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00M-639 IBM astronomical Data Sales Mastery Test v1

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

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

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IBM IBM astronomical Data Sales

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

Brokerages foretell that IBM (NYSE:IBM) will report $18.77 billion in earnings for the existing fiscal quarter, in response to Zacks. five analysts abide 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 foretell 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 foretell 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) terminal posted its quarterly profits statistics on Tuesday, January 22nd. The know-how commerce suggested $4.87 profits per participate (EPS) for the quarter, beating the consensus rate of $four.eighty two through $0.05. The commerce 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 recur on equity of 68.sixty one%. The business’s income became down three.5% on a yr-over-yr basis. throughout the identical epoch terminal year, the firm posted $5.14 income per share.

IBM has been the matter 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 breathe cognizant on Thursday, October 18th. ValuEngine raised shares of IBM from a “promote” rating to a “hold” ranking in a research breathe cognizant 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 price goal on shares of IBM in a analysis file on Friday, December seventh. Three funding analysts abide rated the inventory with a sell score, eleven abide issued a grasp ranking and eight abide issued a buy ranking to the company. IBM perquisite 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 habitual volume of 5,224,408. IBM has a 1-12 months low of $105.ninety four and a 1-year lofty 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 breathe paid on Saturday, March 9th. investors of checklist on Friday, February 8th should breathe 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 relent of 4.58%. IBM’s dividend payout ratio (DPR) is perquisite now forty five.47%.

IBM introduced that its Board of directors has accepted a inventory buyback blueprint 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 locality on Wednesday, February 6th. The shares abide been bought at a benchmark fee of $135.67, for a total price 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 breathe accessed through this hyperlink. 0.17% of the inventory is at the jiffy owned via company insiders.

Institutional traders abide 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 totality over the period. Albion fiscal community UT elevated its stake in IBM by 1.5% in the third quarter. Albion fiscal neighborhood UT now owns 18,471 shares of the technology business’s stock valued at $2,793,000 after buying an extra 281 shares perquisite 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 passage 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 perquisite 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 totality over the length. Hedge funds and different institutional investors personal 61.97% of the company’s inventory.

IBM commerce Profile

overseas enterprise Machines agency operates as an integrated technology and features commerce global. Its Cognitive options segment presents Watson, a computing platform that interacts in language, strategies astronomical 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, commerce companionable utility, aptitude management solutions, and tailored commerce 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 expend of AI | killexams.com real Questions and Pass4sure dumps

In September 2018, IBM announced a brand current 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 pick on most advantageous patterns and passes this suggestions to the Db2 query optimizer for expend by passage 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 expend 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 nascence aimed toward zServer-based analytics applications. some of the first evident choices changed into zSystem performance monitoring and tuning. materiel 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 material 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, increase key efficiency warning signs (KPIs) and then feed the model results returned into the gadget to abide an effect on gadget configuration adjustments that can increase performance.

    The subsequent step changed into to invoke 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 capitalize 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 lead 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 existence 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 breathe accessed and develops a listing of candidate statistics access paths. These access paths can include indexes, desk scans, quite a few desk breathe allotment of methods and others. within the information warehouse and massive facts environments there are always further selections accessible. One of those is the existence of summary tables (on occasion called materialized question tables) that comprise pre-summarized or aggregated records, accordingly allowing Db2 to prevent re-aggregation processing. another alternative is the starjoin access path, common 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." charge 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 charge entry path, stores it in recollection (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 expend a graphical interface to build and exploit 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 breathe able to carry out first-rate analytics for your diverse facts stores you requisite 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 ripen team of workers nearing retirement?

    take into account additionally that a astronomical allotment of decreasing the overall charge of ownership of these programs is to net 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 account that it is regularly knotty 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 too breathe 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 amass and maintain SQL query textual content and entry course details, as well as genuine performance-linked traditional assistance similar to CPU time used, elapsed instances and effect 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 expend the mannequin scoring facts as input to its entry course option algorithm.

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

    This will too breathe certainly vital if you save data in part areas. for instance, many analytical queries in opposition t huge information require concurrent access to clear 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 maintain earnings records may additionally are looking to combination or summarize earnings by vicinity; therefore, the StoreLocation table should breathe used via some astronomical records queries. during this ambiance it is habitual to capture the dimension tables and duplicate them continually to the astronomical 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 astronomical records company analysts. From Db2's point of view, totality these queries are equal, and are forwarded to the Optimizer. however, in the case of operational queries and warehouse queries they may noiseless absolutely breathe directed to access the StoreLocation desk within the warehouse. even so, the query from the commerce analyst towards astronomical data tables should doubtless access the copy of the desk there. This results in a proliferations of odds entry paths, and more travail for the Optimizer. luckily, Db2 AI for z/OS can give the Optimizer the guidance it needs to get 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 warrant operation, an SQL commentary is passed to the Optimizer;
  • The Optimizer chooses the data access route; as the option 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 breathe 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 maintain this information in a mannequin;
  • because the volume of statistics in every model grows, the MLz Scoring service (which can too breathe achieved on a zIIP processor) analyzes the mannequin statistics and scores the habits;
  • all over the next bind, rebind, save together or explain, the Optimizer now has entry to the scoring for SQL models, and makes applicable adjustments to entry route decisions.
  • There are too 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 effect in Db2 edition 12 to increase the efficiency of analytical queries as well as operational queries and their associated purposes. This requires management attention, as you abide to determine that your commerce is prepared to consume these ML and AI conclusions. How will you measure the prices and advantages of the expend of machine researching? Which IT aid staff ought to breathe tasked to reviewing the effect 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 cognizant your statistics, its distribution, its integrity and your existing and proposed entry paths? this can determine the locality the DBAs expend their time in aiding analytics and operational utility efficiency.

    # # #

    Reference 1

    John Campbell, IBM Db2 unique EngineerFrom "IBM Db2 AI for z/OS: increase 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 totality articles via Lockwood Lyon


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

    IBM plans an even bigger propel into records crunching through opening a brand current 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 charge of huge records, pointed out in a web video seen with the aid of Bloomberg and later eliminated that the brand current focus will at terminal condominium “hundreds of americans” working basically with a free expertise called Spark.

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

    The expertise can travail with or change Hadoop, which has won traction in recent years with agencies relish Yahoo (YHOO) and facebook (FB) that expend it to shop and mode massive amounts of records. relish with a lot of know-how, what’s charged in statistics crunching alterations quickly as current 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 commerce has been public with its serve for the technology and has claimed that it will too breathe used to boost the performance of Hadoop.

    IBM has made information evaluation a astronomical a allotment of its earnings pitch, allotment 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 expend to dissect healthcare facts.

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

    by passage of helping Spark and attracting employees who know the passage to expend 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 current know-how to revitalize its business. huge information technology may well breathe a much bigger a allotment 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 leer across the learn-to-code industry — with the proliferation of bootcamps, MOOCs, and alternative learning options — I often miracle 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 passage 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 commerce factors that led us to focus on mastery-based learning.

    This is a memoir about how they built Launch School over the terminal 5 years and how their opinions around programming, teaching, and commerce 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 opportunity 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 travail on something together, but didn’t abide any concrete ideas. After months of watchful 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 halt totality LMSes. As they worked through the specifications and design, one thing became painfully obvious: they had no concept 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 net some sustain teaching real students. Now, I’d relish to reason 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 halt of 2012, they decided to give up their (extremely) lofty 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 halt totality LMSes).

    I participate this backstory because this root memoir will arrive back to influence many of their later decisions. It’s indispensable to remember: they didn’t descry an opportunity to get money and came into teaching programming as an exercise in learning about how to educate people.

    Side Note: they quickly dropped the LMS concept because they found out students don’t buy LMSes, and selling a current 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 breathe known as “coding bootcamps”. It was during this epoch that a few intrepid companies were starting to prove that you could net graduates a lofty 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 breathe 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 too grabbed beers with other local bootcamp founders, relish Roshan Choxi and Dave Paola, founders of Bloc.io. It felt relish something astronomical was about to happen in the industry and San Francisco was the epicenter.

    Meanwhile, Kevin and I continued executing their cohort-based courses. Their courses during this epoch 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 relish 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 concept seemed promising: using innovative online tools, they could teach minuscule live cohorts and ensure that everyone engaged with the material.

    In order to design out what topics to teach, they asked students what they would breathe interested in learning. Not surprisingly, they mentioned totality 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 teach them.

    The problems they encountered were immediate and obvious.

  • Student readiness levels elope the gamut. It’s impossible to teach 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 maintain pace with the lectures. About half the students stopped attending the live lectures after the first week. Though totality lectures were recorded, few made an endeavor to get up for lost time and instead elected to fade at their own pace. By the halt of the month-long course, only a few students were noiseless attending the live lectures.
  • The above two problems forced us early on to pick if they cared about students’ comprehension at the halt of courses. If they didn’t, the solution would breathe easier: they could just sell recorded videos and content for a fixed price and focus their energies on marketing the content. On the other hand, if they did custody about comprehension afterwards, we’d abide to find another teaching format because while the concept of live lectures with quizzes seemed first-rate in theory, in practice, most people don’t abide 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 attest up.

    These problems too forced us to reason difficult about who their audience was. If companies relish Dev Bootcamp were able to train people for lofty paying jobs, why couldn’t they carry out the same, if only they selected the perquisite 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 halt if they could find and train first-rate students? This line of thinking only made things more confusing, because if they maintain pushing on that logic, wouldn’t it breathe easier to just become a recruiting company? Why bother doing totality the difficult travail of trying to train unprepared people when they can just filter for the best? That seemed relish a more viable business, especially since totality the startup literature says to charge 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 loyal for most education institutions, a result of selection color as opposed to their incredible training methods. The choices in front of us were either to 1) design out a passage to get money and give up on making confident students actually understood the material, or 2) design out a passage to better train people for comprehension and not worry about optimizing for revenue.

    We made a few censorious 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 serve people, not only to get money for ourselves. After all, they had just quit lofty paying jobs to carry out something meaningful together. Helping employers didn’t look very meaningful to us personally and while they were ok with that being a side effect of producing powerful programmers, they didn’t want to incentivize ourselves to become a recruiting company.
  • We decided to not capture venture funding. Though it may abide been a bit early in their lifecycle to get that decision, they felt that training companies carry out not abide a significant viral first-mover advantage. Instead, the odds was in long-term reputation. Sure, it’d breathe viable to over-promise and over-hype the marketing in the short-term, but their hypothesis was that over time the want of results will trap up with the hype. They had decided to dedicate their entire thirties to this experiment, and they felt that this long-term mindset could breathe an odds in the education space. It’s effulgent that Shereef, Roshan, and Dave opted for the contrary 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 too 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 net them to purchase licenses in bulk. Instead, they took time to abide calls with every prospective student.

    By going the bootstrapped route, they decided on a low-burn long-term fiscal plan, which usually meant sacrificing marketing for curriculum development. In their hypothesis, there’s no rush to net to market, and it’s more indispensable to protect Launch School’s reputation by always doing “the perquisite thing for the student”. Venture-backed companies abide a “fail fast, fail often” mentality where growth rules above all. But in education, “failing” means negatively affecting students’ lives. They weren’t cozy with purposefully hurting even a minuscule group of students as allotment of the commerce 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 indispensable ways:

  • From synchronous to asynchronous (aka, self-paced). Instead of relying on live lectures that were sparsely attended, we’d hurry to recordings that students could watch at any time.
  • From one 1-month long course, they moved to 3 courses that would capture roughly 4 months in total. The courses would start from the ground up, teaching basic programming principles to start, then edifice up to web progress basics, and finally to totality the advanced concepts employers wanted.
  • These two changes made a huge dissimilarity and students understood this sequence of courses much better. Instead of zeal 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 price per course.

    Even with the current self-paced 3-course sequence, results noiseless varied widely. Some graduates got jobs that paid over $100k, and others who finished totality 3 courses said they didn’t learn a thing. They posted the $100k student on their testimonials, but it felt relish selection color 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 perquisite thing for students. So how can someone pay over $2,000 and expend over 4 months, and then enlighten they didn’t learn anything? Even if it was a minuscule number of students, that was noiseless a crushing result for us. They couldn’t let it fade 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 descry 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 recall HTTP from course 1?”, they’d enlighten something to the effect of “sure, kind of, but I went through that allotment pretty fast, and to breathe honest, it’s noiseless a itsy-bitsy fuzzy”. After seeing this over and over, they realized that they were missing a censorious component in their courses: assessments.

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

    Upton Sinclair once said, “It is difficult to net 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 meet 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 contest with their mastery-based values. They charged per course, so adding assessments would abide resulted in less revenue. The key lesson they took away from this observation was: breathe cognizant 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 fade 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 found 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 lofty paying jobs? You either abide to give up the lofty paying jobs or you abide to filter based on experience. If you only abide a 4 or 6 month timeframe, what topics carry out you cover and how carry out you get confident 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 concept that came up over and over in their research and discussions was operating for the *long-term*.

    If they capture a long-term perspective in their commerce operations, then it’d breathe viable to too capture a long-term perspective on their pedagogical approach for the curriculum. They can’t abide 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 breathe aligned. After realizing that, they made an indispensable decision: they decided to not only expend their thirties on this, but to expend the relaxation of their careers on this project. That seems histrionic and conjures up images of a sworn blood oath under a complete moon, but it wasn’t a difficult decision at totality and they made it fairly quickly and unceremoniously. That’s because 1) they didn’t abide any other first-rate ideas in the pipeline, 2) they believe that working on this problem will positively influence the world, 3) they believe in each other and don’t want to travail on part things, and 4) teaching online allows us to engage with a worldwide community of students, which brings a certain joy to the project. They didn’t abide any reason to stop, and they thought that by focusing on decades in the future, they could expend that perspective to their advantage.

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

    Just relish how a long-simmering programming bewilder may arrive into more focus as one spends more time digesting it, the education bewilder 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 commerce 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 current curriculum, and at the halt of 2015, they launched Launch School. They didn’t abide proof that this current curriculum would breathe good; it seemed perquisite based on their sustain and values, but since they just started, they didn’t abide any concrete results to show. They asked prospective students to trust the process and asked if learning fundamentals to mastery made intuitive sense. They didn’t carry out any market research and built the current curriculum based off of their own standards of excellence, so they weren’t confident how people would react. Would they leer at their proposal of learning indefinitely and then compare it with a 3-month bootcamp and laugh at us? Would they coincide with us that the issue with learning advanced topics and frameworks was totality about understanding fundamentals? The current marketplace was complete of hype about turning around a six-figure job after a few months. How would people receive the concept of potentially learning for a few years?

    Fortunately for us, some people chose to trust 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 resolve to demonstrate that there’s a quantitative dissimilarity 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 halt of 2016, they were able to capture some of their Launch School students and save them into an intense instructor-led program to descry if they could address the “last mile” problem. They created Capstone, a finishing program where students could apply their already-mastered fundamentals to more knotty engineering problems. They wanted to attest the world what’s viable when you capture 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 minuscule victories along the passage with many of their students saw their courses changed their lives, but teaching fundamentals for years too meant taking us farther away from concrete results. Now that they abide them, the results are astounding; descry for yourself.

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

    To address the question that initially triggered this article, I reason 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, commerce process innovation, niche audience segmentation, but no one at all look 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 abide been viable with venture funding. Had they taken investors’ money, we’d abide 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 indispensable attributes for educational companies: their pedagogy.

    Quality over data.

    I relish to reason I’m a data-driven person, but many operators act larger than they are. Most minuscule education companies are not operating at the scale of Amazon (the archetype for the soul-less numbers driven company), and yet they expend numbers to override values. Numbers and data are important, but you must abide some opinions on character regarding your craft that you can’t compromise on regardless of what the numbers say. Had they followed the difficult 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 too things they won’t do, no matter what the data says. For example, they just plainly refuse to “fail fast, and fail often” because it hurts people (also, they get enough honest mistakes that they don’t requisite a company philosophy to propel for more). I recall first hearing about this concept and thought “that’s a powerful hack for startup founders”. But when you’re on the receiving halt of this ideology as a customer, you reason “what a bunch of amateurs and assholes”. In order to carry out the perquisite thing, you abide to abide an view around quality. If you don’t yet abide one, it’s indispensable to hurry slowly and design 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 handle starting a commerce as a treasure hunt for revenue. In the course of running a business, many decisions arrive down to this choice: get money or improve quality. It seems counterintuitive, shouldn’t the higher character product get 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 get money and then design it out later (another venture-backed mantra: “fake it until you get 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 get 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 travail and doing something positive for the world and working with people I relish being around. Just as lofty salaries are actually not the halt goal for students at Launch School (they are a side effect of learning to mastery), revenue is not the halt goal of the commerce side of Launch School — it is a side effect 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 commerce Intelligence (BI) Tools of 2018 | killexams.com real questions and Pass4sure dumps

    Analytics Beyond Spreadsheets

    For many years, Microsoft outstrip and other spreadsheets were the tools of option for commerce professionals who were looking to visualize their data. But spreadsheets had their limits for many commerce intelligence (BI)-related tasks. Even today, trying to creating charts analyzing knotty datasets in outstrip can noiseless breathe frustrating. Sometimes you start with the wrong kind of data, for example, or you may not know how to exploit 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 requisite has arisen for effective tools that people of totality skill levels can expend to get sense of the wealth of information created by businesses every single day.

    Spreadsheets too Fall down when the data isn't well-structured or can't breathe sorted out in tidy rows and columns. And, if you abide millions of rows or very sparse matrices, then the data in a spreadsheet can breathe painful to enter and it can breathe difficult to visualize your data. Spreadsheets too abide 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 too breathe a problem, particularly if you abide exported graphics that requisite to breathe refreshed when the data changes. Finally, spreadsheets aren't first-rate 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 get expend of tables of numbers, they are really acting in different arenas with different purposes. A spreadsheet is first and foremost a passage to store and array calculations. While some spreadsheets can create very sophisticated mathematical models, at their core it is totality about the math more than the model itself.

    This is totality a long-winded passage of saw that when businesses expend a spreadsheet, they are actively sabotaging themselves and their aptitude to consistently net valuable insights from their data. BI tools are specficially designed to serve businesses better understand their data, and can prove to breathe a huge capitalize to those upgrading from what a limited spreadsheet can do.

    What Is commerce Intelligence?

    Defining BI is tricky. When you examine what it does and why companies expend it, it can start to sound vague and nebulous. After all, many different kinds of software proffer analytics features, and totality businesses want to improve. Understanding what a BI is or isn't can breathe unclear.

    BI is an umbrella term meant to cover totality 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 existence and operations. The ultimate goal is being able to increase 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 compress 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 technique 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 equable progression towards more in-depth erudition 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 knotty to breathe 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 certain operations or software buckets. This was the case unless IT took on the stint of integrating various silos, typically through painstaking and highly manual processes.

    While BI software noiseless covers a variety of software applications used to dissect raw data, today it usually refers to analytics for data mining, analytical processing, querying, reporting, and especially visualizing. The main dissimilarity between today's BI software and astronomical Data analytics is mostly scale. BI software handles data sizes typical for most organizations, from minuscule to large. astronomical 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 accepted half a decade or more ago required specialists, not just to expend but too to interpret the resulting data and conclusions. That led to an often inconvenient and fallible filter between the people who really needed to net and understand the business—the company decision 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 commerce whose data they were analyzing. That led to a focus on data the company didn't need, a misinterpretation of results, and often a string of "standard" reporting that analysts would elope on a scheduled basis instead of more ad hoc intelligence gathering and interpretation, which can breathe highly valuable in fast-moving situations.

    This problem has led to a growing current trend among current 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 breathe available and usable by anyone in the organization. Instead of requesting reports or queries through the IT or database departments, executives and decision 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 tool arsenal, but IT no longer acts as a bottleneck to every query and report request.

    As a result, users can capture odds of this distributed BI model. Key tools and censorious data abide moved from a centralized and difficult-to-access architecture to a decentralized model that merely requires access credentials and familiarity with current BI software. This results in a all current kind of analysis becoming available to the organization, namely, that of experienced, front-line commerce people who not only know what data they requisite but how they requisite to expend it.

    The emerging crop of BI tools totality travail difficult at developing front-end tools that are more intuitive and easier to expend than those of older generations—with varying degrees of success. However, that means a key criteria in any BI tool purchasing decision will breathe to evaluate who in the organization should access such tools and whether the tool is appropriately designed for that audience. Most BI vendors attest they're looking for their tool suites to become as ubiquitous and effortless to expend for commerce users as typical commerce collaboration tools or productivity suites, such as Microsoft Office. no one at all abide gotten quite that far yet in my estimation, but some are closer than others. To that end, these BI tool suites mind to focus on three core types of analytics: descriptive (what did happen), prescriptive (what should happen now), and predictive (what will happen later).

    What Is Data Visualization?

    In the context of BI software, data visualization is a posthaste and effective mode of transferring information from a machine to a human brain. The concept is to locality digital information into a visual context so that the analytic output can breathe quickly ingested by humans, often at a glance. If this sounds relish 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 reach more granular information on one or more aspects incorporated in the bigger picture. For example, current values can breathe 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 search artistic awards but instead are designed with role 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 leer impressive at first glance but, if your audience needs serve 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 proffer 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 too third-party and even free data visualization tools that can breathe 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 commerce analyst. But I too kept in humor the viewpoint of users who might abide 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 aim was to evaluate cloud versions alone, as I often carry out analysis on the waft 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 instance of this is Tableau Desktop, a favorite tool of Microsoft outstrip users who simply abide an affinity for the desktop tool (and who just hurry 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 tool over a web tool anyway." Again, I don't doubt Microsoft's claim but that does look 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 tool preference is a bit more personal than fact-based, but to each his own.

    Then there's Google Analytics, a pure cloud player. The tool is designed to dissect 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 found them, simply to let buyers know they exist. IBM Watson Analytics is one tool with the aptitude to extend to highly advanced features and was too one of the easiest to expend upfront. IBM Watson Analytics is well-suited for commerce analysts and for widespread data democratization because it requires little, if any, erudition of data science. Instead, it works well by using natural language and keywords to configuration queries, a characteristic that can get it valuable to practically anyone. It's highly intuitive, very powerful, and effortless to learn. Microsoft Power BI is a stout second as it, too, is powerful while too familiar, certainly to any of the millions of Microsoft commerce users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they totality abide their own pros and cons. We'll breathe 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 breathe a problem in the immediate future. However, for those involved with analyzing commerce processes as they happen, such as website performance metrics or customer behavior patterns, streaming data can breathe invaluable. Also, the Internet of Things (IoT) will drive this issue in the near future and get streaming data and streaming analytics a must-have feature. Many of these tools will abide to up their game accordingly so, unless you want to jump ship in a year or two, it's best to reason ahead when considering BI and the IoT.

    BI and astronomical Data

    Another locality in which self-service BI is taking off is in analyzing astronomical Data. This is a newer progress in the database space but it's driving tremendous growth and innovation. The denomination is an apt descriptor because astronomical Data generally refers to huge data sets that are simply too astronomical to breathe 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 companionable media tools (to denomination a few) that abide become so accepted over the terminal several years.

    Not only carry out these tools generate loads of current data, they too often generate a current kind 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 kind of data is weighty on text (even free-form text) while too containing more easily defined data, such as dates or credit card numbers. Examples of apps that generate this kind of data include the customer behavior-tracking tools you expend to descry 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 companionable 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 current information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data. current data storage innovations such as data lakes are emerging to resolve 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 astronomical Data analysis standards. The golden benchmark here is Hadoop, which is an open-source software framework that Apache specifically designed to query large data sets stored in a distributed vogue (meaning, in your data center, the cloud, or both). Not only does Hadoop let you query astronomical Data, it lets you simultaneously query both unstructured as well as traditional structured data. In other words, if you want to query totality of your commerce 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 effective to expend 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, totality advocate 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 expect customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

    Finding the perquisite BI Tool

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

    Getting the overall workflow down with these tools will capture some study and discussion with the people you'll breathe 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 abide 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 wait there.

    In recent years, companies abide been taking odds 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 indispensable to get confident that your employees actually know how to expend these BI platforms so that you can get confident your investment was worthwhile. There are many ways of approaching this, but using the perquisite online learning platform might breathe a first-rate locality to start looking.

    Given the wide price sweep of these products, you should segment your analytics needs before you get 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 get it effortless to net started. Plus, they mind to abide large ecosystems of add-ons and partners that can breathe 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, totality three products received their Editors' option award. Tableau Desktop may abide a astronomical price tag depending on which version you pick 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 endeavor to it. Microsoft Power BI and Tableau Desktop too abide large and growing collections of data connectors, and both Microsoft and Tableau abide their own sizable communities of users that are vocal about their wants and needs. This can carry a lot of weight with the vendors' progress teams so it's a first-rate concept to expend some time looking through those community forums to net an concept where these companies are headed.

  • Pros: Extremely user-friendly. grotesque automatic report generation. Impressive advocate availability.

    Cons: Automated reports can quickly become defaults. abrupt learning curve that might fuddle beginners.

    Bottom Line: Zoho Reports is a solid option for common commerce users who might not breathe knowledgeable in analytics software. It's too available at an attractive price.

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

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

    Bottom Line: IBM Watson Analytics is an exceptional commerce intelligence (BI) app that offers a stout 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' option 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' option veneration for its impressive usability, top-notch data visualization capabilities, and superior compatibility with other Microsoft Office products.

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

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

    Bottom Line: Tableau Desktop is one of the most ripen 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 breathe too difficult for self-service commerce intelligence (BI). Analytics process noiseless needs to breathe ironed out. Natural language capability can breathe limited.

    Bottom Line: Sisense is a complete platform that should breathe accepted for experienced BI users. It may Fall short for beginners, however.

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

    Cons: User interface is not intuitive. abrupt learning curve. Unwelcoming to current analysts.

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

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

    Cons: Customer advocate has passage too much automation. Focus on marketing and advertising can breathe 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 abrupt learning curve but it is an awesome commerce intelligence tool.

    Read Review
  • Pros: Designed with common commerce users in mind. Solid recur on investment.

    Cons: The data you can expend 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 get it an attractive offering.

    Read Review
  • Pros: Real-time analytics for Internet of Things (IoT) and streaming data features. Massive ecosystem with ample extenders. Responsive pages get 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 commerce already uses SAP's HANA database platform or some of its other back-end commerce platforms, then SAP Analytics Cloud is a powerful, well-priced choice. But breathe warned that there's a abrupt learning curve and a notable dependence on other SAP products for complete functionality.

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

    Cons: Poorly designed user interface. abrupt learning curve.

    Bottom Line: Chartio excels at edifice a powerful analytics platform that experienced commerce intelligence (BI) users will appreciate. Those current 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 minuscule teams.

    Bottom Line: Looker is a powerful self-service commerce intelligence (BI) tool that can serve unify SQL and astronomical Data management across your enterprise.

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

    Cons: knotty pricing is a deterrent.

    Bottom Line: Qlik Sense Enterprise Server is a self-service commerce intelligence (BI) tool that delivers the best collection of user access roles among the BI tools they tested, and too 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 visitation available. Training webinars can breathe costly.

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

    Read Review
  • Pros: Very effortless to net started. Nice team management and collaboration features.

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

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

    Read Review
  • Pros: Excellent analytical advocate for Intuit QuickBooks. Very effortless setup.

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

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

    Read Review

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

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

    (FORTUNE Magazine) – A taciturn revolution is stirring in the passage 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 crop to meet your body. But you can too buy pills with the exact blend of vitamins, minerals, and herbs that you like, glasses molded to meet your kisser 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 fiscal profile, or a night at a hotel where every employee knows your favorite wine. And if your child does not relish any of Mattel's 125 different Barbie dolls, she will soon breathe 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 nascence 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 breathe the organizing principle of commerce in the next century, just as mass production was the organizing principle in this one.

    The two philosophies couldn't clash more. Mass producers dictate a one-to-many relationship, while mass customizers require perennial dialogue with customers. Mass production is cost-efficient. But mass customization is a elastic 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 complete expend of cutting-edge technology.

    A all list of technological advances that get customization viable is finally in place. Computer-controlled factory materiel and industrial robots get it easier to quickly readjust assembly lines. The proliferation of bar-code scanners makes it viable to track virtually every allotment and product. Databases now store trillions of bytes of information, including individual customers' predilections for everything from cottage cheese to suede boots. Digital printers get 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 book Mass Customization: "Anything you can digitize, you can customize." The Net makes it effortless for companies to hurry data from an online order configuration to the factory floor. The Net makes it effortless for manufacturing types to communicate with marketers. Most of all, the Net makes it effortless 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 too often the best passage for a customer to learn which company has the most to proffer him--if he's not ecstatic with one company's wares, nearly consummate 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 rational 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 abide actually been ordered. Everyone from Compaq to IBM is struggling to copy Dell's model. And for first-rate reason. Dell passed IBM terminal quarter to claim 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 miracle Michael Dell has become the poster boy of the current economy. As Pine says, "The closest person they abide 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 perquisite parts and products are delivered to the perquisite locality at the perquisite 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 requisite it. When an order comes in, the data collected are quickly parsed out--to suppliers that requisite to rush over a shipment of difficult drives, say, or to the factory floor, where assemblers save 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 abide no waste."

    The company has been propelled by this thinking ever since Michael Dell started selling PCs from his college dorm room 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 requisite 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 flux of character information.

    It's not much of a astound that a leading tech company relish 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. capture Mattel. Starting by October, girls will breathe able to log on to barbie.com and design their own friend of Barbie's. They will breathe able to pick the doll's skin tone, eye color, hairdo, hair color, clothes, accessories, and denomination (6,000 permutations will breathe 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 breathe next to impossible. Mattel does get specific versions of Barbie for customers such as Toys "R" Us, and the company customizes cheerleader Barbies for universities. But this will breathe the first time Mattel produces Barbie dolls in lots of one. relish Dell, Mattel must expend high-end manufacturing and logistics software to ensure that the order data on its Website are distributed to the parts of the company that requisite them. The only real concern is whether Mattel's systems can handle the expected exact in a timely fashion. perquisite 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 net a custom-ordered BMW.

    Nevertheless, Parducci is pumped. "Personalization is a dream they abide 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 allotment 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 first-rate 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 vogue designer or creating a personality is "through the roof."

    Levi Strauss too likes giving customers the haphazard to play vogue 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 proffer more options and will feature men's jeans as well.

    With the serve 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 breathe measured. They will try on a modest pair of test-drive jeans to get confident they relish the meet 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 race to add more choices. "It is censorious 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 proffer fewer components totality the time." To these two, mass customization isn't about boundless choices but about offering a well number of benchmark parts that can breathe mixed and matched in thousands of ways. That gives customers the illusion of boundless option 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 send him promotional offers. And customers who design their own jeans get the consummate focus group; Levi's can apply what it learns from them to the jeans it mass-produces for the relaxation 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 breathe 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. terminal year some MIT commerce students proposed a similar concept for a custom-made bra company dubbed consummate Underwear.

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

    Thirty years ago auto manufacturers were, effectively, mass customizers. People would expend 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 itsy-bitsy more than a few benchmark 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 look 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 get better expend of its customer database to net more Americans to custom-order. BMW dealers save about $450 in inventory costs on every such order. Reinhard Fischer, head of logistics for BMW of North America, says, "The astronomical battle is to capture cost out of the distribution chain. The best passage 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 requisite a current car. Some dealers now convoke customers a few months before their leases are up to descry whether they'd relish to custom-order their next car. Soon, however, customers will breathe able to configure their own car online and send that info to a dealer. Fischer can even descry a day when the Website will proffer data about vehicles sailing on ships from Germany, so that people can descry whether a car matching their preferences is already on the way. That does, of course, raise the question, Why not send the requests directly to BMW, circumventing dealers altogether? Says Fischer: "We don't want to liquidate their role, but maybe they should abide 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 kisser 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 mind to reason of automation as a process that eliminates the requisite for human interaction, mass customization makes the relationship with customers more indispensable 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 breathe cleaned (recent ones in its labs include flutes and goose down) or visits its customers' premises to dissect 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 maintain coming back are using software to personalize your experience. totality 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 net a three-second decision 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 get a quick decision.

    The bank too uses similar software in its small-business lending unit. According to vice chairman Terri Dial, Wells Fargo used to gyrate away lots of qualified minuscule businesses--the loans were too minuscule 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 deny 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 net out of that line of business."

    She'd better maintain investing. Combine the software that enables customization with the ubiquity of the Web, and you net a situation that threatens Wells' very existence. If consumers grow accustomed to designing their own products, will they trust brand-name manufacturers and service providers or will they gyrate to a current kind of middleman? candid Shlier, a director of research at the Gartner Group in Stamford, Conn., sees disintermediaries emerging totality over the Net to serve people sift through the thousands of choices presented to them. In fiscal services, he suggests, there is "a current role for a trusted adviser, maybe someone who doesn't own any banks."

    Shlier's middleman sounds a lot relish 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 inveigle 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 price and fit? Elaborates Pehong Chen, CEO of Internet software outfit BroadVision: "The Nirvana is that you are so close to your customers, you can meet totality their needs. Even if you don't get the item yourself, you own the relationship."

    Amazon.com has three million relationships. It sells books online and now is pathetic into music (with videos probably next). Every time someone buys a book 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 iterate buyers.

    The Web is a supermall of mass customizers. You can drop music tracks on your own CDs (cductive.com); pick from over a billion options of printed art, mats, and frames (artuframe.com); net stock picks geared to your goals (personalwealth.com); or get your own vitamins (acumins.com). And you can net totality kinds of tailored data; NewsEdge, for example, will send a customized newspaper to your PC.

    These companies want to maintain customers ecstatic by giving them a product that cannot breathe 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 current supplement, totality Acumin needs to carry out is add it to the blend.

    Acumin's products address what Pine calls customer sacrifice--the compromise they totality get when they can't net 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 get that kind 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 configuration ready for broadcast. Says i2 CEO Sanjiv Sidhu, "The Internet is bringing society into a culture of hasten that has not really existed before." As current middlemen customize orders for the masses, differentiating one company from its competitors will become tougher than ever. Responding to price cuts or character improvements will continue to breathe important, but the key differentiator may breathe how quickly a company can serve a customer. Says Artuframe.com CEO Bill Lederer: "Mass customization is novel today. It will breathe common tomorrow." If he is right, the Web will wind up creating a atypical competitive landscape, where companies temporarily connect to meet one customer's desires, then disband, then reconnect with other enterprises to meet a different order from a different customer.

    That's the vision anyway. For now, companies are struggling to capture the first steps toward mass customization. The ones that are already there abide been working on the process for years. Matthew Sigman is an executive at R.R. Donnelley & Sons, whose digital publishing commerce 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 arrive with a money-back guarantee. Levi's can't afford for you not to relish them.



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