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00M-639 - IBM Big Data Sales Mastery Test v1 - Dump Information

Vendor : IBM
Exam Code : 00M-639
Exam Name : IBM Big Data Sales Mastery Test v1
Questions and Answers : 51 Q & A
Updated On : February 15, 2019
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00M-639 IBM Big Data Sales Mastery Test v1

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

Test Code : 00M-639
Test Name : IBM Big Data Sales Mastery Test v1
Vendor Name : IBM
Q&A : 51 Real Questions

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IBM IBM Big Data Sales

Why IBM is having a bet big on this new large information know-how | killexams.com Real Questions and Pass4sure dumps

IBM plans a much bigger push into facts crunching by using opening a new technology center in San Francisco committed to a trendy technology that’s making waves in Silicon Valley, Bloomberg news experiences.

Rob Thomas, an IBM (IBM) vp in cost of big facts, stated in a web video considered by using Bloomberg and later eliminated that the new middle will ultimately condominium “tons of of people” working essentially with a free expertise called Spark.

Spark lets businesses system information more quickly than what is at present viable the use of one other open-source know-how referred to as Hadoop, according to many analysts. among other things, businesses use Spark for speedy evaluation of earnings information like what number of branch shop consumers purchased a particular shirt.

The technology can work with or exchange Hadoop, which has won traction in fresh years with companies like Yahoo (YHOO) and fb (FB) that use it to shop and manner huge amounts of records. Like with a lot of technology, what’s hot in facts crunching adjustments rapidly as new application emerges it really is faster and more convenient to make use of.

It’s because of this velocity and potential to procedure data to directly that has IBM excited. The one hundred-yr ancient business has been public with its assist for the technology and has claimed that it can also be used to raise the efficiency of Hadoop.

IBM has made statistics evaluation a large a part of its earnings pitch, part of which revolves round Watson, the robot that made an appearance on the Jeopardy television online game demonstrate. In April, the business launched its Watson health carrier that businesses can use to investigate healthcare information.

It’s doubtful what IBM plans for Spark. but it could assist with making the underlying applied sciences in the back of Watson or identical services come to life.

by way of aiding Spark and attracting employees who know how to use the infrastructure technology, IBM can claim that it’s ahead of the pack in slicing-side expertise.

With its hardware earnings generating much less salary than it they once did, IBM more and more counting on new technology to revitalize its company. massive records technology could be a much bigger part of the plan.

For more on IBM and massive data, try the following Fortune video:


IBM research Ai Hardware center to drive subsequent-generation AI Chips | killexams.com Real Questions and Pass4sure dumps

An IBM chip comprising a couple of Analog AI instruments used for in-reminiscence computing.

these days IBM announced an formidable plan to create a global research hub to enhance next-technology AI hardware and expand their joint analysis efforts in nanotechnology. As a part of a $three billion dedication, the IBM analysis AI Hardware center might be the nucleus of a new ecosystem of research and commercial companions collaborating with IBM researchers to extra speed up the construction of AI-optimized hardware improvements.

IBM headquarters for the AI Hardware middle might be on the SUNY Polytechnic campus in Albany, new york, and the center will improve a number technologies from chip-stage gadgets, substances and structure to the utility assisting AI workloads. These efforts will push the limits of chip technology needed to fulfill the rising demands of cloud computing, massive statistics, and cognitive computing techniques.

The groundbreaking work that these engineers will behavior at SUNY Polytechnic Institute reflects IBM’s lengthy-time period commitment to inventing the way forward for microelectronics,” talked about Dr. John Kelly, Senior vp, options Portfolio and analysis at IBM. “The IBMers working at SUNY Poly possess pleasing capabilities and capabilities, positioning our business to pressure building of the subsequent generation of chips and to gas a new era of computing.”

IBM analysis AI Hardware core is constructing a roadmap for 1,000x growth in AI compute efficiency efficiency over the next decade, with a pipeline of Digital AI Cores and Analog AI Cores.

The $three billion investment that IBM introduced in July 2014 focuses on the construction of basic substances science to make it feasible to cut back semiconductors to 7 nanometers and beyond, in addition to assist research into fully new areas beyond traditional silicon architectures, equivalent to synaptic computing, quantum devices, carbon nanotubes, and photonics, that might radically change computing of every kind.

Partnerships within an open ecosystem are key to advancing hardware and utility innovation that are the basis of AI. New IBM analysis AI Hardware core partnerships announced these days will aid in those continuing efforts. Samsung is a strategic IBM companion in each manufacturing and analysis. Mellanox applied sciences is a leading business enterprise of excessive-efficiency, conclusion-to-end smart interconnect solutions that accelerate many of the world’s main AI and computer studying systems. Synopsys is the leader in software platforms, emulation and prototyping solutions, and IP for setting up the high-efficiency silicon chips and secure software functions which are riding developments in AI.

IBM analog AI cores are part of an in-reminiscence computing strategy in performance efficiency which improves by suppressing the so-called Von Neuman bottleneck via casting off statistics transfer to and from memory. Deep neural networks are mapped to analog cross element arrays and new non-risky cloth characteristics are toggled to keep community parameters within the pass aspects. learn the way this expertise works in our reside interactive demo.

IBM and SUNY Poly have developed a enormously a hit, globally identified partnership on the multi-billion dollar Albany NanoTech advanced, highlighted by using the establishment’s core for Semiconductor research (CSR), a $500 million program that also contains the world’s leading nanoelectronics agencies. The CSR is a protracted-term, multi-part, joint R&D cooperative software on future desktop chip know-how. IBM changed into a founding member of Governor Andrew M. Cuomo’s international 450 Consortium. It continues to supply pupil scholarships and fellowships on the institution to support prepare the next technology of nanotechnology scientists, researchers and engineers.

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IBM's New big records Analytics: What Does the longer term dangle? | killexams.com Real Questions and Pass4sure dumps

No outcome discovered, are trying new keyword!The newly brought massive information product will help many organizations to benefit ... So it can also be stated that it's run correctly. With a cost to revenue of 2.25, IBM is buying and selling cheaply, even though it has a ...

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

As I look across the learn-to-code industry — with the proliferation of bootcamps, MOOCs, and alternative learning options — I often wonder why we (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 business factors that led us to focus on mastery-based learning.

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

The Backstory

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

Of course as programmers, the first thing we set out to do was to build a revolutionary Learning Management System (LMS) that would end all LMSes. As we worked through the specifications and design, one thing became painfully obvious: we had no idea what we were doing because neither of us had any deep experience with teaching or education. So naturally, before we could build a LMS, we had to get some experience teaching real students. Now, I’d like to think that we’re both pretty well-rounded people with a lot of interests, but we both really only had one skill that could attract students: programming. Towards the end of 2012, we decided to give up our (extremely) high paying jobs and try teaching people programming so we could better understand the problems around education and teaching (…so we could build an LMS to end all LMSes).

I share this backstory because this origin story will come back to influence many of our later decisions. It’s important to remember: we didn’t see an opportunity to make money and came into teaching programming as an exercise in learning about how to educate people.

Side Note: we quickly dropped the LMS idea because we found out students don’t buy LMSes, and selling a new LMS to large organizations requires a skill that we 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, we started our teach-people-to-code exploration at nearly the same time as many other companies, who later collectively came to be known as “coding bootcamps”. It was during this period that a few intrepid companies were starting to prove that you could get graduates a high 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 be 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: we could roll our 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, we declined. Besides meeting Shereef, I also grabbed beers with other local bootcamp founders, like Roshan Choxi and Dave Paola, founders of Bloc.io. It felt like something big was about to happen in the industry and San Francisco was the epicenter.

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

In order to figure out what topics to teach, we asked students what they would be interested in learning. Not surprisingly, they mentioned all 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 we set out to teach them.

The problems we encountered were immediate and obvious.

  • Student readiness levels run the gamut. It’s impossible to teach TDD when someone doesn’t know basic programming principles. We can’t talk about APIs when students didn’t know HTTP. We can’t walk through algorithms when students can’t control nested loops.
  • Related to the first issue, students didn’t keep pace with the lectures. About half the students stopped attending the live lectures after the first week. Though all lectures were recorded, few made an effort to make up for lost time and instead elected to go at their own pace. By the end of the month-long course, only a few students were still attending the live lectures.
  • The above two problems forced us early on to decide if we cared about students’ comprehension at the end of courses. If we didn’t, the solution would be easier: we could just sell recorded videos and content for a fixed price and focus our energies on marketing the content. On the other hand, if we did care about comprehension afterwards, we’d have to find another teaching format because while the idea of live lectures with quizzes seemed good in theory, in practice, most people don’t have the discipline to finish a rigorous course. And without the threat of withholding a credential, we couldn’t do anything to force people to show up.

    These problems also forced us to think hard about who our audience was. If companies like Dev Bootcamp were able to train people for high paying jobs, why couldn’t we do the same, if only we selected the right students? My previous experience 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 we monetize that end if we could find and train good students? This line of thinking only made things more confusing, because if we keep pushing on that logic, wouldn’t it be easier to just become a recruiting company? Why bother doing all the hard work of trying to train unprepared people when we can just filter for the best? That seemed like a more viable business, especially since all the startup literature says to charge businesses instead of individual users wherever you can.

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

    We made a few critical decisions then that we still adhere to today:

  • Students are our customers, not employers. By eliminating employers as a possible revenue source, it brought clarity to what we were supposed to do. One of the things we wanted to do was to help people, not only to make money for ourselves. After all, we had just quit high paying jobs to do something meaningful together. Helping employers didn’t seem very meaningful to us personally and while we were ok with that being a side effect of producing great programmers, we didn’t want to incentivize ourselves to become a recruiting company.
  • We decided to not take venture funding. Though it may have been a bit early in our lifecycle to make that decision, we felt that training companies do not have a significant viral first-mover advantage. Instead, the advantage was in long-term reputation. Sure, it’d be possible to over-promise and over-hype the marketing in the short-term, but our hypothesis was that over time the lack of results will catch up with the hype. We had decided to dedicate our entire thirties to this experiment, and we felt that this long-term mindset could be an advantage in the education space. It’s interesting 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 our energy.

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

    By going the bootstrapped route, we decided on a low-burn long-term financial plan, which usually meant sacrificing marketing for curriculum development. In our hypothesis, there’s no rush to get to market, and it’s more important to protect Launch School’s reputation by always doing “the right thing for the student”. Venture-backed companies have a “fail fast, fail often” mentality where growth rules above all. But in education, “failing” means negatively affecting students’ lives. We weren’t comfortable with purposefully hurting even a small group of students as part of the business plan.

    2013–2014: Tealeaf Academy

    We continued running our synchronous cohorts and the problematic patterns kept repeating cohort after cohort. We took everything we learned and decided to change our curriculum in a couple of important ways:

  • From synchronous to asynchronous (aka, self-paced). Instead of relying on live lectures that were sparsely attended, we’d move to recordings that students could watch at any time.
  • From one 1-month long course, we moved to 3 courses that would take roughly 4 months in total. The courses would start from the ground up, teaching basic programming principles to start, then building up to web development basics, and finally to all the advanced concepts employers wanted.
  • These two changes made a huge difference and students understood this sequence of courses much better. Instead of feeling overwhelmed in the first week, students could complete lectures and assignments on their own schedule. We 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 new self-paced 3-course sequence, results still varied widely. Some graduates got jobs that paid over $100k, and others who finished all 3 courses said they didn’t learn a thing. We posted the $100k student on our testimonials, but it felt like selection bias and not real education for all. It felt that despite our efforts to avoid becoming a recruiting company, we just ended up creating a recruiting company with a 3-course filter.

    The whole point of charging students and forgoing funding was so we can align ourselves with students and do the right thing for students. So how can someone pay over $2,000 and spend over 4 months, and then say they didn’t learn anything? Even if it was a small number of students, that was still a crushing result for us. We 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. We participated in countless 1on1 sessions with students who were struggling and began noticing patterns. We would pair with students who were struggling in course 3 and see that what they were struggling with was not the advanced topic, but fundamentals. They couldn’t build an API not because they couldn’t intellectually understand the concept of an API, but because they didn’t know how HTTP worked. It had nothing to do with intellectual ability, but everything do with understanding of prerequisite knowledge. When we asked “don’t you remember HTTP from course 1?”, they’d say something to the effect of “sure, kind of, but I went through that part pretty fast, and to be honest, it’s still a little fuzzy”. After seeing this over and over, we realized that we were missing a critical component in our courses: assessments.

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

    Upton Sinclair once said, “It is difficult to get a man to understand something, when his salary depends on his not understanding it.” We fell into this trap by not thinking carefully about how our pricing fit with our pedagogy. We never seriously thought about adding rigorous assessments because it meant that less students would enroll in and pay for subsequent courses. We were financially incentivizing ourselves to usher students to subsequent courses without regard to mastery, which is in direct conflict with our mastery-based values. We charged per course, so adding assessments would have resulted in less revenue. The key lesson we took away from this observation was: be 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, we had enough information to go back to the lab and build a curriculum from the ground up anew. We spent the next year studying, researching and debating about what a great training program looked like. Over and over, we found ourselves constantly trapped by incompatible goals. For example, we wanted a democratic learning program that could cater to all, but how do you reconcile that goal with the desire to drive people to high paying jobs? You either have to give up the high paying jobs or you have to filter based on experience. If you only have a 4 or 6 month timeframe, what topics do you cover and how do you make 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, we started from our own first principles by thinking about how we’re different, what our core beliefs were, and our personal stance on learning and comprehension. One idea that came up over and over in our research and discussions was operating for the *long-term*.

    If we take a long-term perspective in our business operations, then it’d be possible to also take a long-term perspective on our pedagogical approach for the curriculum. We can’t have 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 be aligned. After realizing that, we made an important decision: we decided to not only spend our thirties on this, but to spend the rest of our careers on this project. That seems dramatic and conjures up images of a sworn blood oath under a full moon, but it wasn’t a hard decision at all and we made it fairly quickly and unceremoniously. That’s because 1) we didn’t have any other good ideas in the pipeline, 2) we believe that working on this problem will positively affect the world, 3) we believe in each other and don’t want to work on separate things, and 4) teaching online allows us to engage with a worldwide community of students, which brings a certain joy to the project. We didn’t have any reason to stop, and we thought that by focusing on decades in the future, we could use that perspective to our advantage.

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

    Just like how a long-simmering programming puzzle may come into more focus as one spends more time digesting it, the education puzzle began to unfold for us as we shifted into long-term thinking. With the long-term perspective as our north star, we came up with the following values for our business and learning pedagogy:

  • Mastery of fundamentals first.
  • No time limit 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 our Mastery-based Learning pedagogy at Launch School.

    2016: Launch School

    It took us a year to build the new curriculum, and at the end of 2015, we launched Launch School. We didn’t have proof that this new curriculum would be good; it seemed right based on our experience and values, but since we just started, we didn’t have any concrete results to show. We asked prospective students to trust the process and asked if learning fundamentals to mastery made intuitive sense. We didn’t do any market research and built the new curriculum based off of our own standards of excellence, so we weren’t sure how people would react. Would they look at our 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 all 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 idea 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, we felt we were setting up students for long-term success. But we still had the “last mile” problem to solve to demonstrate that there’s a quantitative difference 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 end of 2016, we were able to take some of our Launch School students and put them into an intense instructor-led program to see if we could address the “last mile” problem. We created Capstone, a finishing program where students could apply their already-mastered fundamentals to more complex engineering problems. We wanted to show the world what’s possible when you take years to really learn something well by putting our Capstone graduates into the marketplace. We 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, we were able to showcase the results so far. Because it took a few years for us to wrap our head around the problem, and then a few more years for students to complete our curriculum, we are only seeing quantitative results now in 2018. Of course, we had many small victories along the way with many of our students saying our courses changed their lives, but teaching fundamentals for years also meant taking us farther away from concrete results. Now that we have them, the results are astounding; see for yourself.

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

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

    We’re bootstrapped.

    Other programs focus on financing and pricing innovation, partnerships, scholarships, marketing, government sponsorships, accreditation/credentialism, business process innovation, niche audience segmentation, but none seem interested in pedagogical innovation. I believe that we were able to focus squarely on pedagogy because we kept expanding our time horizon, which wouldn’t have been possible with venture funding. Had we taken investors’ money, we’d have 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 important attributes for educational companies: their pedagogy.

    Quality over data.

    I like to think I’m a data-driven person, but many operators act larger than they are. Most small education companies are not operating at the scale of Amazon (the archetype for the soul-less numbers driven company), and yet they use numbers to override values. Numbers and data are important, but you must have some opinions on quality regarding your craft that you can’t compromise on regardless of what the numbers say. Had we followed the hard logic of numbers from our first year of teaching, we would’ve ended up a recruiting company because that’s what the data says employers wanted. There are also things we won’t do, no matter what the data says. For example, we just plainly refuse to “fail fast, and fail often” because it hurts people (also, we make enough honest mistakes that we don’t need a company philosophy to push for more). I remember first hearing about this concept and thought “that’s a great hack for startup founders”. But when you’re on the receiving end of this ideology as a customer, you think “what a bunch of amateurs and assholes”. In order to do the right thing, you have to have an opinion around quality. If you don’t yet have one, it’s important to move slowly and figure it out until you do. Following a 100% numbers driven analysis, no one would arrive at Mastery-based Learning.

    Have core values.

    A lot of people treat starting a business as a treasure hunt for revenue. In the course of running a business, many decisions come down to this choice: make money or improve quality. It seems counterintuitive, shouldn’t the higher quality product make 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 make money and then figure it out later (another venture-backed mantra: “fake it until you make 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 make 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 work and doing something positive for the world and working with people I enjoy being around. Just as high salaries are actually not the end goal for students at Launch School (they are a side effect of learning to mastery), revenue is not the end goal of the business side of Launch School — it is a side effect of becoming a meaningful long-term organization. I believe that this perspective is what helped us to unlock the long-term value behind Mastery-based Learning.


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

    Analytics Beyond Spreadsheets

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

    Spreadsheets also fall down when the data isn't well-structured or can't be sorted out in neat rows and columns. And, if you have millions of rows or very sparse matrices, then the data in a spreadsheet can be painful to enter and it can be hard to visualize your data. Spreadsheets also have 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 same spreadsheet.

    A spreadsheet containing up-to-the-minute data can also be a problem, particularly if you have exported graphics that need to be refreshed when the data changes. Finally, spreadsheets aren't good 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 make use 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 display calculations. While some spreadsheets can create very sophisticated mathematical models, at their core it is all about the math more than the model itself.

    This is all a long-winded way of saying that when businesses use a spreadsheet, they are actively sabotaging themselves and their ability to consistently get valuable insights from their data. BI tools are specficially designed to help businesses better understand their data, and can prove to be a huge benefit to those upgrading from what a limited spreadsheet can do.

    What Is Business Intelligence?

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

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

    Framed that way, BI as a concept has been around as long as business. But that concept has evolved from early basics [like Accounts Payable (AP) and Accounts Receivable (AR) reports and customer contact and contract information] to much more sophisticated and nuanced information. This information ranges across everything from customer behaviors to IT infrastructure monitoring to even long-term fixed asset performance. Separately tracking such metrics is something most businesses can do regardless of the tools employed. Combining them, especially disparate results from metrics normally not associated with one another, into understandable and actionable information, well, that's the art 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 steady progression towards more in-depth knowledge about the business, competitors, customers, industry, market, and suppliers, to name 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 be entirely handled by mere humans. Early efforts to do these tasks via software, such as customer relationship management (CRM) and enterprise resource planning (ERP), led to the formation of "data silos" wherein data was trapped and useful only within the confines of certain 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 still covers a variety of software applications used to analyze raw data, today it usually refers to analytics for data mining, analytical processing, querying, reporting, and especially visualizing. The main difference between today's BI software and Big Data analytics is mostly scale. BI software handles data sizes typical for most organizations, from small to large. Big Data analytics and apps handle data analysis for very large data sets, such as silos measured in petabytes (PBs).

    Self-Service BI and Data Democratization

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

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

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

    The emerging crop of BI tools all work hard at developing front-end tools that are more intuitive and easier to use than those of older generations—with varying degrees of success. However, that means a key criteria in any BI tool purchasing decision will be to evaluate who in the organization should access such tools and whether the tool is appropriately designed for that audience. Most BI vendors indicate they're looking for their tool suites to become as ubiquitous and easy to use for business users as typical business collaboration tools or productivity suites, such as Microsoft Office. None have gotten quite that far yet in my estimation, but some are closer than others. To that end, these BI tool suites tend 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 fast and effective method of transferring information from a machine to a human brain. The idea is to place digital information into a visual context so that the analytic output can be quickly ingested by humans, often at a glance. If this sounds like 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, new values can be added that will change the visualization on the fly, or the visualization is actually built on rapidly changing data that can turn a static visual into an animation or a dashboard.

    The best visualizations do not seek 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 look impressive at first glance but, if your audience needs help to understand what's being conveyed, then they've ultimately failed.

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

    Products and Testing

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

    My aim was to evaluate cloud versions alone, as I often do analysis on the fly or at least on a variety of machines, as do legions of other analysts. But, in some cases, it was necessary to evaluate a desktop version as well or instead of the cloud version. One example of this is Tableau Desktop, a favorite tool of Microsoft Excel users who simply have an affinity for the desktop tool (and who just move to the cloud long enough to share and collaborate).

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

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

    While I didn't test any of these tools from a data scientist's role, I did mention advanced capabilities when I found them, simply to let buyers know they exist. IBM Watson Analytics is one tool with the ability to extend to highly advanced features and was also one of the easiest to use upfront. IBM Watson Analytics is well-suited for business 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 form queries, a characteristic that can make it valuable to practically anyone. It's highly intuitive, very powerful, and easy to learn. Microsoft Power BI is a strong second as it, too, is powerful while also familiar, certainly to any of the millions of Microsoft business users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they all have their own pros and cons. We'll be adding even more in the coming months.

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

    BI and Big Data

    Another area in which self-service BI is taking off is in analyzing Big Data. This is a newer development in the database space but it's driving tremendous growth and innovation. The name is an apt descriptor because Big Data generally refers to huge data sets that are simply too big to be 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 social media tools (to name a few) that have become so popular over the last several years.

    Not only do these tools generate loads of new data, they also often generate a new kind of data, namely "unstructured" data. Broadly speaking, this is simply data that hasn't been organized in a predefined way. Unlike more traditional, structured data, this kind of data is heavy on text (even free-form text) while also containing more easily defined data, such as dates or credit card numbers. Examples of apps that generate this kind of data include the customer behavior-tracking tools you use to see 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 social media tracking tools.

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

    Enter Big Data analysis standards. The golden standard here is Hadoop, which is an open-source software framework that Apache specifically designed to query large data sets stored in a distributed fashion (meaning, in your data center, the cloud, or both). Not only does Hadoop let you query Big Data, it lets you simultaneously query both unstructured as well as traditional structured data. In other words, if you want to query all of your business data for maximum insight, then Hadoop is what you need.

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

    Finding the Right BI Tool

    Given the issues spreadsheets can have when used as ad hoc BI tools and how firmly ingrained they are in our psyches, finding the right BI tool isn't a simple process. Unlike spreadsheets, BI tools have 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 make use of their features. Finally, to make 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 take some study and discussion with the people you'll be 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 have this together, you can start sharing those results online or across your organization's network. With others, such as Chartio or Google Analytics, you start in the cloud and stay there.

    In recent years, companies have been taking advantage of the wide selection of online learning platforms out there to train their employees on using these platforms. As intuitive as these platforms may be, it is important to make sure that your employees actually know how to use these BI platforms so that you can make sure your investment was worthwhile. There are many ways of approaching this, but using the right online learning platform might be a good place to start looking.

    Given the wide price range of these products, you should segment your analytics needs before you make 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 make it easy to get started. Plus, they tend to have large ecosystems of add-ons and partners that can be a cost-effective replacement for doing BI inside a spreadsheet. Tableau Desktop still has the largest collection of charts and visualizations and the biggest partner 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 our roundup. However, all three products received our Editors' Choice award. Tableau Desktop may have a big price tag depending on which version you choose but, as previously mentioned, it has an exceptionally large and growing collection of visualizations plus a manageable learning curve if you're willing to devote some effort to it. Microsoft Power BI and Tableau Desktop also have large and growing collections of data connectors, and both Microsoft and Tableau have their own sizable communities of users that are vocal about their wants and needs. This can carry a lot of weight with the vendors' development teams so it's a good idea to spend some time looking through those community forums to get an idea where these companies are headed.

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

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

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

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

    Cons: Unable to do real-time streaming analytics.

    Bottom Line: IBM Watson Analytics is an exceptional business intelligence (BI) app that offers a strong analytics engine along with an excellent natural language querying tool. This is one of the best BI platforms you'll find and easily takes our Editors' Choice 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 our Editors' Choice honor for its impressive usability, top-notch data visualization capabilities, and superior compatibility with other Microsoft Office products.

    Read Review
  • Pros: Enormous collection of data connectors and visualizations. User-friendly design. Impressive processing engine. Mature 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 mature offerings on the market and that shows in its feature set. While it has a steeper learning curve than other platforms, it's easily one of the best tools in the space.

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

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

    Bottom Line: Sisense is a complete platform that should be popular for experienced BI users. It may fall short for beginners, however.

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

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

    Bottom Line: Domo isn't for newcomers but for companies that already have business intelligence (BI) experience 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 be 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 name in website and mobile app intelligence. It has a steep learning curve but it is an awesome business intelligence tool.

    Read Review
  • Pros: Designed with general business users in mind. Solid return on investment.

    Cons: The data you can use 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 make it an attractive offering.

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

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

    Bottom Line: If your business already uses SAP's HANA database platform or some of its other back-end business platforms, then SAP Analytics Cloud is a powerful, well-priced choice. But be warned that there's a steep learning curve and a noted 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 business intelligence (BI) users will appreciate. Those new to BI, however, will find it very difficult to use.

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

    Cons: Very expensive. Not for small teams.

    Bottom Line: Looker is a great self-service business intelligence (BI) tool that can help unify SQL and Big 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 business intelligence (BI) tool that delivers the best collection of user access roles among the BI tools we tested, and also demonstrates a promising start towards integrating Data-as-a-Service (DaaS).

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

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

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

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

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

    Bottom Line: While Tibco is still making the transition from a desktop to a cloud software vendor, its self-service business intelligence (BI) tool Tibco Spotfire is a great way to start visualizing your Excel 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 business intelligence (BI) tool for in-depth analysis of your Intuit QuickBooks files, though you'll need to look elsewhere for broader BI tasks.

    Read Review

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