to the top line this year, but Teradata has high hopes for it.

    The one thing everyone is trying to figure out right now is how big the "big data" addressable market is – and guess what? Teradata doesn't know, either. (Maybe they should have shelled out $1bn for SPSS for its predictive analytics, instead of IBM, back in July 2009.) "At this juncture, I don't think the market opportunity is clear, but it is large – extremely large," Koehler said in the call.

    What about competitive pressures, with Oracle and IBM being very aggressive in data warehousing? What did Teradata see in Q2? "Other than some advertisements, not a lot," Koehler said with a laugh.

    "I have never seen our competitive position so strong. Our competitive win rate really started to kick in a year and a half ago. Since Netezza [now part of IBM] entered the market three years ago, our win rate has gone up. We compete more with Oracle, but we really can't find much Exadata in our accounts. The only thing we can figure is they are mainly used with OLTP."

    This is what Teradata has been saying for several quarters, and Oracle doesn't provide specifics about its Exadata machines – which can in fact be used to support data warehousing or OLTP workloads, unlike Teradata and Netezza iron, which only does data warehousing.

    Teradata had planned to add 30 new sales regions this year, focusing on specific industry verticals, mid-market customers, and growing and emerging markets in Europe and Asia, and as of the end of June, all of these were up and running. Koehler said that Teradata would therefore add another 30 regions to help push sales. The added 30 regions have already contributed more than $50m in revenues during the first half of 2011. These regions take three or four years to hit their full yield level, Teradata says.

    Because of the need for more data analytics and interest in big-data munching, Teradata is raising its revenue guidance for 2001 by 4 per cent, and now expects sales to rise by between 18 and 20 per cent. The company also raised earnings-per-share guidance by a few pennies, to between $2.20 and $2.28 per share. The expansion in those additional 30 territories will ding profits for a bit, but will eventually yield a stronger bottom line over the long haul, says Teradata.

    As El Reg goes to press, the Dow Jones Industrial Average is down 4.3 per cent, – 512.5 points – but Teradata is only off a fraction of a per cent because of its good numbers and raised guidance. It was one of four companies that actually had a stock that was up – initially by 6 per cent early in the day – in the S&P 500 during a trading day that deteriorated as the day wore on because of nervousness about the global economy.

    Even with that tiny haircut, Teradata is way too pricey at a market capitalization of over $9bn for anyone to consider buying. Spinning out Teradata might just go down as one of the dumbest things NCR has ever done. ®



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