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

Test Code : 00M-639
Test denomination : IBM huge Data Sales Mastery Test v1
Vendor denomination : IBM
exam questions : 51 actual Questions

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IBM IBM huge Data Sales

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

Brokerages foretell that IBM (NYSE:IBM) will report $18.77 billion in earnings for the existing fiscal quarter, in response to Zacks. five analysts own 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 selfsame quarter final yr, which implies a negative year over 12 months boom cost of 1.6%. The company is scheduled to file its subsequent salary effects on Tuesday, April sixteenth.

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

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

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

IBM stock traded down $0.26 on Monday, hitting $137.27. 1,202,955 shares of the enterprise’s stock traded fingers, compared to its usual volume of 5,224,408. IBM has a 1-12 months low of $105.ninety four and a 1-year 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 yield of 4.58%. IBM’s dividend payout ratio (DPR) is prerogative now forty five.47%.

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

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

IBM traffic Profile

overseas enterprise Machines agency operates as an integrated technology and features traffic global. Its Cognitive options segment presents Watson, a computing platform that interacts in language, strategies huge 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, traffic gregarious utility, faculty management solutions, and tailored traffic solutions; and transaction processing application that runs mission-essential systems in banking, airlines, and retail industries.

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

In September 2018, IBM announced a brand modern 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 settle on most beneficial patterns and passes this suggestions to the Db2 query optimizer for exercise by way 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 brace of facets of this product offering are aimed toward helping a community of model developers and executives. as an instance:

  • It helps diverse programming languages equivalent to Python, Scala and R. This permits facts modelers and scientists to exercise a language with which they are general;
  • A graphical person interface known as the visual model Builder guides model developers without requiring totally-technical programming abilities;
  • It contains numerous dashboards for monitoring model effects and scoring features, in addition to controlling the device configuration.
  • This machine getting to know suite turned into at the birth aimed toward zServer-based analytics applications. some of the first evident choices changed into zSystem performance monitoring and tuning. equipment 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 apposite 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 difficulty foreseen with the aid of humans, augment key efficiency warning signs (KPIs) and then feed the model results returned into the gadget to own an upshot on gadget configuration adjustments that can augment 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 profit an knowing of Db2 subsystem fitness. it can dynamically construct baselines for key performance warning signs, give a dashboard of those KPIs and give operational group of workers real-time insight into Db2 operations.

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

    AI comes to Db2

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

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

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

    The problem for the DBA

    to live able to upshot worthy 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 silent IT managers assist this ambiance, particularly with essentially the most skilled and develope team of workers nearing retirement?

    take into account additionally that a huge allotment of decreasing the overall imbue of ownership of these programs is to deserve Db2 functions to flee 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 complex to even identify which functions might improvement from performance tuning, one strategy is to automate the detection and correction of tuning issues. here is where desktop researching and synthetic intelligence can too 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 accumulate and preserve SQL query textual content and entry course details, as well as genuine performance-linked outmoded assistance similar to CPU time used, elapsed instances and upshot set sizes. This providing, described as Db2 AI for z/OS, analyzes and retailers the data in computing device researching fashions, with the mannequin evaluation outcomes then being scored and made available to the Db2 Optimizer. The subsequent time a scored SQL statement is encountered, the Optimizer can then exercise the mannequin scoring facts as input to its entry course option algorithm.

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

    This will too live certainly vital if you reclaim data in distinct areas. for instance, many analytical queries in opposition t huge information require concurrent access to determined records warehouse tables. These tables are generally known as dimension tables, and that they comprehend 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 preserve earnings records may additionally are looking to combination or summarize earnings by vicinity; therefore, the StoreLocation table should live used via some huge records queries. during this ambiance it is usual to assume the dimension tables and duplicate them continually to the huge 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 huge records company analysts. From Db2's point of view, total these queries are equal, and are forwarded to the Optimizer. however, in the case of operational queries and warehouse queries they may silent absolutely live directed to access the StoreLocation desk within the warehouse. even so, the query from the traffic analyst towards huge data tables should doubtless access the copy of the desk there. This results in a proliferations of edge entry paths, and more drudgery for the Optimizer. luckily, Db2 AI for z/OS can give the Optimizer the guidance it needs to compose 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 define 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 preserve this information in a mannequin;
  • because the volume of statistics in every model grows, the MLz Scoring service (which can too live achieved on a zIIP processor) analyzes the mannequin statistics and scores the habits;
  • all over the next bind, rebind, keep together or explain, the Optimizer now has entry to the scoring for SQL models, and makes applicable adjustments to entry route decisions.
  • There are too numerous consumer interfaces that give the administrator visibility to the repute of the collected SQL statement performance statistics and mannequin scoring.

    summary

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

    In different phrases, how well were you aware your statistics, its distribution, its integrity and your existing and proposed entry paths? this can determine the spot 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: augment 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 total articles via Lockwood Lyon


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

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

    Spark lets companies mode 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 exercise Spark for quickly evaluation of sales facts infatuation what number of department reclaim customers purchased a particular shirt.

    The expertise can drudgery with or change Hadoop, which has won traction in recent years with agencies infatuation Yahoo (YHOO) and facebook (FB) that exercise it to shop and mode massive amounts of records. infatuation with a lot of know-how, what’s pungent in statistics crunching alterations quickly as modern 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 traffic has been public with its abet for the technology and has claimed that it will too live used to boost the performance of Hadoop.

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

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

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

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

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


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

    As I explore across the learn-to-code industry — with the proliferation of bootcamps, MOOCs, and alternative learning options — I often wonder 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 way 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 traffic factors that led us to focus on mastery-based learning.

    This is a memoir about how they built Launch School over the eventual 5 years and how their opinions around programming, teaching, and traffic 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 occasion never came up. Finally around 2012, they had a window of time where both of us were looking to upshot something new. They only knew that they wanted to drudgery on something together, but didn’t own any concrete ideas. After months of heedful deliberation, they decided to focus their thirties on Education.

    Of course as programmers, the first thing they set out to upshot was to build a revolutionary Learning Management System (LMS) that would wait total LMSes. As they worked through the specifications and design, one thing became painfully obvious: they had no view what they were doing because neither of us had any deep suffer with teaching or education. So naturally, before they could build a LMS, they had to deserve some suffer teaching actual students. Now, I’d infatuation to believe 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 wait 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 wait total LMSes).

    I participate this backstory because this source memoir will reach back to influence many of their later decisions. It’s well-known to remember: they didn’t contemplate an occasion to compose money and came into teaching programming as an exercise in learning about how to educate people.

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

    2012–2013: Bootcamps

    Unbeknownst to us at the time, this was the golden era of learning to code. In an odd case of multiple-discovery, they started their teach-people-to-code exploration at nearly the selfsame time as many other companies, who later collectively came to live known as “coding bootcamps”. It was during this epoch that a few intrepid companies were starting to prove that you could deserve 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 too grabbed beers with other local bootcamp founders, infatuation Roshan Choxi and Dave Paola, founders of Bloc.io. It felt infatuation something huge was about to happen in the industry and San Francisco was the epicenter.

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

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

    The problems they encountered were immediate and obvious.

  • Student readiness levels flee the gamut. It’s impossible to discipline 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 preserve pace with the lectures. About half the students stopped attending the live lectures after the first week. Though total lectures were recorded, few made an pains to compose up for lost time and instead elected to Go at their own pace. By the wait of the month-long course, only a few students were silent attending the live lectures.
  • The above two problems forced us early on to settle if they cared about students’ comprehension at the wait of courses. If they didn’t, the solution would live easier: they could just sell recorded videos and content for a fixed price and focus their energies on marketing the content. On the other hand, if they did trust about comprehension afterwards, we’d own to find another teaching format because while the view of live lectures with quizzes seemed worthy in theory, in practice, most people don’t own the discipline to finish a rigorous course. And without the threat of withholding a credential, they couldn’t upshot anything to coerce people to expose up.

    These problems too forced us to believe arduous about who their audience was. If companies infatuation Dev Bootcamp were able to train people for lofty paying jobs, why couldn’t they upshot the same, if only they selected the prerogative students? My previous suffer 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 wait if they could find and train worthy students? This line of thinking only made things more confusing, because if they preserve pushing on that logic, wouldn’t it live easier to just become a recruiting company? Why bother doing total the arduous drudgery of trying to train unprepared people when they can just filter for the best? That seemed infatuation a more viable business, especially since total the startup literature says to imbue businesses instead of individual users wherever you can.

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

    We made a few captious decisions then that they silent adhere to today:

  • Students are their customers, not employers. By eliminating employers as a possible revenue source, it brought clarity to what they were suppositious to do. One of the things they wanted to upshot was to abet people, not only to compose money for ourselves. After all, they had just quit lofty paying jobs to upshot something meaningful together. Helping employers didn’t seem very meaningful to us personally and while they were ok with that being a side upshot of producing Great programmers, they didn’t want to incentivize ourselves to become a recruiting company.
  • We decided to not assume venture funding. Though it may own been a bit early in their lifecycle to compose that decision, they felt that training companies upshot not own a significant viral first-mover advantage. Instead, the edge 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 lack 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 edge in the education space. It’s involving that Shereef, Roshan, and Dave opted for the opposite route with their companies and took on venture funding.
  • The consequences of those decisions significantly focused their energy.

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

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

    2013–2014: Tealeaf Academy

    We continued running their synchronous cohorts and the problematic patterns kept repeating cohort after cohort. They took everything they learned and decided to change their curriculum in a brace of well-known ways:

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

    Even with the modern self-paced 3-course sequence, results silent varied widely. Some graduates got jobs that paid over $100k, and others who finished total 3 courses said they didn’t learn a thing. They posted the $100k student on their testimonials, but it felt infatuation selection color and not actual 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 entire point of charging students and forgoing funding was so they can align ourselves with students and upshot the prerogative thing for students. So how can someone pay over $2,000 and disburse over 4 months, and then boom they didn’t learn anything? Even if it was a tiny number of students, that was silent a crushing result for us. They couldn’t let it Go 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 upshot with intellectual ability, but everything upshot with understanding of prerequisite knowledge. When they asked “don’t you bethink HTTP from course 1?”, they’d boom something to the upshot of “sure, kindly of, but I went through that allotment pretty fast, and to live honest, it’s silent a cramped fuzzy”. After seeing this over and over, they realized that they were missing a captious component in their courses: assessments.

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

    Upton Sinclair once said, “It is difficult to deserve 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 proper 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 admiration to mastery, which is in direct conflict with their mastery-based values. They charged per course, so adding assessments would own resulted in less revenue. The key lesson they took away from this observation was: live aware 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 Go 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 Great training program looked like. Over and over, they institute ourselves constantly trapped by incompatible goals. For example, they wanted a democratic learning program that could cater to all, but how upshot you reconcile that goal with the want to drive people to lofty paying jobs? You either own to give up the lofty paying jobs or you own to filter based on experience. If you only own a 4 or 6 month timeframe, what topics upshot you cover and how upshot you compose sure people are following along? Is it ok if only the top 10% or 20% understand the material at the end?

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

    If they assume a long-term perspective in their traffic operations, then it’d live possible to too assume a long-term perspective on their pedagogical approach for the curriculum. They can’t own 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 well-known decision: they decided to not only disburse their thirties on this, but to disburse the ease of their careers on this project. That seems stagy and conjures up images of a sworn blood oath under a full moon, but it wasn’t a arduous conclusion at total and they made it fairly quickly and unceremoniously. That’s because 1) they didn’t own any other worthy ideas in the pipeline, 2) they believe that working on this problem will positively strike the world, 3) they believe in each other and don’t want to drudgery on separate things, and 4) teaching online allows us to engage with a worldwide community of students, which brings a unavoidable joy to the project. They didn’t own any intuition to stop, and they thought that by focusing on decades in the future, they could exercise that perspective to their advantage.

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

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

  • Mastery of fundamentals first.
  • No time circumscribe 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 modern curriculum, and at the wait of 2015, they launched Launch School. They didn’t own proof that this modern curriculum would live good; it seemed prerogative based on their suffer and values, but since they just started, they didn’t own any concrete results to show. They asked prospective students to trust the process and asked if learning fundamentals to mastery made intuitive sense. They didn’t upshot any market research and built the modern curriculum based off of their own standards of excellence, so they weren’t sure how people would react. Would they explore at their proposal of learning indefinitely and then compare it with a 3-month bootcamp and laugh at us? Would they agree with us that the issue with learning advanced topics and frameworks was total about understanding fundamentals? The current marketplace was full of hype about turning around a six-figure job after a few months. How would people receive the view of potentially learning for a few years?

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

    2017: Capstone

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

    Towards the wait of 2016, they were able to assume some of their Launch School students and keep 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 complex engineering problems. They wanted to expose the world what’s possible when you assume 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 tiny victories along the way with many of their students maxim their courses changed their lives, but teaching fundamentals for years too meant taking us farther away from concrete results. Now that they own them, the results are astounding; contemplate for yourself.

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

    To address the question that initially triggered this article, I believe 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, traffic process innovation, niche audience segmentation, but nooneatall seem 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 own been possible with venture funding. Had they taken investors’ money, we’d own 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 well-known attributes for educational companies: their pedagogy.

    Quality over data.

    I infatuation to believe I’m a data-driven person, but many operators act larger than they are. Most tiny education companies are not operating at the scale of Amazon (the archetype for the soul-less numbers driven company), and yet they exercise numbers to override values. Numbers and data are important, but you must own some opinions on attribute regarding your craft that you can’t compromise on regardless of what the numbers say. Had they followed the arduous logic of numbers from their first year of teaching, they would’ve ended up a recruiting company because that’s what the data says employers wanted. There are too 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 compose enough honest mistakes that they don’t requisite a company philosophy to thrust for more). I bethink first hearing about this concept and thought “that’s a Great hack for startup founders”. But when you’re on the receiving wait of this ideology as a customer, you believe “what a bunch of amateurs and assholes”. In order to upshot the prerogative thing, you own to own an conviction around quality. If you don’t yet own one, it’s well-known to journey slowly and design it out until you do. Following a 100% numbers driven analysis, no one would arrive at Mastery-based Learning.

    Have core values.

    A lot of people handle starting a traffic as a treasure hunt for revenue. In the course of running a business, many decisions reach down to this choice: compose money or better quality. It seems counterintuitive, shouldn’t the higher attribute product compose 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 compose money and then design it out later (another venture-backed mantra: “fake it until you compose it”). One major lesson I learned starting Launch School was in learning more about myself. For example, I learned that there wasn’t one or two lines, but lots of lines I wasn’t willing to cross to compose money. I learned about who I was, and who I wanted to become and it’s not a Great entrepreneur or the founder of a multi-million dollar company. For me, it’s about trying to build something worthwhile that lasts as long as possible. It’s about enjoying the daily process of drudgery and doing something positive for the world and working with people I savor being around. Just as lofty salaries are actually not the wait goal for students at Launch School (they are a side upshot of learning to mastery), revenue is not the wait goal of the traffic side of Launch School — it is a side upshot of becoming a meaningful long-term organization. I believe that this perspective is what helped us to unlock the long-term value behind Mastery-based Learning.


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

    Analytics Beyond Spreadsheets

    For many years, Microsoft transcend and other spreadsheets were the tools of preference for traffic professionals who were looking to visualize their data. But spreadsheets had their limits for many traffic intelligence (BI)-related tasks. Even today, trying to creating charts analyzing complex datasets in transcend can silent live frustrating. Sometimes you start with the wrong kindly of data, for example, or you may not know how to manipulate the spreadsheet to create the data visualization{{/ZIFFARTICLE} you need. On the other hand, the rising tide of data democratization is giving everyone in an organization access to corporate data. The requisite has arisen for efficient tools that people of total skill levels can exercise to compose sense of the wealth of information created by businesses every single day.

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

    A spreadsheet containing up-to-the-minute data can too live a problem, particularly if you own exported graphics that requisite to live refreshed when the data changes. Finally, spreadsheets aren't worthy 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 compose exercise of tables of numbers, they are really acting in different arenas with different purposes. A spreadsheet is first and foremost a way to store and array calculations. While some spreadsheets can create very sophisticated mathematical models, at their core it is total about the math more than the model itself.

    This is total a long-winded way of maxim that when businesses exercise a spreadsheet, they are actively sabotaging themselves and their faculty to consistently deserve valuable insights from their data. BI tools are specficially designed to abet businesses better understand their data, and can prove to live a huge profit to those upgrading from what a limited spreadsheet can do.

    What Is traffic Intelligence?

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

    BI is an umbrella term meant to cover total of the activities necessary for a company to swirl raw information into actionable knowledge. In other words, it's a company's efforts to understand what it knows and what it doesn't know of its own actuality and operations. The ultimate goal is being able to augment profits and sharpen its competitive edge.

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

    BI software has been instrumental in this constant progression towards more in-depth knowledge about the business, competitors, customers, industry, market, and suppliers, to denomination just a few possible metric targets. But as businesses grow and their information stores balloon, the capturing, storing, and organizing of information becomes too large and complex to live entirely handled by mere humans. Early efforts to upshot 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 unavoidable 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 silent covers a variety of software applications used to resolve raw data, today it usually refers to analytics for data mining, analytical processing, querying, reporting, and especially visualizing. The main dissimilarity between today's BI software and huge Data analytics is mostly scale. BI software handles data sizes typical for most organizations, from tiny to large. huge Data analytics and apps manipulate data analysis for very large data sets, such as silos measured in petabytes (PBs).

    Self-Service BI and Data Democratization

    The BI tools that were approved half a decade or more ago required specialists, not just to exercise but too to interpret the resulting data and conclusions. That led to an often inconvenient and fallible filter between the people who really needed to deserve 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 traffic whose data they were analyzing. That led to a focus on data the company didn't need, a misinterpretation of results, and often a progression of "standard" reporting that analysts would flee 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 modern trend among modern 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 tool arsenal, but IT no longer acts as a bottleneck to every query and report request.

    As a result, users can assume edge of this distributed BI model. Key tools and captious data own moved from a centralized and difficult-to-access architecture to a decentralized model that merely requires access credentials and familiarity with modern BI software. This results in a entire modern kindly of analysis becoming available to the organization, namely, that of experienced, front-line traffic people who not only know what data they requisite but how they requisite to exercise it.

    The emerging crop of BI tools total drudgery arduous at developing front-end tools that are more intuitive and easier to exercise than those of older generations—with varying degrees of success. However, that means a key criteria in any BI tool purchasing conclusion will live to evaluate who in the organization should access such tools and whether the tool is appropriately designed for that audience. Most BI vendors witness they're looking for their tool suites to become as ubiquitous and easy to exercise for traffic users as typical traffic collaboration tools or productivity suites, such as Microsoft Office. nooneatall own gotten quite that far yet in my estimation, but some are closer than others. To that end, these BI tool suites watch 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 quickly and efficient mode of transferring information from a machine to a human brain. The view is to spot digital information into a visual context so that the analytic output can live quickly ingested by humans, often at a glance. If this sounds infatuation those pie and bar charts you've seen in Microsoft Excel, then you're right. Those are early examples of data visualizations.

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

    The best visualizations upshot not hunt artistic awards but instead are designed with function 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 explore impressive at first glance but, if your audience needs abet to understand what's being conveyed, then they've ultimately failed.

    Most BI software, including those reviewed here, comes with visualization capabilities. However, some products proffer more options than others so, if advanced visuals are key to your BI process, then you'll want to closely examine these tools. There are too third-party and even free data visualization tools that can 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 traffic analyst. But I too kept in wit the viewpoint of users who might own no familiarity with data processing or analytics. I loaded and used the selfsame data sets and posed the selfsame queries, evaluating results and the processes involved.

    My plane was to evaluate cloud versions alone, as I often upshot analysis on the soar or at least on a variety of machines, as upshot 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 example of this is Tableau Desktop, a favorite tool of Microsoft transcend users who simply own an affinity for the desktop tool (and who just journey to the cloud long enough to participate and collaborate).

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

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

    While I didn't test any of these tools from a data scientist's role, I did mention advanced capabilities when I institute them, simply to let buyers know they exist. IBM Watson Analytics is one tool with the faculty to extend to highly advanced features and was too one of the easiest to exercise upfront. IBM Watson Analytics is well-suited for traffic analysts and for widespread data democratization because it requires little, if any, knowledge of data science. Instead, it works well by using natural language and keywords to shape queries, a characteristic that can compose it valuable to practically anyone. It's highly intuitive, very powerful, and easy to learn. Microsoft Power BI is a tenacious second as it, too, is powerful while too familiar, certainly to any of the millions of Microsoft traffic users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they total own 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 manipulate streaming data. For many users, that won't live a problem in the immediate future. However, for those involved with analyzing traffic processes as they happen, such as website performance metrics or customer conduct patterns, streaming data can live invaluable. Also, the Internet of Things (IoT) will drive this issue in the near future and compose streaming data and streaming analytics a must-have feature. Many of these tools will own to up their game accordingly so, unless you want to jump ship in a year or two, it's best to believe ahead when considering BI and the IoT.

    BI and huge Data

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

    Not only upshot these tools generate loads of modern data, they too often generate a modern kindly 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 kindly of data is heavy on text (even free-form text) while too containing more easily defined data, such as dates or credit card numbers. Examples of apps that generate this kindly of data comprehend the customer behavior-tracking tools you exercise 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 gregarious media tracking tools.

    Organizations deploying these tools are being challenged not only by a sudden deluge of unstructured data that quickly strains storage resources [think beyond terabytes (TB) into the PB and even exabyte (EB) range] but, even more importantly, they're finding it difficult to query this modern information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data. modern data storage innovations such as data lakes are emerging to unravel for this need, but organizations silent 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 huge Data analysis standards. The golden benchmark here is Hadoop, which is an open-source software framework that Apache specifically designed to query large data sets stored in a distributed style (meaning, in your data center, the cloud, or both). Not only does Hadoop let you query huge Data, it lets you simultaneously query both unstructured as well as traditional structured data. In other words, if you want to query total of your traffic data for maximum insight, then Hadoop is what you need.

    You can download and implement Hadoop itself to effect your queries, but it's typically easier and more efficient to exercise commercial querying tools that employ Hadoop as the foundation of more intuitive and full-featured analysis packages. Notably, most of the tools reviewed here, including Chartio, IBM Watson Analytics, Microsoft Power BI, and Tableau Desktop, total support this. However, each requires varying levels of configuration or even add-on tools to upshot so—with IBM, Microsoft, and Tableau offering exceptionally deep capabilities. However, both IBM and Microsoft will silent expect customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

    Finding the prerogative BI Tool

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

    Getting the overall workflow down with these tools will assume 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 own this together, you can start sharing those results online or across your organization's network. With others, such as Chartio or Google Analytics, you start in the cloud and wait there.

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

    Given the wide price compass of these products, you should segment your analytics needs before you compose 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 compose it easy to deserve started. Plus, they watch to own large ecosystems of add-ons and partners that can live a cost-effective replacement for doing BI inside a spreadsheet. Tableau Desktop silent has the largest collection of charts and visualizations and the biggest ally network, though both IBM Watson Analytics and Microsoft Power BI are catching up fast.

    IBM Watson Analytics scored the highest, and Microsoft Power BI and Tableau Desktop scored the next highest in their roundup. However, total three products received their Editors' preference award. Tableau Desktop may own a huge price tag depending on which version you elect but, as previously mentioned, it has an exceptionally large and growing collection of visualizations plus a manageable learning curve if you're willing to pledge some pains to it. Microsoft Power BI and Tableau Desktop too own large and growing collections of data connectors, and both Microsoft and Tableau own their own sizable communities of users that are vocal about their wants and needs. This can carry a lot of weight with the vendors' evolution teams so it's a worthy view to disburse some time looking through those community forums to deserve an view where these companies are headed.

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

    Cons: Automated reports can quickly become defaults. steep learning curve that might befuddle beginners.

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

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

    Cons: Unable to upshot real-time streaming analytics.

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

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

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

    Bottom Line: Tableau Desktop is one of the most develope 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 traffic intelligence (BI). Analytics process silent needs to live ironed out. Natural language capability can live limited.

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

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

    Cons: User interface is not intuitive. steep learning curve. Unwelcoming to modern analysts.

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

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

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

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

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

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

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

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

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

    Cons: Poorly designed user interface. steep learning curve.

    Bottom Line: Chartio excels at building a powerful analytics platform that experienced traffic intelligence (BI) users will appreciate. Those modern 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 tiny teams.

    Bottom Line: Looker is a Great self-service traffic intelligence (BI) tool that can abet unify SQL and huge Data management across your enterprise.

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

    Cons: complex pricing is a deterrent.

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

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

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

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

    Read Review
  • Pros: Very easy to deserve started. Nice team management and collaboration features.

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

    Bottom Line: While Tibco is silent making the transition from a desktop to a cloud software vendor, its self-service traffic intelligence (BI) tool Tibco Spotfire is a Great way to start visualizing your transcend data.

    Read Review
  • Pros: Excellent analytical support for Intuit QuickBooks. Very easy setup.

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

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

    Read Review

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

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

    (FORTUNE Magazine) – A reserved revolution is stirring in the way things are made and services are delivered. Companies with millions of customers are starting to build products designed just for you. You can, of course, buy a Dell computer assembled to your exact specifications. And you can buy a pair of Levi's crop to proper your body. But you can too buy pills with the exact blend of vitamins, minerals, and herbs that you like, glasses molded to proper your puss precisely, CDs with music tracks that you choose, cosmetics mixed to match your skin tone, textbooks whose chapters are picked out by your professor, a loan structured to meet your fiscal profile, or a night at a hotel where every employee knows your favorite wine. And if your child does not infatuation 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 birth 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 traffic 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 continuous dialogue with customers. Mass production is cost-efficient. But mass customization is a springy 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 full exercise of cutting-edge technology.

    A entire list of technological advances that compose customization possible is finally in place. Computer-controlled factory equipment and industrial robots compose it easier to quickly readjust assembly lines. The proliferation of bar-code scanners makes it possible to track virtually every allotment and product. Databases now store trillions of bytes of information, including individual customers' predilections for everything from cottage cheese to suede boots. Digital printers compose 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 bespeak Mass Customization: "Anything you can digitize, you can customize." The Net makes it easy for companies to journey data from an online order shape to the factory floor. The Net makes it easy for manufacturing types to communicate with marketers. Most of all, the Net makes it easy for a company to conduct an ongoing, one-to-one dialogue with each of its customers, to learn about and respond to their exact preferences. Conversely, the Net is too often the best way for a customer to learn which company has the most to proffer him--if he's not elated with one company's wares, nearly perfect information about a competitor's is just a mouse click away. Combine that with mass customization, and the nature of a company's relationship with its customers is forever changed. Much of the leverage that once belonged to companies now belongs to customers.

    If a company can't customize, it's got a problem. The Industrial Age model of making things cheaper by making them the selfsame will not hold. Competitors can copy product innovations faster than ever. Meanwhile, consumers demand 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 upshot not belong to Coca-Cola, Anheuser-Busch, or Microsoft. They belong to a category called Other. Now companies are trying to bear a unique Other for each of us. It is the logical culmination of markets' being chopped into finer and finer segments. After all, the ultimate niche is a market of one.

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

    To upshot 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 arduous drives, say, or to the factory floor, where assemblers keep 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 own no waste."

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

    It's not much of a surprise that a leading tech company infatuation 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. assume 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 elect the doll's skin tone, eye color, hairdo, hair color, clothes, accessories, and denomination (6,000 permutations will live available initially). The girls will even fill out a questionnaire that asks about the doll's likes and dislikes. When the Barbie pal arrives in the mail, the girls will find their doll's denomination on the package, along with a computer-generated paragraph about her personality.

    Offering such a product without the Net would live next to impossible. Mattel does compose 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. infatuation Dell, Mattel must exercise high-end manufacturing and logistics software to ensure that the order data on its Website are distributed to the parts of the company that requisite them. The only actual concern is whether Mattel's systems can manipulate the expected demand in a timely fashion. prerogative 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 deserve a custom-ordered BMW.

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

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

    With the abet 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 plain pair of test-drive jeans to compose sure they infatuation the proper 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 quicken to add more choices. "It is captious to carefully pick the choices that you offer," says Choudhuri. "An unlimited amount will create inefficiencies at the plant." Dell Computer's Rollins agrees: "We want to proffer fewer components total the time." To these two, mass customization isn't about illimitable choices but about offering a hardy number of benchmark 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 denomination and address in case Levi's wants to ship him promotional offers. And customers who design their own jeans compose the perfect focus group; Levi's can apply what it learns from them to the jeans it mass-produces for the ease of us.

    If Levi's experiment pays off, other apparel makers will ensue 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. eventual year some MIT traffic students proposed a similar view for a custom-made bra company dubbed perfect Underwear.

    Morpheus Technologies, a wacky startup in Portland, Me., hopes to set up studios equipped with cadaver scanners. Founder Parker Poole III wants to "digitize people and connect their measurement data to their credit cards." Someone with the foresight to live scanned by Morpheus could then summon up Eddie Bauer, say, give his credit card number, and order a robe that matches his dimensions. His digital self could too 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 own 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 better manufacturing efficiency by offering cramped more than a few benchmark options packages. BMW wants to swirl back the clock. About 60% of the cars it sells in Europe are built to order, vs. just 15% in the U.S. Europeans seem 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 compose better exercise of its customer database to deserve more Americans to custom-order. BMW dealers reclaim about $450 in inventory costs on every such order. Reinhard Fischer, head of logistics for BMW of North America, says, "The huge battle is to assume cost out of the distribution chain. The best way to upshot 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 modern car. Some dealers now summon customers a few months before their leases are up to contemplate whether they'd infatuation to custom-order their next car. Soon, however, customers will live able to configure their own car online and ship that info to a dealer. Fischer can even contemplate a day when the Website will proffer 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 ship the requests directly to BMW, circumventing dealers altogether? Says Fischer: "We don't want to purge their role, but maybe they should own 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 puss and to whether he wants, say, casual frames, a sports pair, sunglasses, or more formal specs. The customer can check out on a monitor various choices superimposed over a scanned image of his face. Once he chooses the pair he likes, the lenses are ground and the rimless frames attached.

    While they watch to believe of automation as a process that eliminates the requisite for human interaction, mass customization makes the relationship with customers more well-known 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 comprehend flutes and goose down) or visits its customers' premises to resolve their dirt. After the analysis, the company brews up a special batch of cleanser. The soap is then placed on the customer's property in reusable containers ChemStation monitors and keeps full. For most customers, teaching another company their cleansing needs is not worth the effort. About 95% of ChemStation's clients never leave.

    Hotels that want you to preserve coming back are using software to personalize your experience. total 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 deserve 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 upshot cash-flow analysis. With a few pieces of customized information from the loan seeker, the software whips into action to compose a quick decision.

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

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

    Shlier's middleman sounds a lot infatuation 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 seduce someone to Levi's if she could instead visit a clothing Website that stored her digital dimensions and ordered custom-fit jeans from the manufacturer with the best price and fit? Elaborates Pehong Chen, CEO of Internet software outfit BroadVision: "The Nirvana is that you are so close to your customers, you can answer total their needs. Even if you don't compose 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 bespeak 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); elect from over a billion options of printed art, mats, and frames (artuframe.com); deserve stock picks geared to your goals (personalwealth.com); or compose your own vitamins (acumins.com). And you can deserve total kinds of tailored data; NewsEdge, for example, will ship a customized newspaper to your PC.

    These companies want to preserve customers elated 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 modern supplement, total Acumin needs to upshot is add it to the blend.

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

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



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