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00M-639 IBM gargantuan Data Sales Mastery Test v1

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

Test Code : 00M-639
Test cognomen : IBM gargantuan Data Sales Mastery Test v1
Vendor cognomen : IBM
braindumps : 51 existent Questions

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IBM IBM gargantuan Data Sales

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

Brokerages predict that IBM (NYSE:IBM) will report $18.77 billion in earnings for the existing fiscal quarter, in response to Zacks. five analysts believe 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 very 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 predict 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 predict 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 tolerable commonplace based on a survey of sell-facet analysts that cowl IBM.

IBM (NYSE:IBM) last posted its quarterly profits statistics on Tuesday, January 22nd. The know-how business suggested $4.87 profits per share (EPS) for the quarter, beating the consensus assay of $four.eighty two through $0.05. The business 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 recrudesce on equity of 68.sixty one%. The business’s income became down three.5% on a yr-over-yr basis. throughout the very age last year, the difficult posted $5.14 income per share.

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

IBM stock traded down $0.26 on Monday, hitting $137.27. 1,202,955 shares of the enterprise’s stock traded fingers, compared to its 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 live paid on Saturday, March 9th. investors of checklist on Friday, February 8th should live given a $1.57 dividend. The ex-dividend date of this dividend is Thursday, February seventh. This represents a $6.28 annualized dividend and a dividend bow of 4.58%. IBM’s dividend payout ratio (DPR) is privilege now forty five.47%.

IBM introduced that its Board of directors has accepted a inventory buyback strategy 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 region on Wednesday, February 6th. The shares believe been bought at a touchstone fee of $135.67, for a total charge of $780,645.18. Following the transaction, the insider now owns 23,117 shares in the enterprise, valued at about $3,136,283.39. The transaction become disclosed in a doc filed with the SEC, which can live accessed through this hyperlink. 0.17% of the inventory is at the flash owned via company insiders.

Institutional traders believe 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 financial neighborhood UT now owns 18,471 shares of the technology business’s stock valued at $2,793,000 after buying an extra 281 shares privilege through the length. Paloma companions administration Co improved its stake in IBM through 127.4% in the third quarter. Paloma companions administration Co now owns 1,453 shares of the expertise business’s stock valued at $220,000 after buying an additional 6,757 shares during the length. Crossvault Capital administration LLC elevated its stake in IBM by course of 12.four% within the third quarter. Crossvault Capital administration LLC now owns 7,seven-hundred shares of the technology enterprise’s inventory valued at $1,164,000 after purchasing an extra 850 shares privilege through the length. at last, Edmp Inc. elevated its stake in IBM by using 2.3% within the 4th quarter. Edmp Inc. now owns eleven,032 shares of the technology business’s inventory valued at $1,254,000 after purchasing an extra 243 shares totality over the length. Hedge funds and different institutional investors personal 61.97% of the company’s inventory.

IBM business Profile

overseas enterprise Machines agency operates as an integrated technology and features business global. Its Cognitive options segment presents Watson, a computing platform that interacts in language, strategies gargantuan 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, business convivial utility, skill management solutions, and tailored business 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 utilize of AI | killexams.com existent Questions and Pass4sure dumps

In September 2018, IBM announced a brand new 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 utilize by course 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 pair 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 utilize 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. outfit management Facility (SMF) statistics that are immediately generated with the aid of the working gadget deliver the raw records for gadget resource consumption reminiscent of relevant 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 vicissitude foreseen with the aid of humans, extend key efficiency warning signs (KPIs) and then feed the model results returned into the gadget to believe an sequel on gadget configuration adjustments that can extend 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 concurrent DBAs in a Db2 environment. In modern-day IT world they should sheperd one or extra great 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 being considering the origins of the database, and DBAs are continually tasked with this as neatly.

    The heart of query route evaluation in Db2 is the Optimizer. It accepts SQL statements from purposes, verifies authority to access the facts, studies the locations of the objects to live accessed and develops a listing of candidate statistics access paths. These access paths can include indexes, desk scans, quite a few desk live portion of methods and others. within the information warehouse and massive facts environments there are always further selections accessible. One of those is the being of summary tables (on occasion called materialized question tables) that comprise pre-summarized or aggregated records, accordingly allowing Db2 to avert 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." pervade 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 pervade entry path, stores it in reminiscence (and, optionally, within the Db2 directory) and starts off entry course execution.

    big records and statistics warehouse operations now consist of application suites that enable the enterprise analyst to utilize 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 live able to Do superior 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 mature team of workers nearing retirement?

    take into account additionally that a gargantuan portion of decreasing the overall pervade of ownership of these programs is to gain Db2 functions to speed 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 deem that it is regularly intricate to even identify which functions might improvement from performance tuning, one strategy is to automate the detection and correction of tuning issues. here is where desktop researching and synthetic intelligence can likewise live used to incredible effect.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 version 12 on z/OS uses the computer researching amenities outlined above to amass and maintain SQL query textual content and entry course details, as well as genuine performance-linked old-fashioned assistance similar to CPU time used, elapsed instances and sequel 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 utilize the mannequin scoring facts as input to its entry course option algorithm.

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

    This will likewise live certainly vital if you redeem data in several areas. for instance, many analytical queries in opposition t huge information require concurrent access to several 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 live used via some gargantuan records queries. during this ambiance it is habitual to Take the dimension tables and duplicate them continually to the gargantuan 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 gargantuan 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 live directed to access the StoreLocation desk within the warehouse. even so, the query from the business analyst towards gargantuan data tables should doubtless access the copy of the desk there. This results in a proliferations of advantage entry paths, and more travail for the Optimizer. luckily, Db2 AI for z/OS can give the Optimizer the guidance it needs to accomplish 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 preference is made, Db2 AI captures the SQL syntax, entry course alternative and query performance data (CPU used, and so forth.) and passes it to a "learning assignment";
  • The researching assignment, which can live finished on a zIIP processor (a non-familiar-goal CPU core that does not factor into utility licensing costs), interfaces with the laptop getting to know software (MLz mannequin functions) to maintain this information in a mannequin;
  • because the volume of statistics in every model grows, the MLz Scoring service (which can likewise live achieved on a zIIP processor) analyzes the mannequin statistics and scores the habits;
  • all over the next bind, rebind, Put together or explain, the Optimizer now has entry to the scoring for SQL models, and makes applicable adjustments to entry route decisions.
  • There are likewise numerous consumer interfaces that give the administrator visibility to the repute of the collected SQL statement performance statistics and mannequin scoring.

    summary

    IBM's laptop getting to know for zOS (MLz) offering is getting used to exquisite sequel in Db2 edition 12 to extend the efficiency of analytical queries as well as operational queries and their associated purposes. This requires management attention, as you believe to determine that your business is prepared to consume these ML and AI conclusions. How will you measure the prices and advantages of the utilize of machine researching? Which IT aid staff ought to live tasked to reviewing the sequel 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 conscious your statistics, its distribution, its integrity and your existing and proposed entry paths? this can determine the region the DBAs disburse their time in aiding analytics and operational utility efficiency.

    # # #

    Reference 1

    John Campbell, IBM Db2 unique EngineerFrom "IBM Db2 AI for z/OS: extend 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 new huge records know-how | killexams.com existent Questions and Pass4sure dumps

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

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

    The expertise can travail with or change Hadoop, which has won traction in recent years with agencies fancy Yahoo (YHOO) and facebook (FB) that utilize it to shop and course massive amounts of records. fancy with a lot of know-how, what’s fiery in statistics crunching alterations quickly as new 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 business has been public with its encourage for the technology and has claimed that it will likewise live used to boost the performance of Hadoop.

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

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

    by course of helping Spark and attracting employees who know the course to utilize 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 new know-how to revitalize its business. huge information technology may well live a much bigger a portion of the plan.

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


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    IBM gargantuan Data Sales Mastery Test v1

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

    As I glare 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 course 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 business factors that led us to focus on mastery-based learning.

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

    The Backstory

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

    Of course as programmers, the first thing they set out to Do was to build a revolutionary Learning Management System (LMS) that would discontinue totality LMSes. As they worked through the specifications and design, one thing became painfully obvious: they had no strategy what they were doing because neither of us had any deep savor with teaching or education. So naturally, before they could build a LMS, they had to gain some savor teaching existent students. Now, I’d fancy to contemplate that we’re both pretty well-rounded people with a lot of interests, but they both really only had one skill that could attract students: programming. Towards the discontinue 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 discontinue totality LMSes).

    I share this backstory because this origin tale will compass back to influence many of their later decisions. It’s distinguished to remember: they didn’t contemplate an opening to accomplish money and came into teaching programming as an exercise in learning about how to educate people.

    Side Note: they quickly dropped the LMS strategy because they found out students don’t buy LMSes, and selling a new LMS to great 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 very time as many other companies, who later collectively came to live known as “coding bootcamps”. It was during this age that a few intrepid companies were starting to prove that you could gain 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 live based out San Francisco at the time and met with Shereef Bishay, founder of Dev Bootcamp, in their Chinatown office. Shereef became interested in what Kevin and I were doing and offered a partnership: they could roll their courses under the Dev Bootcamp brand and become their “preparatory” program. Because of their initial success, Dev Bootcamp started attracting a larger variety of students and many of their applicants lacked readiness. Not being interested in working for someone else, they declined. Besides meeting Shereef, I likewise grabbed beers with other local bootcamp founders, fancy Roshan Choxi and Dave Paola, founders of Bloc.io. It felt fancy something gargantuan 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 age 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 Do 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 fancy a much better savor than a typical college lecture, where you could sulk in the back of a great classroom and not ever engage with the instructor. The strategy seemed promising: using innovative online tools, they could drill wee live cohorts and ensure that everyone engaged with the material.

    In order to pattern out what topics to teach, they asked students what they would live 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 drill them.

    The problems they encountered were immediate and obvious.

  • Student readiness levels speed the gamut. It’s impossible to drill 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 exertion to accomplish up for lost time and instead elected to proceed at their own pace. By the discontinue 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 discontinue of courses. If they didn’t, the solution would live easier: they could just sell recorded videos and content for a fixed charge and focus their energies on marketing the content. On the other hand, if they did pervade about comprehension afterwards, we’d believe to find another teaching format because while the strategy of live lectures with quizzes seemed superior in theory, in practice, most people don’t believe the discipline to finish a rigorous course. And without the threat of withholding a credential, they couldn’t Do anything to constrain people to exhibit up.

    These problems likewise forced us to contemplate difficult about who their audience was. If companies fancy Dev Bootcamp were able to train people for lofty paying jobs, why couldn’t they Do the same, if only they selected the privilege students? My previous savor 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 discontinue if they could find and train superior students? This line of thinking only made things more confusing, because if they maintain pushing on that logic, wouldn’t it live 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 fancy a more viable business, especially since totality the startup literature says to pervade 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 privilege for most education institutions, a result of selection color as opposed to their astounding training methods. The choices in front of us were either to 1) pattern out a course to accomplish money and give up on making confident students actually understood the material, or 2) pattern out a course 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 possible revenue source, it brought clarity to what they were hypothetical to do. One of the things they wanted to Do was to encourage people, not only to accomplish money for ourselves. After all, they had just quit lofty paying jobs to Do something meaningful together. Helping employers didn’t appear very meaningful to us personally and while they were ok with that being a side sequel of producing powerful programmers, they didn’t want to incentivize ourselves to become a recruiting company.
  • We decided to not Take venture funding. Though it may believe been a bit early in their lifecycle to accomplish that decision, they felt that training companies Do not believe a significant viral first-mover advantage. Instead, the advantage was in long-term reputation. Sure, it’d live possible to over-promise and over-hype the marketing in the short-term, but their hypothesis was that over time the requisite of results will tangle up with the hype. They had decided to dedicate their entire thirties to this experiment, and they felt that this long-term mindset could live an advantage in the education space. It’s inquisitive 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 likewise meant we’re a B2C company and not a B2B company. This had implications to their processes. For example, they stopped doing sales calls to employers to try to gain them to purchase licenses in bulk. Instead, they took time to believe calls with every prospective student.

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

  • From synchronous to asynchronous (aka, self-paced). Instead of relying on live lectures that were sparsely attended, we’d travel to recordings that students could watch at any time.
  • From one 1-month long course, they moved to 3 courses that would Take roughly 4 months in total. The courses would start from the ground up, teaching basic programming principles to start, then pile up to web progress basics, and finally to totality the advanced concepts employers wanted.
  • These two changes made a huge unlikeness and students understood this sequence of courses much better. Instead of sentiment overwhelmed in the first week, students could complete lectures and assignments on their own schedule. They didn’t give too much thought to the pricing structure and continued to sell the courses at a fixed charge per course.

    Even with the new 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 fancy selection color and not existent 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 gross point of charging students and forgoing funding was so they can align ourselves with students and Do the privilege thing for students. So how can someone pay over $2,000 and disburse over 4 months, and then express they didn’t learn anything? Even if it was a wee number of students, that was noiseless a crushing result for us. They couldn’t let it proceed 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 contemplate 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 Do with intellectual ability, but everything Do with understanding of prerequisite knowledge. When they asked “don’t you remember HTTP from course 1?”, they’d express something to the sequel of “sure, kind of, but I went through that portion pretty fast, and to live honest, it’s noiseless a diminutive 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 believe known for centuries: you must believe some test of mastery to demonstrate comprehension.

    Upton Sinclair once said, “It is difficult to gain a man to understand something, when his salary depends on his not understanding it.” They fell into this trap by not thinking carefully about how their pricing suitable with their pedagogy. They never seriously thought about adding rigorous assessments because it meant that less students would enroll in and pay for subsequent courses. They were financially incentivizing ourselves to usher students to subsequent courses without esteem to mastery, which is in direct fight with their mastery-based values. They charged per course, so adding assessments would believe resulted in less revenue. The key lesson they took away from this observation was: live conscious 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 proceed 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 Do you reconcile that goal with the covet to drive people to lofty paying jobs? You either believe to give up the lofty paying jobs or you believe to filter based on experience. If you only believe a 4 or 6 month timeframe, what topics Do you cover and how Do you accomplish 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 strategy that came up over and over in their research and discussions was operating for the *long-term*.

    If they Take a long-term perspective in their business operations, then it’d live possible to likewise Take a long-term perspective on their pedagogical approach for the curriculum. They can’t believe a company that’s focused on chasing quarterly revenue results and reconcile that with a long-term curriculum. The company’s vision and the pedagogy must live aligned. After realizing that, they made an distinguished decision: they decided to not only disburse their thirties on this, but to disburse the repose of their careers on this project. That seems theatrical and conjures up images of a sworn blood oath under a replete moon, but it wasn’t a difficult conclusion at totality and they made it fairly quickly and unceremoniously. That’s because 1) they didn’t believe any other superior ideas in the pipeline, 2) they believe that working on this problem will positively move the world, 3) they believe in each other and don’t want to travail on sever things, and 4) teaching online allows us to engage with a worldwide community of students, which brings a confident joy to the project. They didn’t believe any reason to stop, and they thought that by focusing on decades in the future, they could utilize that perspective to their advantage.

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

    Just fancy how a long-simmering programming confound may compass into more focus as one spends more time digesting it, the education confound 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 business 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 new curriculum, and at the discontinue of 2015, they launched Launch School. They didn’t believe proof that this new curriculum would live good; it seemed privilege based on their savor and values, but since they just started, they didn’t believe any concrete results to show. They asked prospective students to dependence the process and asked if learning fundamentals to mastery made intuitive sense. They didn’t Do any market research and built the new curriculum based off of their own standards of excellence, so they weren’t confident how people would react. Would they glare 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 replete of hype about turning around a six-figure job after a few months. How would people receive the strategy of potentially learning for a few years?

    Fortunately for us, some people chose to dependence 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 unravel to demonstrate that there’s a quantitative unlikeness 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 discontinue of 2016, they were able to Take some of their Launch School students and Put them into an violent instructor-led program to contemplate if they could address the “last mile” problem. They created Capstone, a finishing program where students could apply their already-mastered fundamentals to more intricate engineering problems. They wanted to exhibit the world what’s possible when you Take 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 wee victories along the course with many of their students saying their courses changed their lives, but teaching fundamentals for years likewise meant taking us farther away from concrete results. Now that they believe them, the results are astounding; contemplate for yourself.

    Why doesn’t anyone else Do Mastery-based Learning?

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

    We’re bootstrapped.

    Other programs focus on financing and pricing innovation, partnerships, scholarships, marketing, government sponsorships, accreditation/credentialism, business process innovation, niche audience segmentation, but not any appear interested in pedagogical innovation. I believe that they were able to focus squarely on pedagogy because they kept expanding their time horizon, which wouldn’t believe been possible with venture funding. Had they taken investors’ money, we’d believe 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 distinguished attributes for educational companies: their pedagogy.

    Quality over data.

    I fancy to contemplate I’m a data-driven person, but many operators act larger than they are. Most wee education companies are not operating at the scale of Amazon (the archetype for the soul-less numbers driven company), and yet they utilize numbers to override values. Numbers and data are important, but you must believe some opinions on trait 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 likewise things they won’t do, no matter what the data says. For example, they just plainly decline to “fail fast, and fail often” because it hurts people (also, they accomplish enough honest mistakes that they don’t requisite a company philosophy to propel for more). I remember first hearing about this concept and thought “that’s a powerful hack for startup founders”. But when you’re on the receiving discontinue of this ideology as a customer, you contemplate “what a bunch of amateurs and assholes”. In order to Do the privilege thing, you believe to believe an feeling around quality. If you don’t yet believe one, it’s distinguished to travel slowly and pattern 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 deal starting a business as a treasure hunt for revenue. In the course of running a business, many decisions compass down to this choice: accomplish money or improve quality. It seems counterintuitive, shouldn’t the higher trait product accomplish more money? In industries where results are not obvious or delayed by months and years, it’s very possible to over-promise and lead with marketing. In such industries, it’s much easier to first accomplish money and then pattern it out later (another venture-backed mantra: “fake it until you accomplish 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 accomplish 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 discontinue goal for students at Launch School (they are a side sequel of learning to mastery), revenue is not the discontinue goal of the business side of Launch School — it is a side sequel 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 business Intelligence (BI) Tools of 2018 | killexams.com existent questions and Pass4sure dumps

    Analytics Beyond Spreadsheets

    For many years, Microsoft surpass and other spreadsheets were the tools of preference for business professionals who were looking to visualize their data. But spreadsheets had their limits for many business intelligence (BI)-related tasks. Even today, trying to creating charts analyzing intricate datasets in surpass can noiseless live 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 efficacious tools that people of totality skill levels can utilize to accomplish sense of the wealth of information created by businesses every single day.

    Spreadsheets likewise topple down when the data isn't well-structured or can't live sorted out in elegant rows and columns. And, if you believe millions of rows or very sparse matrices, then the data in a spreadsheet can live painful to enter and it can live difficult to visualize your data. Spreadsheets likewise believe 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 very spreadsheet.

    A spreadsheet containing up-to-the-minute data can likewise live a problem, particularly if you believe exported graphics that requisite to live refreshed when the data changes. Finally, spreadsheets aren't superior 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 accomplish utilize of tables of numbers, they are really acting in different arenas with different purposes. A spreadsheet is first and foremost a course to store and display 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 course of saying that when businesses utilize a spreadsheet, they are actively sabotaging themselves and their skill to consistently gain valuable insights from their data. BI tools are specficially designed to encourage businesses better understand their data, and can prove to live a huge capitalize to those upgrading from what a limited spreadsheet can do.

    What Is business Intelligence?

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

    BI is an umbrella term meant to cover totality of the activities necessary for a company to spin 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 being and operations. The ultimate goal is being able to extend 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 shrink 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 Do 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 craft 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 uniform progression towards more in-depth erudition about the business, competitors, customers, industry, market, and suppliers, to cognomen just a few possible metric targets. But as businesses grow and their information stores balloon, the capturing, storing, and organizing of information becomes too great and intricate to live entirely handled by mere humans. Early efforts to Do 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 confident operations or software buckets. This was the case unless IT took on the task of integrating various silos, typically through painstaking and highly manual processes.

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

    Self-Service BI and Data Democratization

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

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

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

    The emerging crop of BI tools totality travail difficult at developing front-end tools that are more intuitive and easier to utilize than those of older generations—with varying degrees of success. However, that means a key criteria in any BI instrument purchasing conclusion will live to evaluate who in the organization should access such tools and whether the instrument is appropriately designed for that audience. Most BI vendors indicate they're looking for their instrument suites to become as ubiquitous and effortless to utilize for business users as typical business collaboration tools or productivity suites, such as Microsoft Office. not any believe gotten quite that far yet in my estimation, but some are closer than others. To that end, these BI instrument suites attend 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 rapidly and efficacious course of transferring information from a machine to a human brain. The strategy is to region digital information into a visual context so that the analytic output can live quickly ingested by humans, often at a glance. If this sounds fancy 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 compass more granular information on one or more aspects incorporated in the bigger picture. For example, new values can live added that will change the visualization on the fly, or the visualization is actually built on rapidly changing data that can spin a static visual into an animation or a dashboard.

    The best visualizations Do not seek 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 glare impressive at first glance but, if your audience needs encourage 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 present more options than others so, if advanced visuals are key to your BI process, then you'll want to closely examine these tools. There are likewise third-party and even free data visualization tools that can live used on top of your BI software for even more options.

    Products and Testing

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

    My direct was to evaluate cloud versions alone, as I often Do analysis on the hover or at least on a variety of machines, as Do legions of other analysts. But, in some cases, it was necessary to evaluate a desktop version as well or instead of the cloud version. One illustration of this is Tableau Desktop, a favorite instrument of Microsoft surpass users who simply believe an affinity for the desktop instrument (and who just travel to the cloud long enough to share 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 instrument over a web instrument anyway." Again, I don't doubt Microsoft's title but that does appear eerie 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 instrument preference is a bit more personal than fact-based, but to each his own.

    Then there's Google Analytics, a simple cloud player. The instrument is designed to anatomize 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 instrument with the skill to extend to highly advanced features and was likewise one of the easiest to utilize upfront. IBM Watson Analytics is well-suited for business 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 contour queries, a characteristic that can accomplish it valuable to practically anyone. It's highly intuitive, very powerful, and effortless to learn. Microsoft Power BI is a strong second as it, too, is powerful while likewise familiar, certainly to any of the millions of Microsoft business users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they totality believe their own pros and cons. We'll live adding even more in the coming months.

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

    BI and gargantuan Data

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

    Not only Do these tools generate loads of new data, they likewise often generate a new 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 hefty on text (even free-form text) while likewise containing more easily defined data, such as dates or credit card numbers. Examples of apps that generate this kind of data include the customer behavior-tracking tools you utilize to contemplate 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 convivial 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 new information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data. new data storage innovations such as data lakes are emerging to unravel 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 gargantuan Data analysis standards. The golden touchstone here is Hadoop, which is an open-source software framework that Apache specifically designed to query great data sets stored in a distributed mode (meaning, in your data center, the cloud, or both). Not only does Hadoop let you query gargantuan 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 business data for maximum insight, then Hadoop is what you need.

    You can download and implement Hadoop itself to accomplish your queries, but it's typically easier and more efficacious to utilize 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 back this. However, each requires varying levels of configuration or even add-on tools to Do so—with IBM, Microsoft, and Tableau offering exceptionally deep capabilities. However, both IBM and Microsoft will noiseless anticipate customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

    Finding the privilege BI Tool

    Given the issues spreadsheets can believe when used as ad hoc BI tools and how firmly ingrained they are in their psyches, finding the privilege BI instrument isn't a simple process. Unlike spreadsheets, BI tools believe 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 precipitous learning curve to really accomplish utilize of their features. Finally, to accomplish matters worse, there are dozens if not hundreds of such tools on the market today, with many vendors willing to title the self-serve BI label even if it doesn't quite fit.

    Getting the overall workflow down with these tools will Take some study and discussion with the people you'll live designating as users. Tableau Desktop and Microsoft Power BI, for example, will start users out with the desktop version to build visualizations and link up to various data sources. Once you believe 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 stay there.

    In recent years, companies believe been taking advantage 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 distinguished to accomplish confident that your employees actually know how to utilize these BI platforms so that you can accomplish confident your investment was worthwhile. There are many ways of approaching this, but using the privilege online learning platform might live a superior region to start looking.

    Given the wide charge scope of these products, you should segment your analytics needs before you accomplish 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 accomplish it effortless to gain started. Plus, they attend to believe great ecosystems of add-ons and partners that can live a cost-effective replacement for doing BI inside a spreadsheet. Tableau Desktop noiseless has the largest collection of charts and visualizations and the biggest confederate 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' preference award. Tableau Desktop may believe a gargantuan charge tag depending on which version you pick but, as previously mentioned, it has an exceptionally great and growing collection of visualizations plus a manageable learning curve if you're willing to consecrate some exertion to it. Microsoft Power BI and Tableau Desktop likewise believe great and growing collections of data connectors, and both Microsoft and Tableau believe 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 superior strategy to disburse some time looking through those community forums to gain an strategy where these companies are headed.

  • Pros: Extremely user-friendly. bizarre automatic report generation. Impressive back availability.

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

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

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

    Cons: Unable to Do real-time streaming analytics.

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

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

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

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

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

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

    Bottom Line: Sisense is a complete platform that should live favorite for experienced BI users. It may topple short for beginners, however.

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

    Cons: User interface is not intuitive. precipitous learning curve. Unwelcoming to new analysts.

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

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

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

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

    Read Review
  • Pros: Designed with common business users in mind. Solid recrudesce on investment.

    Cons: The data you can utilize 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 accomplish it an attractive offering.

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

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

    Cons: Poorly designed user interface. precipitous learning curve.

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

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

    Cons: Very expensive. Not for wee teams.

    Bottom Line: Looker is a powerful self-service business intelligence (BI) instrument that can encourage unify SQL and gargantuan Data management across your enterprise.

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

    Cons: intricate pricing is a deterrent.

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

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

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

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

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

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

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

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

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

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

    Read Review

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

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

    (FORTUNE Magazine) – A secretive revolution is stirring in the course 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 nick to suitable your body. But you can likewise buy pills with the exact blend of vitamins, minerals, and herbs that you like, glasses molded to suitable your 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 financial profile, or a night at a hotel where every employee knows your favorite wine. And if your child does not fancy any of Mattel's 125 different Barbie dolls, she will soon live able to design her own.

    Welcome to the world of mass customization, where mass-market goods and services are uniquely tailored to the needs of the individuals who buy them. Companies as diverse as BMW, Dell Computer, Levi Strauss, Mattel, McGraw-Hill, Wells Fargo, and a slew of leading Web businesses are adopting mass customization to maintain or obtain a competitive edge. Many are just 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 live the organizing principle of business 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 perpetual dialogue with customers. Mass production is cost-efficient. But mass customization is a flexible manufacturing technique that can slash inventory. And mass customization has two huge advantages over mass production: It is at the service of the customer, and it makes replete utilize of cutting-edge technology.

    A gross list of technological advances that accomplish customization possible is finally in place. Computer-controlled factory outfit and industrial robots accomplish it easier to quickly readjust assembly lines. The proliferation of bar-code scanners makes it possible to track virtually every portion and product. Databases now store trillions of bytes of information, including individual customers' predilections for everything from cottage cheese to suede boots. Digital printers accomplish 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 reserve Mass Customization: "Anything you can digitize, you can customize." The Net makes it effortless for companies to travel data from an online order contour 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 likewise often the best course for a customer to learn which company has the most to present him--if he's not tickled with one company's wares, nearly impeccable 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 very 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 Do not belong to Coca-Cola, Anheuser-Busch, or Microsoft. They belong to a category called Other. Now companies are trying to produce a unique Other for each of us. It is the logical culmination of markets' being chopped into finer and finer segments. After all, the ultimate niche is a market of one.

    The best--and most famous--example of mass customization is Dell Computer, which has a direct relationship with customers and builds only PCs that believe actually been ordered. Everyone from Compaq to IBM is struggling to copy Dell's model. And for superior reason. Dell passed IBM last quarter to title the No. 2 spot in PC market share (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 new economy. As Pine says, "The closest person they believe to Henry Ford is Michael Dell."

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

    To Do 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 Put 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 believe no waste."

    The company has been propelled by this thinking ever since Michael Dell started selling PCs from his college dorm leeway 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 current of trait information.

    It's not much of a surprise that a leading tech company fancy 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. Take Mattel. Starting by October, girls will live able to log on to barbie.com and design their own friend of Barbie's. They will live able to pick the doll's skin tone, eye color, hairdo, hair color, clothes, accessories, and cognomen (6,000 permutations will live available initially). The girls will even fill out a questionnaire that asks about the doll's likes and dislikes. When the Barbie pal arrives in the mail, the girls will find their doll's cognomen on the package, along with a computer-generated paragraph about her personality.

    Offering such a product without the Net would live next to impossible. Mattel does accomplish specific versions of Barbie for customers such as Toys "R" Us, and the company customizes cheerleader Barbies for universities. But this will live the first time Mattel produces Barbie dolls in lots of one. fancy Dell, Mattel must utilize 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 existent concern is whether Mattel's systems can exploit the expected exact in a timely fashion. privilege now, marketing VP Anne Parducci is shooting for delivery of the dolls within six weeks--a bit much considering that that is how long it takes to gain a custom-ordered BMW.

    Nevertheless, Parducci is pumped. "Personalization is a dream they believe had for several years," she says. Parducci thinks the custom Barbies could become one of next year's hottest toys. Then, says Parducci, "we are going to build a database of children's names, to develop a one-to-one relationship with these girls." That may sound creepy, but portion of mass customization is treating your customers, even preteens, as adults. By allowing the girls to define beauty in their own terms, Mattel is in theory helping them feel superior 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 mode designer or creating a personality is "through the roof."

    Levi Strauss likewise likes giving customers the chance to play mode 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 present more options and will feature men's jeans as well.

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

    Today a fully stocked Levi's store carries approximately 130 ready-to-wear pairs of jeans for any given waist and inseam. With Personal Pair, that number jumped to 430 choices. And with Original Spin, it will leap again, to about 750. Sanjay Choudhuri, Levi's director of mass customization, isn't in a accelerate 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 present fewer components totality the time." To these two, mass customization isn't about illimitable choices but about offering a hardy number of touchstone parts that can live mixed and matched in thousands of ways. That gives customers the illusion of boundless preference 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 cognomen and address in case Levi's wants to send him promotional offers. And customers who design their own jeans accomplish the impeccable focus group; Levi's can apply what it learns from them to the jeans it mass-produces for the repose of us.

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

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

    Thirty years ago auto manufacturers were, effectively, mass customizers. People would disburse 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 diminutive more than a few touchstone options packages. BMW wants to spin back the clock. About 60% of the cars it sells in Europe are built to order, vs. just 15% in the U.S. Europeans appear willing to wait three to four months for a vehicle, while most Americans won't wait longer than four weeks.

    Now the company wants to accomplish better utilize of its customer database to gain more Americans to custom-order. BMW dealers redeem about $450 in inventory costs on every such order. Reinhard Fischer, head of logistics for BMW of North America, says, "The gargantuan battle is to Take cost out of the distribution chain. The best course to Do 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 new car. Some dealers now convoke customers a few months before their leases are up to contemplate whether they'd fancy to custom-order their next car. Soon, however, customers will live able to configure their own car online and send that info to a dealer. Fischer can even contemplate a day when the Website will present data about vehicles sailing on ships from Germany, so that people can contemplate 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 eliminate their role, but maybe they should believe 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 attend to contemplate of automation as a process that eliminates the requisite for human interaction, mass customization makes the relationship with customers more distinguished than ever. ChemStation in Dayton has about 1,700 industrial-soap formulas--for car washes, factories, landfills, railroads, airlines, and mines. The company analyzes items that are to live cleaned (recent ones in its labs include flutes and goose down) or visits its customers' premises to anatomize 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 gain a three-second conclusion on a loan structured specifically for them. A lot of behind-the-scenes technology makes this possible, including real-time links to credit bureaus, databases with checking-account histories and property values, and software that can Do cash-flow analysis. With a few pieces of customized information from the loan seeker, the software whips into action to accomplish a quick decision.

    The bank likewise uses similar software in its small-business lending unit. According to vice chairman Terri Dial, Wells Fargo used to spin away lots of qualified wee businesses--the loans were too wee 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 approve or contradict 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 gain 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 gain a situation that threatens Wells' very existence. If consumers grow accustomed to designing their own products, will they dependence brand-name manufacturers and service providers or will they spin to a new kind of middleman? straightforward Shlier, a director of research at the Gartner Group in Stamford, Conn., sees disintermediaries emerging totality over the Net to encourage people sift through the thousands of choices presented to them. In financial services, he suggests, there is "a new role for a trusted adviser, maybe someone who doesn't own any banks."

    Shlier's middleman sounds a lot fancy 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 entice someone to Levi's if she could instead visit a clothing Website that stored her digital dimensions and ordered custom-fit jeans from the manufacturer with the best charge and fit? Elaborates Pehong Chen, CEO of Internet software outfit BroadVision: "The Nirvana is that you are so immediate to your customers, you can fill totality their needs. Even if you don't accomplish the particular yourself, you own the relationship."

    Amazon.com has three million relationships. It sells books online and now is touching into music (with videos probably next). Every time someone buys a reserve 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 reiterate 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); gain stock picks geared to your goals (personalwealth.com); or accomplish your own vitamins (acumins.com). And you can gain totality kinds of tailored data; NewsEdge, for example, will send a customized newspaper to your PC.

    These companies want to maintain customers tickled by giving them a product that cannot live compared to a competitor's. Acumin, for instance, blends vitamins, herbs, and minerals per customers' instructions, compressing up to 95 ingredients into three to five pills. If a customer wants to start taking a new supplement, totality Acumin needs to Do is add it to the blend.

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

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



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