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00M-639 IBM Big Data Sales Mastery Test v1

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00M-639 exam Dumps Source : IBM Big Data Sales Mastery Test v1

Test Code : 00M-639
Test denomination : IBM Big Data Sales Mastery Test v1
Vendor denomination : IBM
: 51 real Questions

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IBM IBM Big Data Sales

$18.seventy seven Billion in income anticipated for IBM (IBM) This Quarter | killexams.com real Questions and Pass4sure dumps

Brokerages prognosticate that IBM (NYSE:IBM) will report $18.77 billion in earnings for the existing fiscal quarter, in response to Zacks. five analysts possess issued estimates for IBM’s profits, with estimates ranging from $18.forty three billion to $19.26 billion. IBM posted income of $19.07 billion within the identical quarter final yr, which implies a negative year over 12 months boom cost of 1.6%. The company is scheduled to file its subsequent salary effects on Tuesday, April sixteenth.

in accordance with Zacks, analysts prognosticate that IBM will record full-year income of $78.31 billion for the current fiscal 12 months, with estimates ranging from $76.85 billion to $eighty.70 billion. For the next fiscal 12 months, analysts prognosticate that the enterprise will report sales of $78.09 billion, with estimates starting from $seventy seven.02 billion to $seventy nine.65 billion. Zacks’ revenue averages are an middling commonplace based on a survey of sell-facet analysts that cowl IBM.

IBM (NYSE:IBM) final posted its quarterly profits statistics on Tuesday, January 22nd. The know-how trade suggested $4.87 profits per participate (EPS) for the quarter, beating the consensus rate of $four.eighty two through $0.05. The trade had income of $21.seventy six billion during the quarter, in comparison to analysts’ expectations of $21.seventy nine billion. IBM had a web margin of 10.ninety seven% and a revert on equity of 68.sixty one%. The business’s income became down three.5% on a yr-over-yr basis. throughout the identical term final year, the arduous posted $5.14 income per share.

IBM has been the topic of a few fresh analysis stories. Wedbush reduce their target expense on shares of IBM from $185.00 to $a hundred sixty five.00 and set a “impartial” rating for the company in a research note on Thursday, October 18th. Zacks investment research raised shares of IBM from a “promote” rating to a “cling” score in a research live vigilant on Thursday, October 18th. ValuEngine raised shares of IBM from a “promote” rating to a “hold” ranking in a research live vigilant on Wednesday. Goldman Sachs community restated a “neutral” ranking and issued a $155.00 cost goal on shares of IBM in a analysis record on Monday, October twenty ninth. eventually, BMO Capital Markets restated a “dangle” ranking and issued a $one hundred forty five.00 charge goal on shares of IBM in a analysis file on Friday, December seventh. Three funding analysts possess rated the inventory with a sell score, eleven possess issued a grasp ranking and eight possess issued a buy ranking to the company. IBM privilege now has a consensus ranking of “dangle” and a consensus goal rate of $154.56.

IBM stock traded down $0.26 on Monday, hitting $137.27. 1,202,955 shares of the enterprise’s stock traded fingers, compared to its usual volume of 5,224,408. IBM has a 1-12 months low of $105.ninety four and a 1-year tall of $162.eleven. The enterprise has a market cap of $124.98 billion, a PE ratio of 9.94, a P/E/G ratio of 2.37 and a beta of 1.25. The enterprise has a debt-to-fairness ratio of 2.10, a present ratio of 1.29 and a brief ratio of 1.24.

The enterprise additionally lately declared a quarterly dividend, which may live paid on Saturday, March 9th. investors of checklist on Friday, February 8th should live given a $1.57 dividend. The ex-dividend date of this dividend is Thursday, February seventh. This represents a $6.28 annualized dividend and a dividend defer of 4.58%. IBM’s dividend payout ratio (DPR) is privilege now forty five.47%.

IBM introduced that its Board of directors has accepted a inventory buyback arrangement on Tuesday, October thirtieth that permits the company to repurchase $four.00 billion in shares. This repurchase authorization allows the expertise company to reacquire up to three.5% of its stock through open market purchases. inventory repurchase plans are often a demonstration that the enterprise’s board believes its shares are undervalued.

In other IBM news, insider Diane J. Gherson bought 5,754 shares of the company’s stock in a transaction that took status on Wednesday, February 6th. The shares possess been bought at a standard fee of $135.67, for a total charge of $780,645.18. Following the transaction, the insider now owns 23,117 shares in the enterprise, valued at about $3,136,283.39. The transaction become disclosed in a doc filed with the SEC, which can live accessed through this hyperlink. 0.17% of the inventory is at the instant owned via company insiders.

Institutional traders possess these days added to or decreased their stakes in the enterprise. Cozad Asset management Inc. multiplied its stake in IBM by means of 39.2% in the 4th quarter. Cozad Asset administration Inc. now owns 3,171 shares of the expertise business’s stock valued at $360,000 after purchasing an additional 893 shares bar no portion over the period. Albion fiscal community UT elevated its stake in IBM by 1.5% in the third quarter. Albion monetary neighborhood UT now owns 18,471 shares of the technology business’s stock valued at $2,793,000 after buying an extra 281 shares privilege through the length. Paloma companions administration Co improved its stake in IBM through 127.4% in the third quarter. Paloma companions administration Co now owns 1,453 shares of the expertise business’s stock valued at $220,000 after buying an additional 6,757 shares during the length. Crossvault Capital administration LLC elevated its stake in IBM by artery of 12.four% within the third quarter. Crossvault Capital administration LLC now owns 7,seven-hundred shares of the technology enterprise’s inventory valued at $1,164,000 after purchasing an extra 850 shares privilege through the length. at last, Edmp Inc. elevated its stake in IBM by using 2.3% within the 4th quarter. Edmp Inc. now owns eleven,032 shares of the technology business’s inventory valued at $1,254,000 after purchasing an extra 243 shares bar no portion over the length. Hedge funds and different institutional investors personal 61.97% of the company’s inventory.

IBM trade Profile

overseas enterprise Machines agency operates as an integrated technology and features trade global. Its Cognitive options segment presents Watson, a computing platform that interacts in language, strategies Big statistics, and learns from interactions with americans and computer systems. This section additionally presents records and analytics solutions, including analytics and statistics management platforms, cloud information services, trade gregarious utility, aptitude management solutions, and tailored trade solutions; and transaction processing application that runs mission-essential systems in banking, airlines, and retail industries.

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IBM Db2 question Optimization the exercise of AI | killexams.com real Questions and Pass4sure dumps

In September 2018, IBM announced a brand unique product, IBM Db2 AI for z/OS. This synthetic intelligence engine screens statistics entry patterns from executing SQL statements, uses computing device discovering algorithms to resolve on most profitable patterns and passes this suggestions to the Db2 query optimizer for exercise by artery of subsequent statements.

desktop getting to know on the IBM z Platform

In may of 2018, IBM announced version 1.2 of its computer researching for z/OS (MLz) product. here is a hybrid zServer and cloud application suite that ingests performance records, analyzes and builds models that symbolize the fitness popularity of numerous indicators, displays them over time and offers real-time scoring capabilities.

a couple of facets of this product offering are aimed toward helping a community of model developers and executives. as an instance:

  • It helps diverse programming languages equivalent to Python, Scala and R. This permits facts modelers and scientists to exercise a language with which they are general;
  • A graphical person interface known as the visual model Builder guides model developers without requiring totally-technical programming abilities;
  • It contains numerous dashboards for monitoring model effects and scoring features, in addition to controlling the device configuration.
  • This machine getting to know suite turned into at the beginning aimed toward zServer-based analytics applications. some of the first evident choices changed into zSystem performance monitoring and tuning. outfit management Facility (SMF) statistics that are immediately generated with the aid of the working gadget deliver the raw records for gadget resource consumption reminiscent of germane processor utilization, I/O processing, reminiscence paging etc. IBM MLz can compile and store these facts over time, and build and train models of system behavior, rating these behaviors, determine patterns no longer without hardship foreseen with the aid of humans, enlarge key efficiency warning signs (KPIs) and then feed the model results returned into the gadget to possess an upshot on gadget configuration adjustments that can enlarge performance.

    The subsequent step changed into to enforce this suite to investigate Db2 performance statistics. One solution, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the machine learning technology to Db2 operational records to profit an knowing of Db2 subsystem fitness. it can dynamically construct baselines for key performance warning signs, give a dashboard of those KPIs and give operational group of workers real-time insight into Db2 operations.

    while time-honored Db2 subsystem performance is a crucial aspect in ordinary utility fitness and performance, IBM estimates that the DBA aid staff spends 25% or more of its time, " ... fighting access direction issues which trigger efficiency degradation and repair influence.". (See Reference 1).

    AI comes to Db2

    trust the plight of coincident DBAs in a Db2 environment. In modern-day IT world they should guide one or extra large statistics purposes, cloud software and database functions, software setting up and configuration, Db2 subsystem and application performance tuning, database definition and administration, catastrophe recovery planning, and more. question tuning has been in actuality considering the origins of the database, and DBAs are continually tasked with this as neatly.

    The heart of query route evaluation in Db2 is the Optimizer. It accepts SQL statements from purposes, verifies authority to access the facts, studies the locations of the objects to live accessed and develops a listing of candidate statistics access paths. These access paths can include indexes, desk scans, quite a few desk live portion of methods and others. within the information warehouse and massive facts environments there are always further selections accessible. One of those is the actuality of summary tables (on occasion called materialized question tables) that comprise pre-summarized or aggregated records, accordingly allowing Db2 to preclude re-aggregation processing. another alternative is the starjoin access path, generic within the information warehouse, where the order of desk joins is modified for efficiency reasons.

    The Optimizer then stories the candidate entry paths and chooses the entry path, "with the bottom cost." freight in this context skill a weighted summation of aid usage including CPU, I/O, reminiscence and different substances. finally, the Optimizer takes the bottom can freight entry path, stores it in reminiscence (and, optionally, within the Db2 directory) and starts off entry course execution.

    big records and statistics warehouse operations now consist of application suites that enable the enterprise analyst to exercise a graphical interface to build and manipulate a miniature data mannequin of the records they are looking to analyze. The packages then generate SQL statements in keeping with the clients’ requests.

    The problem for the DBA

    to live able to carry out auspicious analytics for your diverse facts stores you need an outstanding realizing of the facts requirements, an figuring out of the analytical services and algorithms attainable and a high-performance statistics infrastructure. sadly, the quantity and location of data sources is expanding (each in measurement and in geography), records sizes are turning out to be, and applications proceed to proliferate in quantity and complexity. How should noiseless IT managers assist this ambiance, particularly with essentially the most skilled and mature team of workers nearing retirement?

    take into account additionally that a Big portion of decreasing the overall freight of ownership of these programs is to pick up Db2 functions to elope quicker and greater efficiently. This constantly translates into the usage of fewer CPU cycles, doing fewer I/Os and transporting much less information across the network. when you consider that it is regularly complicated to even identify which functions might improvement from performance tuning, one strategy is to automate the detection and correction of tuning issues. here is where desktop researching and synthetic intelligence can likewise live used to incredible effect.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 version 12 on z/OS uses the computer researching amenities outlined above to congregate and withhold SQL query textual content and entry course details, as well as genuine performance-linked archaic assistance similar to CPU time used, elapsed instances and upshot set sizes. This providing, described as Db2 AI for z/OS, analyzes and retailers the data in computing device researching fashions, with the mannequin evaluation outcomes then being scored and made available to the Db2 Optimizer. The subsequent time a scored SQL statement is encountered, the Optimizer can then exercise the mannequin scoring facts as input to its entry course option algorithm.

    The outcome may noiseless live a reduction in CPU consumption as the Optimizer makes exercise of model scoring input to select improved entry paths. This then lowers CPU costs and speeds application response instances. a Big talents is that using AI application does not require the DBA to possess information science scholarship or abysmal insights into query tuning methodologies. The Optimizer now chooses the most desirable entry paths primarily based not most efficacious on SQL question syntax and records distribution information but on modelled and scored historical efficiency.

    This will likewise live certainly vital if you reclaim data in discrete areas. for instance, many analytical queries in opposition t huge information require concurrent access to inevitable records warehouse tables. These tables are generally known as dimension tables, and that they include the data facets usually used to manage subsetting and aggregation. as an instance, in a retail environment believe a table known as StoreLocation that enumerates every shop and its region code. Queries against withhold earnings records may additionally are looking to combination or summarize earnings by vicinity; therefore, the StoreLocation table should live used via some Big records queries. during this ambiance it is usual to bewitch the dimension tables and duplicate them continually to the Big data software. within the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).

    Now suppose about SQL queries from each operational purposes, information warehouse users and Big records company analysts. From Db2's point of view, bar no portion these queries are equal, and are forwarded to the Optimizer. however, in the case of operational queries and warehouse queries they may noiseless absolutely live directed to access the StoreLocation desk within the warehouse. even so, the query from the trade analyst towards Big data tables should doubtless access the copy of the desk there. This results in a proliferations of edge entry paths, and more drudgery for the Optimizer. luckily, Db2 AI for z/OS can give the Optimizer the guidance it needs to design smart access path choices.

    how it Works

    The sequence of events in Db2 AI for z/OS (See Reference 2) is generally the following:

  • all over a bind, rebind, prepare or clarify operation, an SQL commentary is passed to the Optimizer;
  • The Optimizer chooses the data access route; as the altenative is made, Db2 AI captures the SQL syntax, entry course alternative and query performance data (CPU used, and so forth.) and passes it to a "learning assignment";
  • The researching assignment, which can live finished on a zIIP processor (a non-familiar-goal CPU core that does not factor into utility licensing costs), interfaces with the laptop getting to know software (MLz mannequin functions) to withhold this information in a mannequin;
  • because the volume of statistics in every model grows, the MLz Scoring service (which can likewise live achieved on a zIIP processor) analyzes the mannequin statistics and scores the habits;
  • all over the next bind, rebind, reserve together or explain, the Optimizer now has entry to the scoring for SQL models, and makes applicable adjustments to entry route decisions.
  • There are likewise numerous consumer interfaces that give the administrator visibility to the repute of the collected SQL statement performance statistics and mannequin scoring.

    summary

    IBM's laptop getting to know for zOS (MLz) offering is getting used to exquisite upshot in Db2 edition 12 to enlarge the efficiency of analytical queries as well as operational queries and their associated purposes. This requires management attention, as you possess to determine that your trade is prepared to consume these ML and AI conclusions. How will you measure the prices and advantages of the exercise of machine researching? Which IT aid staff ought to live tasked to reviewing the upshot of mannequin scoring, and perhaps approving (or overriding) the consequences? How will you evaluation and warrant the assumptions that the utility makes about access direction decisions?

    In different phrases, how well were you vigilant your statistics, its distribution, its integrity and your existing and proposed entry paths? this can determine the status the DBAs spend their time in aiding analytics and operational utility efficiency.

    # # #

    Reference 1

    John Campbell, IBM Db2 unique EngineerFrom "IBM Db2 AI for z/OS: enlarge IBM Db2 software efficiency with machine researching"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-boost-ibm-db2-utility-performance-with-ma

    Reference 2

    Db2 AI for z/OShttps://www.ibm.com/aid/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html

    See bar no portion articles via Lockwood Lyon


    Why IBM is having a stake massive on this unique huge records know-how | killexams.com real Questions and Pass4sure dumps

    IBM plans an even bigger shove into records crunching through opening a brand unique technology middle in San Francisco committed to a trendy know-how that’s making waves in Silicon Valley, Bloomberg information experiences.

    Rob Thomas, an IBM (IBM) vice chairman in can freight of huge records, pointed out in a web video seen with the aid of Bloomberg and later eliminated that the brand unique hub will at final condominium “hundreds of americans” working basically with a free expertise called Spark.

    Spark lets companies artery statistics more immediately than what is at the instant feasible the usage of an additional open-supply technology known as Hadoop, according to many analysts. among other things, groups exercise Spark for quickly evaluation of sales facts enjoy what number of department reclaim customers purchased a particular shirt.

    The expertise can drudgery with or change Hadoop, which has won traction in recent years with agencies enjoy Yahoo (YHOO) and facebook (FB) that exercise it to shop and artery massive amounts of records. enjoy with a lot of know-how, what’s inflamed in statistics crunching alterations quickly as unique utility emerges it truly is faster and simpler to use.

    It’s as a result of this velocity and skill to manner information to rapidly that has IBM excited. The a hundred-yr historic trade has been public with its profit for the technology and has claimed that it will likewise live used to boost the performance of Hadoop.

    IBM has made information evaluation a Big a portion of its earnings pitch, portion of which revolves around Watson, the robot that made an appearance on the Jeopardy tv video game demonstrate. In April, the enterprise launched its Watson health service that corporations can exercise to resolve healthcare facts.

    It’s dubious what IBM plans for Spark. however it may champion with making the underlying technologies behind Watson or equivalent features foster to lifestyles.

    by artery of helping Spark and attracting employees who know the artery to exercise the infrastructure technology, IBM can declare that it’s ahead of the pack in reducing-area technology.

    With its hardware earnings generating less profits than it they once did, IBM increasingly relying on unique know-how to revitalize its business. huge information technology may well live a much bigger a portion of the plan.

    For extra on IBM and large information, check out here Fortune video:


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    From Bootcamp to Mastery: A Five Year Journey | killexams.com real questions and Pass4sure dumps

    As I contemplate across the learn-to-code industry — with the proliferation of bootcamps, MOOCs, and alternative learning options — I often phenomenon why they (Launch School) are the only program that’s 100% mastery-based. There aren’t a lot of viable pedagogical options from which to choose, especially if the focus is on skills and results rather than credentialism. Yet, no one teaches in a mastery-based artery except us. As I thought more about this, I realized that we, too, started teaching programming in a typical “bootcamp” fashion, and it was due to a unique confluence of personal and trade factors that led us to focus on mastery-based learning.

    This is a memoir about how they built Launch School over the final 5 years and how their opinions around programming, teaching, and trade led us to a Mastery-based pedagogy.

    The Backstory

    I’ve known Kevin since 2002, when they were both software engineers at IBM. They had always talked about working on something together, but the opening never came up. Finally around 2012, they had a window of time where both of us were looking to carry out something new. They only knew that they wanted to drudgery on something together, but didn’t possess any concrete ideas. After months of observant deliberation, they decided to focus their thirties on Education.

    Of course as programmers, the first thing they set out to carry out was to build a revolutionary Learning Management System (LMS) that would cessation bar no portion LMSes. As they worked through the specifications and design, one thing became painfully obvious: they had no notion what they were doing because neither of us had any abysmal sustain with teaching or education. So naturally, before they could build a LMS, they had to pick up some sustain teaching real students. Now, I’d enjoy to contemplate that we’re both pretty well-rounded people with a lot of interests, but they both really only had one skill that could attract students: programming. Towards the cessation of 2012, they decided to give up their (extremely) tall paying jobs and try teaching people programming so they could better understand the problems around education and teaching (…so they could build an LMS to cessation bar no portion LMSes).

    I participate this backstory because this inception memoir will foster back to influence many of their later decisions. It’s essential to remember: they didn’t discern an opening to design money and came into teaching programming as an exercise in learning about how to educate people.

    Side Note: they quickly dropped the LMS notion because they create out students don’t buy LMSes, and selling a unique LMS to large organizations requires a skill that they weren’t interested in developing.

    2012–2013: Bootcamps

    Unbeknownst to us at the time, this was the golden era of learning to code. In an odd case of multiple-discovery, they started their teach-people-to-code exploration at nearly the identical time as many other companies, who later collectively came to live known as “coding bootcamps”. It was during this term that a few intrepid companies were starting to prove that you could pick up graduates a tall paying salary after training for only a few months. That short duration caught everyone’s eye. Dev Bootcamp, in particular, nearly single-handedly created the “coding bootcamp” industry; to this day, it’s called “coding bootcamps” mostly because of Dev Bootcamp.

    I happened to live based out San Francisco at the time and met with Shereef Bishay, founder of Dev Bootcamp, in their Chinatown office. Shereef became interested in what Kevin and I were doing and offered a partnership: they could roll their courses under the Dev Bootcamp brand and become their “preparatory” program. Because of their initial success, Dev Bootcamp started attracting a larger variety of students and many of their applicants lacked readiness. Not being interested in working for someone else, they declined. Besides meeting Shereef, I likewise grabbed beers with other local bootcamp founders, enjoy Roshan Choxi and Dave Paola, founders of Bloc.io. It felt enjoy something Big was about to chance in the industry and San Francisco was the epicenter.

    Meanwhile, Kevin and I continued executing their cohort-based courses. Their courses during this term were similar to ones you’d find in college: daily live lectures with a cohort of about 20–30 students with courses that lasted about a month. I had recently attended an online GMAT prep course offered by Knewton (they no longer carry out this) and was inspired by the format of their live lectures combined with ad-hoc quizzes. It forced participants to pay attention and succeed along, and it felt enjoy a much better sustain than a typical college lecture, where you could sulk in the back of a large classroom and not ever engage with the instructor. The notion seemed promising: using innovative online tools, they could educate little live cohorts and ensure that everyone engaged with the material.

    In order to device out what topics to teach, they asked students what they would live interested in learning. Not surprisingly, they mentioned bar no portion the advanced topics that employers demanded: TDD, APIs, Rails and Angular (this was before React was popular), testing, algorithms, data structures, design patterns, best practices, etc. By this point, Kevin and I each had over 10 years of software engineering experience, so the list of topics seemed straight-forward enough and they set out to educate them.

    The problems they encountered were immediate and obvious.

  • Student readiness levels elope the gamut. It’s impossible to educate TDD when someone doesn’t know basic programming principles. They can’t talk about APIs when students didn’t know HTTP. They can’t walk through algorithms when students can’t control nested loops.
  • Related to the first issue, students didn’t withhold pace with the lectures. About half the students stopped attending the live lectures after the first week. Though bar no portion lectures were recorded, few made an application to design up for lost time and instead elected to travel at their own pace. By the cessation of the month-long course, only a few students were noiseless attending the live lectures.
  • The above two problems forced us early on to resolve if they cared about students’ comprehension at the cessation of courses. If they didn’t, the solution would live easier: they could just sell recorded videos and content for a fixed charge and focus their energies on marketing the content. On the other hand, if they did reliance about comprehension afterwards, we’d possess to find another teaching format because while the notion of live lectures with quizzes seemed auspicious in theory, in practice, most people don’t possess the discipline to finish a rigorous course. And without the threat of withholding a credential, they couldn’t carry out anything to force people to note up.

    These problems likewise forced us to contemplate arduous about who their audience was. If companies enjoy Dev Bootcamp were able to train people for tall paying jobs, why couldn’t they carry out the same, if only they selected the privilege students? My previous sustain as an Engineering Manager told me that companies are willing to pay $15,000 to $30,000+ as a referral fee for qualified candidates. Couldn’t they monetize that cessation if they could find and train auspicious students? This line of thinking only made things more confusing, because if they withhold pushing on that logic, wouldn’t it live easier to just become a recruiting company? Why bother doing bar no portion the arduous drudgery of trying to train unprepared people when they can just filter for the best? That seemed enjoy a more viable business, especially since bar no portion the startup literature says to freight businesses instead of individual users wherever you can.

    Our initial stab at teaching people programming yielded some stars who landed powerful jobs, but that was, as is factual for most education institutions, a result of selection bias as opposed to their wonderful training methods. The choices in front of us were either to 1) device out a artery to design money and give up on making sure students actually understood the material, or 2) device out a artery to better train people for comprehension and not worry about optimizing for revenue.

    We made a few critical decisions then that they noiseless adhere to today:

  • Students are their customers, not employers. By eliminating employers as a viable revenue source, it brought clarity to what they were putative to do. One of the things they wanted to carry out was to profit people, not only to design money for ourselves. After all, they had just quit tall paying jobs to carry out something meaningful together. Helping employers didn’t appear very meaningful to us personally and while they were ok with that being a side upshot of producing powerful programmers, they didn’t want to incentivize ourselves to become a recruiting company.
  • We decided to not bewitch venture funding. Though it may possess been a bit early in their lifecycle to design that decision, they felt that training companies carry out not possess a significant viral first-mover advantage. Instead, the edge was in long-term reputation. Sure, it’d live viable to over-promise and over-hype the marketing in the short-term, but their hypothesis was that over time the lack of results will entangle up with the hype. They had decided to dedicate their entire thirties to this experiment, and they felt that this long-term mindset could live an edge in the education space. It’s exciting that Shereef, Roshan, and Dave opted for the opposite route with their companies and took on venture funding.
  • The consequences of those decisions significantly focused their energy.

    By identifying students as their customers, they aligned ourselves with students and started to focus on pedagogy and comprehension, rather than throughput and conversion. It likewise meant we’re a B2C company and not a B2B company. This had implications to their processes. For example, they stopped doing sales calls to employers to try to pick up them to purchase licenses in bulk. Instead, they took time to possess calls with every prospective student.

    By going the bootstrapped route, they decided on a low-burn long-term monetary plan, which usually meant sacrificing marketing for curriculum development. In their hypothesis, there’s no rush to pick up to market, and it’s more essential to protect Launch School’s reputation by always doing “the privilege thing for the student”. Venture-backed companies possess a “fail fast, fail often” mentality where growth rules above all. But in education, “failing” means negatively affecting students’ lives. They weren’t snug with purposefully hurting even a little group of students as portion of the trade plan.

    2013–2014: Tealeaf Academy

    We continued running their synchronous cohorts and the problematic patterns kept repeating cohort after cohort. They took everything they erudite and decided to change their curriculum in a couple of essential ways:

  • From synchronous to asynchronous (aka, self-paced). Instead of relying on live lectures that were sparsely attended, we’d waddle to recordings that students could watch at any time.
  • From one 1-month long course, they moved to 3 courses that would bewitch roughly 4 months in total. The courses would start from the ground up, teaching basic programming principles to start, then structure up to web evolution basics, and finally to bar no portion the advanced concepts employers wanted.
  • These two changes made a huge inequity and students understood this sequence of courses much better. Instead of emotion overwhelmed in the first week, students could complete lectures and assignments on their own schedule. They didn’t give too much thought to the pricing structure and continued to sell the courses at a fixed charge per course.

    Even with the unique self-paced 3-course sequence, results noiseless varied widely. Some graduates got jobs that paid over $100k, and others who finished bar no portion 3 courses said they didn’t learn a thing. They posted the $100k student on their testimonials, but it felt enjoy selection bias and not real education for all. It felt that despite their efforts to avoid becoming a recruiting company, they just ended up creating a recruiting company with a 3-course filter.

    The all point of charging students and forgoing funding was so they can align ourselves with students and carry out the privilege thing for students. So how can someone pay over $2,000 and spend over 4 months, and then express they didn’t learn anything? Even if it was a little number of students, that was noiseless a crushing result for us. They couldn’t let it travel and write it off as people being unprepared.

    We decided to zoom in on the problem and try to understand the core of the issue. They participated in countless 1on1 sessions with students who were struggling and began noticing patterns. They would pair with students who were struggling in course 3 and discern that what they were struggling with was not the advanced topic, but fundamentals. They couldn’t build an API not because they couldn’t intellectually understand the concept of an API, but because they didn’t know how HTTP worked. It had nothing to carry out with intellectual ability, but everything carry out with understanding of prerequisite knowledge. When they asked “don’t you recollect HTTP from course 1?”, they’d express something to the upshot of “sure, benevolent of, but I went through that portion pretty fast, and to live honest, it’s noiseless a slight fuzzy”. After seeing this over and over, they realized that they were missing a critical component in their courses: assessments.

    After teaching people for 2 years, they erudite what teachers across the world possess known for centuries: you must possess some test of mastery to demonstrate comprehension.

    Upton Sinclair once said, “It is difficult to pick up a man to understand something, when his salary depends on his not understanding it.” They fell into this trap by not thinking carefully about how their pricing suitable with their pedagogy. They never seriously thought about adding rigorous assessments because it meant that less students would enroll in and pay for subsequent courses. They were financially incentivizing ourselves to usher students to subsequent courses without respect to mastery, which is in direct combat with their mastery-based values. They charged per course, so adding assessments would possess resulted in less revenue. The key lesson they took away from this observation was: live vigilant of how pricing introduces natural blindspots to your company or product.

    2015: Lessons Learned

    Having taught people for over 2 years at this point, they had enough information to travel back to the lab and build a curriculum from the ground up anew. They spent the next year studying, researching and debating about what a powerful training program looked like. Over and over, they create ourselves constantly trapped by incompatible goals. For example, they wanted a democratic learning program that could cater to all, but how carry out you reconcile that goal with the wish to drive people to tall paying jobs? You either possess to give up the tall paying jobs or you possess to filter based on experience. If you only possess a 4 or 6 month timeframe, what topics carry out you cover and how carry out you design sure people are following along? Is it ok if only the top 10% or 20% understand the material at the end?

    To address these incompatible learning goals, they started from their own first principles by thinking about how we’re different, what their core beliefs were, and their personal stance on learning and comprehension. One notion that came up over and over in their research and discussions was operating for the *long-term*.

    If they bewitch a long-term perspective in their trade operations, then it’d live viable to likewise bewitch a long-term perspective on their pedagogical approach for the curriculum. They can’t possess a company that’s focused on chasing quarterly revenue results and reconcile that with a long-term curriculum. The company’s vision and the pedagogy must live aligned. After realizing that, they made an essential decision: they decided to not only spend their thirties on this, but to spend the ease of their careers on this project. That seems melodramatic and conjures up images of a sworn blood oath under a replete moon, but it wasn’t a arduous conclusion at bar no portion and they made it fairly quickly and unceremoniously. That’s because 1) they didn’t possess any other auspicious ideas in the pipeline, 2) they believe that working on this problem will positively impress the world, 3) they believe in each other and don’t want to drudgery on sever things, and 4) teaching online allows us to engage with a worldwide community of students, which brings a inevitable joy to the project. They didn’t possess any understanding to stop, and they thought that by focusing on decades in the future, they could exercise that perspective to their advantage.

    Suddenly after that shift in perspective, they could discern how a willingness to contemplate about 10, 20 years into the future allowed us to unlock long-term value, both for us as a trade and their students. While there were a lot of short-term incompatibilities between learning goals and trade goals, these issues melted away when considered in the span of years and decades. Suddenly they could focus on skills to final a career, rather than chase short-term fads. They finally create a artery to align personal, business, and student goals.

    Just enjoy how a long-simmering programming mystify may foster into more focus as one spends more time digesting it, the education mystify began to unfold for us as they shifted into long-term thinking. With the long-term perspective as their north star, they came up with the following values for their trade and learning pedagogy:

  • Mastery of fundamentals first.
  • No time confine for each course.
  • Assessments to test mastery.
  • Pedagogy-led pricing.
  • Don’t focus on short-term revenue.
  • All these ideas taken together formed the foundation for their Mastery-based Learning pedagogy at Launch School.

    2016: Launch School

    It took us a year to build the unique curriculum, and at the cessation of 2015, they launched Launch School. They didn’t possess proof that this unique curriculum would live good; it seemed privilege based on their sustain and values, but since they just started, they didn’t possess any concrete results to show. They asked prospective students to reliance the process and asked if learning fundamentals to mastery made intuitive sense. They didn’t carry out any market research and built the unique curriculum based off of their own standards of excellence, so they weren’t sure how people would react. Would they contemplate at their proposal of learning indefinitely and then compare it with a 3-month bootcamp and laugh at us? Would they agree with us that the issue with learning advanced topics and frameworks was bar no portion about understanding fundamentals? The current marketplace was replete of hype about turning around a six-figure job after a few months. How would people receive the notion of potentially learning for a few years?

    Fortunately for us, some people chose to reliance the process and started learning with us, from fundamentals with mastery.

    2017: Capstone

    By focusing on fundamentals, they felt they were setting up students for long-term success. But they noiseless had the “last mile” problem to decipher to demonstrate that there’s a quantitative inequity between those who took time to learn fundamentals vs those who didn’t. After all, if the results between learning fundamentals for 2 years and cramming frameworks for 2 months are the same, why bother with the fundamentals?

    Towards the cessation of 2016, they were able to bewitch some of their Launch School students and reserve them into an vehement instructor-led program to discern if they could address the “last mile” problem. They created Capstone, a finishing program where students could apply their already-mastered fundamentals to more complicated engineering problems. They wanted to note the world what’s viable when you bewitch years to really learn something well by putting their Capstone graduates into the marketplace. They spent most of 2017 running Capstone cohorts and observing their performance in the most competitive markets in the United States.

    2018: Results and Outcomes

    Finally in 2018, they were able to showcase the results so far. Because it took a few years for us to wrap their head around the problem, and then a few more years for students to complete their curriculum, they are only seeing quantitative results now in 2018. Of course, they had many little victories along the artery with many of their students saw their courses changed their lives, but teaching fundamentals for years likewise meant taking us farther away from concrete results. Now that they possess them, the results are astounding; discern for yourself.

    Why doesn’t anyone else carry out Mastery-based Learning?

    To address the question that initially triggered this article, I contemplate they were the only ones who arrived at Mastery-based Learning because of the following.

    We’re bootstrapped.

    Other programs focus on financing and pricing innovation, partnerships, scholarships, marketing, government sponsorships, accreditation/credentialism, trade process innovation, niche audience segmentation, but no portion appear interested in pedagogical innovation. I believe that they were able to focus squarely on pedagogy because they kept expanding their time horizon, which wouldn’t possess been viable with venture funding. Had they taken investors’ money, we’d possess been pressured to find a path to hyper-growth before the money ran out. This is why so many funded coding bootcamps are under stress and can’t innovate on one of the most essential attributes for educational companies: their pedagogy.

    Quality over data.

    I enjoy to contemplate I’m a data-driven person, but many operators act larger than they are. Most little education companies are not operating at the scale of Amazon (the archetype for the soul-less numbers driven company), and yet they exercise numbers to override values. Numbers and data are important, but you must possess some opinions on attribute regarding your craft that you can’t compromise on regardless of what the numbers say. Had they followed the arduous logic of numbers from their first year of teaching, they would’ve ended up a recruiting company because that’s what the data says employers wanted. There are likewise things they won’t do, no matter what the data says. For example, they just plainly spurn to “fail fast, and fail often” because it hurts people (also, they design enough honest mistakes that they don’t need a company philosophy to shove for more). I recollect first hearing about this concept and thought “that’s a powerful hack for startup founders”. But when you’re on the receiving cessation of this ideology as a customer, you contemplate “what a bunch of amateurs and assholes”. In order to carry out the privilege thing, you possess to possess an conviction around quality. If you don’t yet possess one, it’s essential to waddle slowly and device it out until you do. Following a 100% numbers driven analysis, no one would arrive at Mastery-based Learning.

    Have core values.

    A lot of people treat starting a trade as a treasure hunt for revenue. In the course of running a business, many decisions foster down to this choice: design money or improve quality. It seems counterintuitive, shouldn’t the higher attribute product design more money? In industries where results are not obvious or delayed by months and years, it’s very viable to over-promise and lead with marketing. In such industries, it’s much easier to first design money and then device it out later (another venture-backed mantra: “fake it until you design it”). One major lesson I erudite starting Launch School was in learning more about myself. For example, I erudite that there wasn’t one or two lines, but lots of lines I wasn’t willing to cross to design money. I erudite about who I was, and who I wanted to become and it’s not a powerful entrepreneur or the founder of a multi-million dollar company. For me, it’s about trying to build something worthwhile that lasts as long as possible. It’s about enjoying the daily process of drudgery and doing something positive for the world and working with people I luxuriate in being around. Just as tall salaries are actually not the cessation goal for students at Launch School (they are a side upshot of learning to mastery), revenue is not the cessation goal of the trade side of Launch School — it is a side upshot of becoming a meaningful long-term organization. I believe that this perspective is what helped us to unlock the long-term value behind Mastery-based Learning.


    The Best Self-Service trade Intelligence (BI) Tools of 2018 | killexams.com real questions and Pass4sure dumps

    Analytics Beyond Spreadsheets

    For many years, Microsoft transcend and other spreadsheets were the tools of altenative for trade professionals who were looking to visualize their data. But spreadsheets had their limits for many trade intelligence (BI)-related tasks. Even today, trying to creating charts analyzing complicated datasets in transcend can noiseless live frustrating. Sometimes you start with the wrong benevolent of data, for example, or you may not know how to manipulate the spreadsheet to create the data visualization{{/ZIFFARTICLE} you need. On the other hand, the rising tide of data democratization is giving everyone in an organization access to corporate data. The need has arisen for efficacious tools that people of bar no portion skill levels can exercise to design sense of the wealth of information created by businesses every solitary day.

    Spreadsheets likewise plunge down when the data isn't well-structured or can't live sorted out in natty rows and columns. And, if you possess millions of rows or very sparse matrices, then the data in a spreadsheet can live painful to enter and it can live arduous to visualize your data. Spreadsheets likewise possess issues if you are trying to create a report that spans multiple data tables or that mixes in Structured Query Language (SQL)-based databases, or when multiple users try to maintain and collaborate on the identical spreadsheet.

    A spreadsheet containing up-to-the-minute data can likewise live a problem, particularly if you possess exported graphics that need to live refreshed when the data changes. Finally, spreadsheets aren't auspicious for data exploration; trying to spot trends, outlying data points, or counterintuitive results is difficult when what you are looking for is often hidden in a long row of numbers.

    While spreadsheets and self-service BI tools both design exercise of tables of numbers, they are really acting in different arenas with different purposes. A spreadsheet is first and foremost a artery to store and parade calculations. While some spreadsheets can create very sophisticated mathematical models, at their core it is bar no portion about the math more than the model itself.

    This is bar no portion a long-winded artery of saw that when businesses exercise a spreadsheet, they are actively sabotaging themselves and their aptitude to consistently pick up valuable insights from their data. BI tools are specficially designed to profit businesses better understand their data, and can prove to live a huge profit to those upgrading from what a limited spreadsheet can do.

    What Is trade Intelligence?

    Defining BI is tricky. When you examine what it does and why companies exercise it, it can start to sound vague and nebulous. After all, many different kinds of software offer analytics features, and bar no portion businesses want to improve. Understanding what a BI is or isn't can live unclear.

    BI is an umbrella term meant to cover bar no portion of the activities necessary for a company to gyrate raw information into actionable knowledge. In other words, it's a company's efforts to understand what it knows and what it doesn't know of its own actuality and operations. The ultimate goal is being able to enlarge profits and sharpen its competitive edge.

    Framed that way, BI as a concept has been around as long as business. But that concept has evolved from early basics [like Accounts Payable (AP) and Accounts Receivable (AR) reports and customer contact and contract information] to much more sophisticated and nuanced information. This information ranges across everything from customer behaviors to IT infrastructure monitoring to even long-term fixed asset performance. Separately tracking such metrics is something most businesses can carry out regardless of the tools employed. Combining them, especially disparate results from metrics normally not associated with one another, into understandable and actionable information, well, that's the knack of BI. The future of BI is already shaping up to simultaneously broaden the scope and variety of data used and to sharpen the micro-focus to ever finer, more granular levels.

    BI software has been instrumental in this constant progression towards more in-depth scholarship about the business, competitors, customers, industry, market, and suppliers, to denomination just a few viable metric targets. But as businesses grow and their information stores balloon, the capturing, storing, and organizing of information becomes too large and complicated to live entirely handled by mere humans. Early efforts to carry out these tasks via software, such as customer relationship management (CRM) and enterprise resource planning (ERP), led to the formation of "data silos" wherein data was trapped and useful only within the confines of inevitable operations or software buckets. This was the case unless IT took on the task of integrating various silos, typically through painstaking and highly manual processes.

    While BI software noiseless covers a variety of software applications used to resolve raw data, today it usually refers to analytics for data mining, analytical processing, querying, reporting, and especially visualizing. The main inequity between today's BI software and Big Data analytics is mostly scale. BI software handles data sizes typical for most organizations, from little to large. Big Data analytics and apps handle data analysis for very large data sets, such as silos measured in petabytes (PBs).

    Self-Service BI and Data Democratization

    The BI tools that were well-liked half a decade or more ago required specialists, not just to exercise but likewise to interpret the resulting data and conclusions. That led to an often inconvenient and fallible filter between the people who really needed to pick up and understand the business—the company conclusion makers—and those who were gathering, processing, and interpreting that data—usually data analysts and database administrators. Because being a data specialist is a demanding job, many of these folks were less well-versed in the actual workings of the trade whose data they were analyzing. That led to a focus on data the company didn't need, a misinterpretation of results, and often a succession of "standard" reporting that analysts would elope on a scheduled basis instead of more ad hoc intelligence gathering and interpretation, which can live highly valuable in fast-moving situations.

    This problem has led to a growing unique trend among unique BI tools coming onto the market today: that of self-service BI and data democratization. The goal for much of today's BI software is to live available and usable by anyone in the organization. Instead of requesting reports or queries through the IT or database departments, executives and conclusion makers can create their own queries, reports, and data visualizations through self-service models, and connect to disparate data both within and outside the organization through prebuilt connectors. IT maintains overall control over who has access to which tools and data through these connectors and their management utensil arsenal, but IT no longer acts as a bottleneck to every query and report request.

    As a result, users can bewitch edge of this distributed BI model. Key tools and critical data possess moved from a centralized and difficult-to-access architecture to a decentralized model that merely requires access credentials and familiarity with unique BI software. This results in a all unique benevolent of analysis becoming available to the organization, namely, that of experienced, front-line trade people who not only know what data they need but how they need to exercise it.

    The emerging crop of BI tools bar no portion drudgery arduous at developing front-end tools that are more intuitive and easier to exercise than those of older generations—with varying degrees of success. However, that means a key criteria in any BI utensil purchasing conclusion will live to evaluate who in the organization should access such tools and whether the utensil is appropriately designed for that audience. Most BI vendors bespeak they're looking for their utensil suites to become as ubiquitous and facile to exercise for trade users as typical trade collaboration tools or productivity suites, such as Microsoft Office. no portion possess gotten quite that far yet in my estimation, but some are closer than others. To that end, these BI utensil suites watch to focus on three core types of analytics: descriptive (what did happen), prescriptive (what should chance now), and predictive (what will chance later).

    What Is Data Visualization?

    In the context of BI software, data visualization is a hasty and efficacious artery of transferring information from a machine to a human brain. The notion is to status digital information into a visual context so that the analytic output can live quickly ingested by humans, often at a glance. If this sounds enjoy those pie and bar charts you've seen in Microsoft Excel, then you're right. Those are early examples of data visualizations.

    But today's visualization forms are rapidly evolving from those traditional pie charts to the stylized, the artistic, and even the interactive. An interactive visualization comes with layered "drill downs," which means the viewer can interact with the visual to attain more granular information on one or more aspects incorporated in the bigger picture. For example, unique values can live added that will change the visualization on the fly, or the visualization is actually built on rapidly changing data that can gyrate a static visual into an animation or a dashboard.

    The best visualizations carry out not seek artistic awards but instead are designed with duty in mind, usually the quick and intuitive transfer of information. In other words, the best visualizations are simple but powerful in clearly and directly delivering a message. High-end visuals may contemplate impressive at first glance but, if your audience needs profit to understand what's being conveyed, then they've ultimately failed.

    Most BI software, including those reviewed here, comes with visualization capabilities. However, some products offer more options than others so, if advanced visuals are key to your BI process, then you'll want to closely examine these tools. There are likewise third-party and even free data visualization tools that can live used on top of your BI software for even more options.

    Products and Testing

    In this review roundup, I tested each product from the perspective of a trade analyst. But I likewise kept in intellect the viewpoint of users who might possess no familiarity with data processing or analytics. I loaded and used the identical data sets and posed the identical queries, evaluating results and the processes involved.

    My point was to evaluate cloud versions alone, as I often carry out analysis on the flit or at least on a variety of machines, as carry out legions of other analysts. But, in some cases, it was necessary to evaluate a desktop version as well or instead of the cloud version. One illustration of this is Tableau Desktop, a favorite utensil of Microsoft transcend users who simply possess an affinity for the desktop utensil (and who just waddle to the cloud long enough to participate and collaborate).

    I ended up testing the Microsoft Power BI desktop version, too, on a Microsoft representative's recommendation because, as the rep said, "the more robust data prep tools are there." Besides, said the rep, "most users prefer the desktop utensil over a web utensil anyway." Again, I don't doubt Microsoft's title but that does appear unearthly to me. I've heard it said that desktop tools are preferred when the data is local as the process feels faster and easier. But seriously, how much data is truly local anymore? I suspect this odd desktop utensil preference is a bit more personal than fact-based, but to each his own.

    Then there's Google Analytics, a simple cloud player. The utensil is designed to resolve website and mobile app data so it's a different critter in the BI app zoo. That being the case, I had to deviate from using my test data set and queries, and instead test it in its natural habitat of website data. Nonetheless, it's the processes that are evaluated in this review, not the data.

    While I didn't test any of these tools from a data scientist's role, I did mention advanced capabilities when I create them, simply to let buyers know they exist. IBM Watson Analytics is one utensil with the aptitude to extend to highly advanced features and was likewise one of the easiest to exercise upfront. IBM Watson Analytics is well-suited for trade analysts and for widespread data democratization because it requires little, if any, scholarship of data science. Instead, it works well by using natural language and keywords to shape queries, a characteristic that can design it valuable to practically anyone. It's highly intuitive, very powerful, and facile to learn. Microsoft Power BI is a tough second as it, too, is powerful while likewise familiar, certainly to any of the millions of Microsoft trade users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they bar no portion possess their own pros and cons. We'll live adding even more in the coming months.

    One thing to watch out for during your evaluations of these products is that many don't yet handle streaming data. For many users, that won't live a problem in the immediate future. However, for those involved with analyzing trade processes as they happen, such as website performance metrics or customer deportment patterns, streaming data can live invaluable. Also, the Internet of Things (IoT) will drive this issue in the near future and design streaming data and streaming analytics a must-have feature. Many of these tools will possess to up their game accordingly so, unless you want to jump ship in a year or two, it's best to contemplate ahead when considering BI and the IoT.

    BI and Big Data

    Another locality in which self-service BI is taking off is in analyzing Big Data. This is a newer evolution in the database space but it's driving tremendous growth and innovation. The denomination is an apt descriptor because Big Data generally refers to huge data sets that are simply too Big to live managed or queried with traditional data science tools. What's created these behemoth data collections is the explosion of data-generating, tracking, monitoring, transaction, and gregarious media tools (to denomination a few) that possess become so well-liked over the final several years.

    Not only carry out these tools generate loads of unique data, they likewise often generate a unique benevolent of data, namely "unstructured" data. Broadly speaking, this is simply data that hasn't been organized in a predefined way. Unlike more traditional, structured data, this benevolent of data is massive on text (even free-form text) while likewise containing more easily defined data, such as dates or credit card numbers. Examples of apps that generate this benevolent of data include the customer behavior-tracking tools you exercise to discern what your customers are doing on your e-commerce website, the piles of log and event files generated from some smart devices (such as alarms and smart sensors), and broad-swath gregarious media tracking tools.

    Organizations deploying these tools are being challenged not only by a sudden deluge of unstructured data that quickly strains storage resources [think beyond terabytes (TB) into the PB and even exabyte (EB) range] but, even more importantly, they're finding it difficult to query this unique information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data. unique data storage innovations such as data lakes are emerging to decipher for this need, but organizations noiseless relying exclusively on traditional tools while deploying front-line apps that generate unstructured data often find themselves sitting on mountains of data they don't know how to leverage.

    Enter Big Data analysis standards. The golden standard here is Hadoop, which is an open-source software framework that Apache specifically designed to query large data sets stored in a distributed fashion (meaning, in your data center, the cloud, or both). Not only does Hadoop let you query Big Data, it lets you simultaneously query both unstructured as well as traditional structured data. In other words, if you want to query bar no portion of your trade data for maximum insight, then Hadoop is what you need.

    You can download and implement Hadoop itself to accomplish your queries, but it's typically easier and more efficacious to exercise commercial querying tools that employ Hadoop as the foundation of more intuitive and full-featured analysis packages. Notably, most of the tools reviewed here, including Chartio, IBM Watson Analytics, Microsoft Power BI, and Tableau Desktop, bar no portion champion this. However, each requires varying levels of configuration or even add-on tools to carry out so—with IBM, Microsoft, and Tableau offering exceptionally abysmal capabilities. However, both IBM and Microsoft will noiseless await customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

    Finding the privilege BI Tool

    Given the issues spreadsheets can possess when used as ad hoc BI tools and how firmly ingrained they are in their psyches, finding the privilege BI utensil isn't a simple process. Unlike spreadsheets, BI tools possess major differences when it comes to how they consume data inputs and outputs and manipulate their tables. Some tools are better at exploration than analysis, and some require a fairly sheer learning curve to really design exercise of their features. Finally, to design matters worse, there are dozens if not hundreds of such tools on the market today, with many vendors willing to title the self-serve BI label even if it doesn't quite fit.

    Getting the overall workflow down with these tools will bewitch some study and discussion with the people you'll live designating as users. Tableau Desktop and Microsoft Power BI, for example, will start users out with the desktop version to build visualizations and link up to various data sources. Once you possess this together, you can start sharing those results online or across your organization's network. With others, such as Chartio or Google Analytics, you start in the cloud and linger there.

    In recent years, companies possess been taking edge of the wide selection of online learning platforms out there to train their employees on using these platforms. As intuitive as these platforms may be, it is essential to design sure that your employees actually know how to exercise these BI platforms so that you can design sure your investment was worthwhile. There are many ways of approaching this, but using the privilege online learning platform might live a auspicious status to start looking.

    Given the wide charge compass of these products, you should segment your analytics needs before you design any buying decision. If you want to start out slowly and inexpensively, then the best route is to try something that offers significant functionality for free, such as Microsoft Power BI. Such tools are very affordable and design it facile to pick up started. Plus, they watch to possess large ecosystems of add-ons and partners that can live a cost-effective replacement for doing BI inside a spreadsheet. Tableau Desktop noiseless has the largest collection of charts and visualizations and the biggest ally network, though both IBM Watson Analytics and Microsoft Power BI are catching up fast.

    IBM Watson Analytics scored the highest, and Microsoft Power BI and Tableau Desktop scored the next highest in their roundup. However, bar no portion three products received their Editors' altenative award. Tableau Desktop may possess a Big charge tag depending on which version you select but, as previously mentioned, it has an exceptionally large and growing collection of visualizations plus a manageable learning curve if you're willing to devote some application to it. Microsoft Power BI and Tableau Desktop likewise possess large and growing collections of data connectors, and both Microsoft and Tableau possess their own sizable communities of users that are vocal about their wants and needs. This can carry a lot of weight with the vendors' evolution teams so it's a auspicious notion to spend some time looking through those community forums to pick up an notion where these companies are headed.

  • Pros: Extremely user-friendly. fantastic automatic report generation. Impressive champion availability.

    Cons: Automated reports can quickly become defaults. sheer learning curve that might confuse beginners.

    Bottom Line: Zoho Reports is a solid option for generic trade users who might not live knowledgeable in analytics software. It's likewise available at an attractive price.

    Read Review
  • Pros: Accessible user interface. Smart guidance features. Impressively hasty analytics. Robust natural language querying.

    Cons: Unable to carry out real-time streaming analytics.

    Bottom Line: IBM Watson Analytics is an exceptional trade intelligence (BI) app that offers a tough analytics engine along with an excellent natural language querying tool. This is one of the best BI platforms you'll find and easily takes their Editors' altenative honor.

    Read Review
  • Pros: Extremely powerful platform with a wealth of data source connectors. Very user-friendly. Exceptional data visualization capabilities.

    Cons: Desktop and web versions divide data prep tools. Refresh cycle is limited on free version.

    Bottom Line: Microsoft Power BI earns their Editors' altenative veneration for its impressive usability, top-notch data visualization capabilities, and superior compatibility with other Microsoft Office products.

    Read Review
  • Pros: huge collection of data connectors and visualizations. User-friendly design. Impressive processing engine. mature product with a large community of users.

    Cons: replete mastery of the platform will require substantial training.

    Bottom Line: Tableau Desktop is one of the most mature offerings on the market and that shows in its feature set. While it has a steeper learning curve than other platforms, it's easily one of the best tools in the space.

    Read Review
  • Pros: Bottlenecks are eliminated thanks to in-chip processing. Impressive natural language query in third-party applications.

    Cons: Might live too difficult for self-service trade intelligence (BI). Analytics process noiseless needs to live ironed out. Natural language capability can live limited.

    Bottom Line: Sisense is a complete platform that should live well-liked for experienced BI users. It may plunge short for beginners, however.

    Read Review
  • Pros: Wide compass of connectors. Impressive sharing features. Limitless data storage.

    Cons: User interface is not intuitive. sheer learning curve. Unwelcoming to unique analysts.

    Bottom Line: Domo isn't for newcomers but for companies that already possess trade intelligence (BI) sustain in their organization. Domo's a powerful BI utensil with a lot of data connectors and solid data visualization capabilities.

    Read Review
  • Pros: Exceptional platform for website and mobile app analytics.

    Cons: Customer champion has artery too much automation. Focus on marketing and advertising can live frustrating to users. Relies mostly on third parties for training.

    Bottom Line: Due to its brand recognition and the fact that it's free, Google Analytics is the biggest denomination in website and mobile app intelligence. It has a sheer learning curve but it is an awesome trade intelligence tool.

    Read Review
  • Pros: Designed with generic trade users in mind. Solid revert on investment.

    Cons: The data you can exercise is limited. Needs additional platform to connect.

    Bottom Line: The Salesforce Einstein Analytics Platform is designed for customer, sales, and marketing analyses, although it can server other needs, too. Its powerful analytics capabilities along with its solid natural language querying functionality and a wide array of partners design it an attractive offering.

    Read Review
  • Pros: Real-time analytics for Internet of Things (IoT) and streaming data features. Massive ecosystem with plenteous extenders. Responsive pages design mobile publishing easiest. Impressive storytelling paradigm. Centralized view with consolidated analytics.

    Cons: Data prep features are lacking. Confusing toolbar design. Not friendly for beginners.

    Bottom Line: If your trade already uses SAP's HANA database platform or some of its other back-end trade platforms, then SAP Analytics Cloud is a powerful, well-priced choice. But live warned that there's a sheer learning curve and a celebrated dependence on other SAP products for replete functionality.

    Read Review
  • Pros: Impressive processing engine. Powerful query optimization on SQL. Entirely web-based. complicated queries are handled very well.

    Cons: Poorly designed user interface. sheer learning curve.

    Bottom Line: Chartio excels at structure a powerful analytics platform that experienced trade intelligence (BI) users will appreciate. Those unique to BI, however, will find it very difficult to use.

    Read Review
  • Pros: Very abysmal SQL modeling ability. Uses Git for version management and collaboration.

    Cons: Very expensive. Not for little teams.

    Bottom Line: Looker is a powerful self-service trade intelligence (BI) utensil that can profit unify SQL and Big Data management across your enterprise.

    Read Review
  • Pros: Custom access roles. Solid collection of public data online.

    Cons: complicated pricing is a deterrent.

    Bottom Line: Qlik Sense Enterprise Server is a self-service trade intelligence (BI) utensil that delivers the best collection of user access roles among the BI tools they tested, and likewise demonstrates a promising start towards integrating Data-as-a-Service (DaaS).

    Read Review
  • Pros: One of the largest collections of data connectors. Many granular access roles.

    Cons: No free trial available. Training webinars can live costly.

    Bottom Line: The company's Focus query language is showing its age but Information Builders' self-service trade intelligence (BI) utensil WebFocus nevertheless has some powerful analysis features.

    Read Review
  • Pros: Very facile to pick up started. Nice team management and collaboration features.

    Cons: The cloud version has a subset of features create in Windows version. Online documentation could live improved.

    Bottom Line: While Tibco is noiseless making the transition from a desktop to a cloud software vendor, its self-service trade intelligence (BI) utensil Tibco Spotfire is a powerful artery to start visualizing your transcend data.

    Read Review
  • Pros: Excellent analytical champion for Intuit QuickBooks. Very facile setup.

    Cons: Installation and setup is a bit of chore. No champion for Intuit QuickBooks' online versions.

    Bottom Line: Clearify QQube is the best self-service trade intelligence (BI) utensil for in-depth analysis of your Intuit QuickBooks files, though you'll need to contemplate elsewhere for broader BI tasks.

    Read Review

  • The customized, digitized, have-it-your-way economy Mass customization will change the artery products are made-- forever. | killexams.com real questions and Pass4sure dumps

    The customized, digitized, have-it-your-way economy Mass customization will change the artery products are made-- forever.

    (FORTUNE Magazine) – A reticent revolution is stirring in the artery things are made and services are delivered. Companies with millions of customers are starting to build products designed just for you. You can, of course, buy a Dell computer assembled to your exact specifications. And you can buy a pair of Levi's lop to suitable your body. But you can likewise buy pills with the exact blend of vitamins, minerals, and herbs that you like, glasses molded to suitable your face precisely, CDs with music tracks that you choose, cosmetics mixed to match your skin tone, textbooks whose chapters are picked out by your professor, a loan structured to meet your monetary profile, or a night at a hotel where every employee knows your favorite wine. And if your child does not enjoy any of Mattel's 125 different Barbie dolls, she will soon live able to design her own.

    Welcome to the world of mass customization, where mass-market goods and services are uniquely tailored to the needs of the individuals who buy them. Companies as diverse as BMW, Dell Computer, Levi Strauss, Mattel, McGraw-Hill, Wells Fargo, and a slew of leading Web businesses are adopting mass customization to maintain or obtain a competitive edge. Many are just beginning to dabble, but the direction in which they are headed is clear. Mass customization is more than just a manufacturing process, logistics system, or marketing strategy. It could well live the organizing principle of trade in the next century, just as mass production was the organizing principle in this one.

    The two philosophies couldn't clash more. Mass producers impose a one-to-many relationship, while mass customizers require incessant dialogue with customers. Mass production is cost-efficient. But mass customization is a flexible manufacturing technique that can slash inventory. And mass customization has two huge advantages over mass production: It is at the service of the customer, and it makes replete exercise of cutting-edge technology.

    A all list of technological advances that design customization viable is finally in place. Computer-controlled factory outfit and industrial robots design it easier to quickly readjust assembly lines. The proliferation of bar-code scanners makes it viable to track virtually every portion and product. Databases now store trillions of bytes of information, including individual customers' predilections for everything from cottage cheese to suede boots. Digital printers design it a cinch to change product packaging on the fly. Logistics and supply-chain management software tightly coordinates manufacturing and distribution.

    And then there's the Internet, which ties these disparate pieces together. Says Joseph Pine, author of the pioneering engage Mass Customization: "Anything you can digitize, you can customize." The Net makes it facile for companies to waddle data from an online order shape to the factory floor. The Net makes it facile for manufacturing types to communicate with marketers. Most of all, the Net makes it facile for a company to conduct an ongoing, one-to-one dialogue with each of its customers, to learn about and respond to their exact preferences. Conversely, the Net is likewise often the best artery for a customer to learn which company has the most to offer him--if he's not ecstatic with one company's wares, nearly perfect information about a competitor's is just a mouse click away. Combine that with mass customization, and the nature of a company's relationship with its customers is forever changed. Much of the leverage that once belonged to companies now belongs to customers.

    If a company can't customize, it's got a problem. The Industrial Age model of making things cheaper by making them the identical will not hold. Competitors can copy product innovations faster than ever. Meanwhile, consumers exact more choices. Marketing guru Regis McKenna declares, "Choice has become a higher value than brand in America." The largest market shares for soda, beer, and software carry out not belong to Coca-Cola, Anheuser-Busch, or Microsoft. They belong to a category called Other. Now companies are trying to bear a unique Other for each of us. It is the logical culmination of markets' being chopped into finer and finer segments. After all, the ultimate niche is a market of one.

    The best--and most famous--example of mass customization is Dell Computer, which has a direct relationship with customers and builds only PCs that possess actually been ordered. Everyone from Compaq to IBM is struggling to copy Dell's model. And for auspicious reason. Dell passed IBM final quarter to title the No. 2 spot in PC market participate (behind Compaq). While other computer manufacturers struggle for profits, Dell keeps reporting record numbers; in its most recent quarter the company's sales were up 54%, while earnings soared 62%. No phenomenon Michael Dell has become the poster boy of the unique economy. As Pine says, "The closest person they possess to Henry Ford is Michael Dell."

    Dell's triumph is not so much technological as it is organizational. Dell keeps margins up by keeping inventory down. The company builds computers from modular components that are always readily available. But Dell doesn't want to store tons of parts: Computer components decline in value at a rate of about 1% a week, faster than just about any product other than sushi or losing lottery tickets. So the key to the system is ensuring that the privilege parts and products are delivered to the privilege status at the privilege time.

    To carry out this, Dell employs sophisticated logistics software, some developed internally, some made by i2 Technologies. The software takes info gathered from customers and steers it to the parts of the organization that need it. When an order comes in, the data collected are quickly parsed out--to suppliers that need to rush over a shipment of arduous drives, say, or to the factory floor, where assemblers reserve parts together in the customer's desired configuration. "Our goal," says vice chairman Kevin Rollins, "is to know exactly what the customer wants when they want it, so they will possess no waste."

    The company has been propelled by this thinking ever since Michael Dell started selling PCs from his college dorm play in 1983. The Web makes the process virtually seamless, by allowing the company to easily collect customized, digitized data that are ready for delivery to the people who need them. The result is an entire organization driven by orders placed by individual customers, an organization that does more Web-based commerce than almost anyone else. Dell's future doesn't depend on faster chips or modems--it depends on greater mastery of mass customization, of streamlining the rush of attribute information.

    It's not much of a amaze that a leading tech company enjoy Dell is using software and the Net in such innovative ways. What's startling is the extent to which companies in other industries are embracing mass customization. bewitch Mattel. Starting by October, girls will live able to log on to barbie.com and design their own friend of Barbie's. They will live able to select the doll's skin tone, eye color, hairdo, hair color, clothes, accessories, and denomination (6,000 permutations will live available initially). The girls will even fill out a questionnaire that asks about the doll's likes and dislikes. When the Barbie pal arrives in the mail, the girls will find their doll's denomination on the package, along with a computer-generated paragraph about her personality.

    Offering such a product without the Net would live next to impossible. Mattel does design specific versions of Barbie for customers such as Toys "R" Us, and the company customizes cheerleader Barbies for universities. But this will live the first time Mattel produces Barbie dolls in lots of one. enjoy Dell, Mattel must exercise high-end manufacturing and logistics software to ensure that the order data on its Website are distributed to the parts of the company that need them. The only real concern is whether Mattel's systems can handle the expected exact in a timely fashion. privilege now, marketing VP Anne Parducci is shooting for delivery of the dolls within six weeks--a bit much considering that that is how long it takes to pick up a custom-ordered BMW.

    Nevertheless, Parducci is pumped. "Personalization is a dream they possess had for several years," she says. Parducci thinks the custom Barbies could become one of next year's hottest toys. Then, says Parducci, "we are going to build a database of children's names, to develop a one-to-one relationship with these girls." That may sound creepy, but portion of mass customization is treating your customers, even preteens, as adults. By allowing the girls to define beauty in their own terms, Mattel is in theory helping them feel auspicious about themselves even as it collects personal data. That's quite a step for a company that has stamped out its own stereotypes of beauty for decades, but Parducci's market testing shows that girls' enthusiasm for being a fashion designer or creating a personality is "through the roof."

    Levi Strauss likewise likes giving customers the desultory to play fashion designer. For the past four years it has made measure-to-fit women's jeans under the Personal Pair banner. In October, Levi's will relaunch an expanded version called Original Spin, which will offer more options and will feature men's jeans as well.

    With the profit of a sales associate, customers will create the jeans they want by picking from six colors, three basic models, five different leg openings, and two types of fly. Then their waist, butt, and inseam will live measured. They will try on a unpretentious pair of test-drive jeans to design sure they enjoy the suitable before the order is punched into a Web-based terminal linked to the stitching machines in the factory. Customers can even give the jeans a name--say, Rebel, for a pair of black ones. Two to three weeks later the jeans arrive in the mail; a bar-code tag sealed to the pocket lining stores the measurements for simple reordering.

    Today a fully stocked Levi's store carries approximately 130 ready-to-wear pairs of jeans for any given waist and inseam. With Personal Pair, that number jumped to 430 choices. And with Original Spin, it will leap again, to about 750. Sanjay Choudhuri, Levi's director of mass customization, isn't in a rush to add more choices. "It is critical to carefully pick the choices that you offer," says Choudhuri. "An unlimited amount will create inefficiencies at the plant." Dell Computer's Rollins agrees: "We want to offer fewer components bar no portion the time." To these two, mass customization isn't about infinite choices but about offering a wholesome number of standard parts that can live mixed and matched in thousands of ways. That gives customers the illusion of boundless altenative while keeping the complexity of the manufacturing process manageable.

    Levi's charges a slight premium for custom jeans, but what Choudhuri really likes about the process is that Levi's can become your "jeans adviser." Selling off-the-shelf jeans ends a relationship; the customer walks out of the store as anonymous as anyone else on the street. Customizing jeans starts a relationship; the customer likes the fit, is ready for reorders, and forks over his denomination and address in case Levi's wants to dispatch him promotional offers. And customers who design their own jeans design the perfect focus group; Levi's can apply what it learns from them to the jeans it mass-produces for the ease of us.

    If Levi's experiment pays off, other apparel makers will succeed its lead. In the not-so-distant future people may simply walk into body-scanning booths where they will live bathed with patterns of white light that will determine their exact three-dimensional structure. A not-for-profit company called [TC]2, funded by a consortium of companies including Levi's, is developing just such a technology. final year some MIT trade students proposed a similar notion for a custom-made bra company dubbed perfect Underwear.

    Morpheus Technologies, a wacky startup in Portland, Me., hopes to set up studios equipped with corpse scanners. Founder Parker Poole III wants to "digitize people and connect their measurement data to their credit cards." Someone with the foresight to live scanned by Morpheus could then muster up Eddie Bauer, say, give his credit card number, and order a robe that matches his dimensions. His digital self could likewise live sent to Brooks Brothers for a suit. Gone will live the days of attentive men kneeling on the floor with pins in their mouths. Progress does possess its price.

    Thirty years ago auto manufacturers were, effectively, mass customizers. People would spend hours in the office of a car dealer, picking through pages of options. But that ended when car companies tried to improve manufacturing efficiency by offering slight more than a few standard options packages. BMW wants to gyrate back the clock. About 60% of the cars it sells in Europe are built to order, vs. just 15% in the U.S. Europeans appear willing to wait three to four months for a vehicle, while most Americans won't wait longer than four weeks.

    Now the company wants to design better exercise of its customer database to pick up more Americans to custom-order. BMW dealers reclaim about $450 in inventory costs on every such order. Reinhard Fischer, head of logistics for BMW of North America, says, "The Big battle is to bewitch cost out of the distribution chain. The best artery to carry out that is to build in just the things a consumer wants."

    Since most BMWs in the U.S. are leased, the company knows when customers will need a unique car. Some dealers now muster customers a few months before their leases are up to discern whether they'd enjoy to custom-order their next car. Soon, however, customers will live able to configure their own car online and dispatch that info to a dealer. Fischer can even discern a day when the Website will offer data about vehicles sailing on ships from Germany, so that people can discern whether a car matching their preferences is already on the way. That does, of course, raise the question, Why not dispatch the requests directly to BMW, circumventing dealers altogether? Says Fischer: "We don't want to liquidate their role, but maybe they should possess a 7% margin, not 16%." Ouch.

    Such dilemmas are inevitable, given that mass customization streamlines the order process. What's more, mass customization is about creating products--be they PCs, jeans, cars, eyeglasses, loans, or even industrial soap--that match your needs better than anything a traditional middleman can possibly order for you.

    LensCrafters, for instance, has made quick, in-store production of customized lenses common. But Tokyo-based Paris Miki takes the process a step further. Using special software, it designs lenses and a frame that conform both to the shape of a customer's face and to whether he wants, say, casual frames, a sports pair, sunglasses, or more formal specs. The customer can check out on a monitor various choices superimposed over a scanned image of his face. Once he chooses the pair he likes, the lenses are ground and the rimless frames attached.

    While they watch to contemplate of automation as a process that eliminates the need for human interaction, mass customization makes the relationship with customers more essential than ever. ChemStation in Dayton has about 1,700 industrial-soap formulas--for car washes, factories, landfills, railroads, airlines, and mines. The company analyzes items that are to live cleaned (recent ones in its labs include flutes and goose down) or visits its customers' premises to resolve their dirt. After the analysis, the company brews up a special batch of cleanser. The soap is then placed on the customer's property in reusable containers ChemStation monitors and keeps full. For most customers, teaching another company their cleansing needs is not worth the effort. About 95% of ChemStation's clients never leave.

    Hotels that want you to withhold coming back are using software to personalize your experience. bar no portion Ritz-Carlton hotels, for instance, are linked to a database filled with the quirks and preferences of half-a-million guests. Any bellhop or desk clerk can find out whether you are allergic to feathers, what your favorite newspaper is, or how many extra towels you like.

    Wells Fargo, the largest provider of Internet banking, already allows customers to apply for a home-equity loan over the Net and pick up a three-second conclusion on a loan structured specifically for them. A lot of behind-the-scenes technology makes this possible, including real-time links to credit bureaus, databases with checking-account histories and property values, and software that can carry out cash-flow analysis. With a few pieces of customized information from the loan seeker, the software whips into action to design a quick decision.

    The bank likewise uses similar software in its small-business lending unit. According to vice chairman Terri Dial, Wells Fargo used to gyrate away lots of qualified little businesses--the loans were too little for Wells to warrant the time spent on credit analysis. But now the company can collect a few key details from applicants, customize a loan, and certify or disaffirm credit in four hours--down from the four days the process used to take. In some categories that Wells once virtually ignored, loan approvals are up as much as 50%. Says Dial: "You either invest in the technology or pick up out of that line of business."

    She'd better withhold investing. Combine the software that enables customization with the ubiquity of the Web, and you pick up a situation that threatens Wells' very existence. If consumers grow accustomed to designing their own products, will they reliance brand-name manufacturers and service providers or will they gyrate to a unique benevolent of middleman? straightforward Shlier, a director of research at the Gartner Group in Stamford, Conn., sees disintermediaries emerging bar no portion over the Net to profit people sift through the thousands of choices presented to them. In monetary services, he suggests, there is "a unique role for a trusted adviser, maybe someone who doesn't own any banks."

    Shlier's middleman sounds a lot enjoy Intuit, which lets visitors to its quicken.com Website apply for and purchase mortgages from a variety of lenders, fill out their taxes, or set up a portfolio to track their stocks, bonds, and mutual funds. Tapan Bhat, the exec who oversees quicken.com, says, "The Web is probably the medium most attuned to customization, yet so many sites are centered on the company instead of on the individual." What would decoy someone to Levi's if she could instead visit a clothing Website that stored her digital dimensions and ordered custom-fit jeans from the manufacturer with the best charge and fit? Elaborates Pehong Chen, CEO of Internet software outfit BroadVision: "The Nirvana is that you are so near to your customers, you can satisfy bar no portion their needs. Even if you don't design the item yourself, you own the relationship."

    Amazon.com has three million relationships. It sells books online and now is moving into music (with videos probably next). Every time someone buys a engage on its Website, Amazon.com learns her tastes and suggests other titles she might enjoy. The more Amazon.com learns, the better it serves its customers; the better it serves its customers, the more loyal they become. About 60% are restate buyers.

    The Web is a supermall of mass customizers. You can drop music tracks on your own CDs (cductive.com); select from over a billion options of printed art, mats, and frames (artuframe.com); pick up stock picks geared to your goals (personalwealth.com); or design your own vitamins (acumins.com). And you can pick up bar no portion kinds of tailored data; NewsEdge, for example, will dispatch a customized newspaper to your PC.

    These companies want to withhold customers ecstatic by giving them a product that cannot live compared to a competitor's. Acumin, for instance, blends vitamins, herbs, and minerals per customers' instructions, compressing up to 95 ingredients into three to five pills. If a customer wants to start taking a unique supplement, bar no portion Acumin needs to carry out is add it to the blend.

    Acumin's products address what Pine calls customer sacrifice--the compromise they bar no portion design when they can't pick up exactly the product they want. CEO Brad Oberwager started the company two years ago, when his sister, who was undergoing a special cancer radiation treatment, couldn't find a multivitamin without iodine. (Her doctor had told her to avoid iodine.) "If someone would create a vitamin just for me, I would buy it," she told her brother. So he did.

    The Web will design that benevolent of response the norm. Sure, there are any number of ways for consumers to provide a company with information about their preferences--they can call, they can write, or, heck, they can even walk into the brick-and-mortar store. But the Web changes everything--the information arrives in a digitized shape ready for broadcast. Says i2 CEO Sanjiv Sidhu, "The Internet is bringing society into a culture of accelerate that has not really existed before." As unique middlemen customize orders for the masses, differentiating one company from its competitors will become tougher than ever. Responding to charge cuts or attribute improvements will continue to live important, but the key differentiator may live how quickly a company can serve a customer. Says Artuframe.com CEO Bill Lederer: "Mass customization is novel today. It will live common tomorrow." If he is right, the Web will wind up creating a offbeat competitive landscape, where companies temporarily connect to satisfy one customer's desires, then disband, then reconnect with other enterprises to satisfy a different order from a different customer.

    That's the vision anyway. For now, companies are struggling to bewitch the first steps toward mass customization. The ones that are already there possess been working on the process for years. Matthew Sigman is an executive at R.R. Donnelley & Sons, whose digital publishing trade prints textbooks customized by individual college professors. "The challenge," Sigman warns, "is that if you are making units of one, your margin for error is zero." Custom-fit jeans carry out foster with a money-back guarantee. Levi's can't afford for you not to enjoy them.



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