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00M-639 IBM Big Data Sales Mastery Test v1

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Test Code : 00M-639
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Vendor Name : IBM
: 51 Real Questions

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IBM IBM Big Data Sales

IBM's Mainframe income Crashed. That’s commonplace.

One reason IBM (NYSE:IBM) suffered a earnings decline in the first quarter became slumping demand for its mainframe computer systems. salary from mainframes tumbled 38% 12 months over 12 months, dragging income in the methods segment down 9% on a currency-adjusted groundwork.

while mainframes are not as crucial to IBM today as they had been in the past, the hulking programs, with a large deploy base and great switching expenses, are a key part of the enterprise's competitive benefits. That makes the steep decline in revenue seem to be all of the extra concerning.

IBM's z14 mainframe system.

picture supply: IBM.

here's commonplace

there is no cause to fret about the precipitous decline in mainframe sales. IBM's mainframe income spike each and every time the business launches a new model -- the third quarter of 2017 marked the birth of the newest surge in earnings with the z14 gadget. After 4 or 5 quarters of robust yr-over-12 months boom, pushed by valued clientele upgrading from older fashions, that boom is lapped, and sales begin to say no.

This cycle has performed out multiple instances over the past decade as IBM refreshed its mainframe programs each few years. And regardless of the steep sales declines IBM is now seeing, the present cycle is the strongest in many years.

A chart showing IBM's year-over-year mainframe growth.

Date supply: IBM. Chart by means of creator.

"we are seven quarters into the z14 cycle and the application continues to music ahead of the prior application. They had amazing boom in volumes or shipped MIPS, and new workload MIPS continue to outpace their normal MIPS," referred to IBM CFO James Kavanaugh all through the first-quarter profits call. (MIPS stands for million guidance per 2d, a measure of processing power.)

Kavanaugh added that the single-frame version of the z14, launched final yr and designed to healthy into average information centers, is a boom driver for the mainframe company.

IBM more than doubled mainframe revenue on a 12 months-over-yr groundwork in the 2d quarter of 2018, so one more big decline in sales within the 2nd quarter of this 12 months is inevitable. The enterprise will then begin to lap these declines within the 2d half of this yr.

Given the commonplace hole between mainframe launches, IBM's subsequent-gen mainframe gadget probably might not arrive unless sometime in 2020. That means three or four extra quarters of vulnerable mainframe performance earlier than an extra yr-long surge in income.

2020 is shaping as much as be a large 12 months

now not best will IBM possible get a income boost in 2020 because of the next mainframe launch, but the enterprise will additionally add a couple of billion bucks of revenue from its acquisition of purple Hat. That deal is scheduled to shut earlier than the conclusion of this year.

pink Hat is becoming at a double-digit expense, and its utility strengthens IBM's hand as it goes after the hybrid cloud computing market. there is a mainframe connection right here, too -- red Hat presents a edition of its commercial enterprise Linux operating system for IBM's mainframes. Kavanaugh stated all the way through the salary call that Linux is a key increase driver for the mainframe business.

IBM's total profits will probably decline this 12 months, by and large because of a significant currency headwind, however additionally because of slumping mainframe sales. however next yr will appear lots more desirable.


IBM's earnings have fallen for 15 straight quarters

Why IBM's CEO is hiring Brooklyn teens Why IBM's CEO is hiring Brooklyn teenagers The remaining time IBM's revenue rose, "The Artist" had just received the Oscar for most desirable graphic and the big apple Giants were celebrating a super Bowl win over the new England Patriots.

given that then, IBM has rattled off 15 straight quarters of sales declines. massive Blue talked about Tuesday that its fourth-quarter earnings fell 8.5% from a year ago.

simply over a 12 months in the past, IBM became predicting that its earnings would attain $20 a share via the end of 2015. neatly, they simply comprehensive up 2015, and IBM posted profits of just $14.92. it's no ask yourself IBM deserted that $20 goal within the third quarter of 2014.

it's a surprising stoop for the 104-year old enterprise. And it be only going to worsen.

IBM (IBM) envisioned that a powerful dollar would weigh heavily on income, forecasting that its income would be about $13.50 per share this yr. Wall highway analysts had anticipated income of about $15 a share.

The company changed into sick-organized for its customers' unexpected heat include of cloud computing. Why buy massive, expensive IBM mainframes and servers if you happen to pays Amazon (AMZN) or Microsoft (MSFT) to residence your whole statistics for you -- for more affordable?

over the last several years, IBM has tried to shift its method. it is now an immense cloud player, racking up $10.2 billion in cloud income closing yr. And it has poured millions of greenbacks into massive information analytics, mobility and protection. IBM pointed out sales of those "strategic imperatives" grew by way of 26% in 2015.

however guidance a ship as big as IBM takes lots of time -- and patience that many investors wouldn't have. Shares of IBM are already down 7% this year, after falling 15% ultimate year and 13% in 2014.

$IBM down 5% #premarket. Time for Watson to take over as CEO?

— Paul R. La Monica (@LaMonicaBuzz) January 20, 2016

Yet IBM's most trendy shareholder is sticking by way of the company, for now anyway. Warren Buffett introduced in November that he has misplaced $2 billion on his IBM funding, but he stated he changed into assured that the stock's plunge is only "temporary."

IBM's executive management says it expects that its strategy will pay off quickly, but Wall street analysts aren't anticipating IBM's earnings to develop for at least a different couple years.

"As they have mentioned, this transformation will play out over time," spoke of Martin Schroeter, IBM's chief monetary officer, on a convention name with investors. "we are more and more inspired that the approach is appropriate and that we're executing to seriously change IBM."


IBM Halts earnings of Watson AI For Drug Discovery and analysis

This website can also earn affiliate commissions from the hyperlinks on this web page. phrases of use.

as the AI hype-cycle has developed, we’ve been treated to a plethora of claims about what types of improvements and breakthroughs the know-how can carry. probably the most fundamental — and doubtlessly important — has been the concept that they can use AI to discover new drug treatments and coverings for latest conditions the place present options have come up brief.

That promise has itself now come up short. IBM has announced that it's going to cease promoting its Watson AI gadget as a tool for drug discovery. It’s a excessive-profile retreat for the company, which has aggressively marketed AI as being helpful for these purposes and which bumped into complications closing yr when reviews indicated its programs had made fallacious, dangerous recommendations for cancer sufferers (the system’s options had been in no way put into effect).

while IBM cites gradual earnings as a explanation for its withdrawal, deeper issues are doubtlessly responsible. A recent deep dive through IEEE Spectrum places context round these issues. The upshot: After years of labor and a few moonshot projects, IBM has remarkably little to exhibit for its efforts. And the enterprise has created a certain quantity of unwell will towards itself, IEEE writes, since it took an aggressive, advertising and marketing-first strategy to AI and Watson, promising grandiose achievements that didn’t precisely painting what the device could in reality reliably obtain.

Watson wowed the realm with its efficiency on Jeopardy and an potential to research the relationships between words in preference to treating them like search terms. In idea, Watson may use its engine to kind through reams of medical information in an identical style, discovering the hidden signal within a system filled with noise. truth has not cooperated. Of the small quantity of analysis conducted on the usage of AI to increase affected person results, none of it has concerned IBM’s Watson.

The IEEE piece takes pains to notice that IBM faced massive challenges in trying to carry its AI program on-line and use it quite simply for human medication. Nothing like Watson (or what Watson became intended to be) has ever existed before. no one knew the way to construct it. Yoshua Bengio, a number one AI researcher on the tuition of Montreal, summarized the efforts to support AI take note scientific texts and terminology thusly: “We’re doing highly more suitable with NLP than they were five years ago, yet we’re nevertheless extremely worse than people.”

A Vexing issue

Watson’s issue wasn’t that it didn’t work. The difficulty is, Watson doesn’t do the correct stuff. whereas it without delay learned to ingest and process gigantic portions of data, it had a very good deal of situation determining the bits of advice inside a study that might lead docs to in reality trade their system of care. here's specially genuine if the primary advice turned into incidental to the leading factor of the analysis.

as a result of affected person information wasn’t always accurately formatted or even chronologically organized, the utility had crisis knowing patient histories. And the equipment was incapable of comparing new cancer patients against databases of previous patients to discover hidden medicine patterns, as a result of such practices would not be regarded facts-based. Making a powerful suggestion from evidence-based mostly medication requires double-blind reviews, meta-analyses, and systemic facts studies, no longer an AI gadget claiming to have found a similarity between different types of sufferers.

It’s not clear what’s next for Watson, if the rest. The device has had some success in slender, tailor-made applications with much less ambiguity. but regardless of dozens of deliberate initiatives, oceans of hype, and a pretty good deal of funding, IBM’s Watson for Drug Discovery has evidently neglected its own goals.

replace (4/22/019): IBM contests numerous elements of this story. A spokesperson told us that the enterprise isn't discontinuing Watson for Drug Discovery, and that it is as an alternative “focusing their supplies inside Watson health to double down on the adjoining box of clinical development the place they see an excellent greater market need for their information and AI capabilities.”

It’s no longer a discontinuation. It’s simply a totally distinct center of attention in adjacent markets the place IBM thinks it could possibly earn greater cash.

next, IBM disputes allegations that Watson health has little to demonstrate for its efforts. It presents no certain proof for this claim, beyond noting (absolutely truthfully) that treating cancer is especially problematic and scientific growth is slow. this is some extent they totally agree with.

The question in play is not even if IBM is doing whatever thing rewarding or essential with the aid of specializing in cancer analysis, however no matter if or not its products are producing valuable effects. The constant narrative from the businesses and corporations which have used these products, thus far, is “No,” or at the very least, “now not on the level of ability and ability the advertising department promised.” IBM’s greatest successes, in accordance with IEEE Spectrum, have come from the selected software of AI to slender, well-understood complications.

eventually, IBM notes that in a follow-up cancer treatment advice survey, its accuracy price rose from seventy three p.c in 2017 to 93 p.c in accordance with a January 2018 survey. It is not clear exactly what drove this improvement or whether the beneficial properties are because of an growth in Watson’s competencies or if different aspects of the contrast technique have been modified to produce a superior effect. The 2d check best concentrated on non-concordant cases from the first verify as opposed to retesting the entire statistics set.

Now study:


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7 Digital Marketing Marketing Trends You Can’t Afford to Ignore

7 Digital Marketing Trends You Cant Afford to Ignore

“I would have written you a shorter letter but I didn’t have the time.”

This quote attributed to many different sources sums up what they are facing in this now very complex world. But keeping it simple and short takes time and effort. Distilling the complex into something that makes sense in one sentence or even a six second video is an art form that is to be treasured.

But as marketers that is what you are confronted with. Increasing complexity. It is just one of the trends that will impact your business in the years ahead.

We also need the right tools, platforms and technology to help us scale the huge amounts of data and noise that now confront us online. Here are 7 digital marketing trends that you need to keep your eye on.

1. Increasing complexity

Tech is largely to blame for making marketing complex. But it’s also the answer. Making sense of tons of data, always on marketing and the many forms of splintered media is something tech platforms do well.

Right alongside that sits different categories of complex. Facebook advertising is a discipline on its own that requires focus. Then add the dark science of “Search Engine Optimisation” and understanding the many moving parts that require mastery.

The types of media they need to master includes live streaming, Facebook videos and GIFS and Infographics. Then you need to master marketing automation and artificial intelligence tools and platforms.

2. Marketing technology

Marketing technology (sometimes called Martech) is continuing to grow. According to Venture Beat there has been over $134 billion invested in this category in startups in just 5 years. In 2018 IDC is predicting that CMO’s will spend $32 billion on buying and implementing Martech solutions.

But the category has 3 core categories. B2B marketing, B2C marketing and advertising technology. It should not be seen as one size fits all.

According to Ajay Agarwal in an article on TechCrunch there is an interesting divide between spending on marketing versus sales between B2B and B2C. B2B companies spend much more on their front end sales than on marketing at a ratio of 10:1. B2C spends a lot more on marketing than sales with an inverse ratio of 10:1.

Maybe they will expect to see Salesforce dominate B2B marketing tech as it is the dominant sales tool for B2B globally.

He sees the key trend and opportunity as one in which a B2C marketing company can create a B2C system of record from the start of the customer journey to sale.

3. The rise and rise of algorithms

The beauty at the the birth of social media networks was their simplicity. They flowed past you unfiltered.

Twitter was a distilled stream of unfettered consciousness. Facebook was a flowing page of diverse ideas and people. And some sat outside your tent of ideas and interests.

But things have changed.

The need to make money from the platforms meant that algorithms needed to be programmed to ensure that the social networks could start to monetise their distribution. The organic reach that initially excited marketers, writers and entrepreneurs has been dialled back.

Also the volume of data has also exploded in the last 10 years driven by the two obsessive technologies of social media and smartphones. To make sense of that as finite humans means they need help from the machines. And they run on algorithms.

Why do they need algorithms?

The amount of data that confronts us each day needs filters to help us make sense of it. Here is the global digital snapshot of the size of the ecosystem that confronts us.

These platforms, devices and networks are producing this avalanche of data.

  • 5.97 billion hours of YouTube are watched every day
  • 4.3 billion Facebook messages are posted each day
  • 67 million images posted on Instagram every 24 hours
  • 269 billion emails are sent 24/7 each day
  • How algorithms are applied

    Algorithms also help marketers and entrepreneurs break through the clutter. And they are used to reach your target markets on Amazon, Tripadvisor, search engines and even in email marketing .

  • Amazon – products or books you might like based on past activity and other secret codes.
  • Search – Content and information you want to find.
  • Social – People you want to hear from or see.
  • Emails – Gmail now uses an algorithm that sorts it into tabs and also sends it automatically to spam based on its filters.
  • GPS – Help us navigate a city or find their way to a hotel.
  • Choose a restaurant – Reviews on Yelp or on Tripadvisor.
  • So they need to keep studying how they work and learn to work with them as they continue to change and shape shift.

    4. Platforms and apps

    The web is moving away from an Internet of websites to an Internet of apps and platforms. You may not have noticed it but it is happening right before their eyes. In 2014 more people accessed the Internet though mobile apps than desktop computers for the very first time.

    In 2017 over 86% of their time is spent on apps on their mobile phones.

    This big trend is something that can’t be ignored and marketers are going to need to work out how they reach consumers via apps and platforms. The obvious answer to this in part will be the rise and rise of Facebook advertising from the platform and also applying marketing tactics to apps like Messenger and WhatsApp.

    5. Artificial intelligence

    There are two things that humans aren’t good at. Collating and making sense of the vast amounts of data and also scaling their humanity. That is what machines excel at and artificial intelligence allows humans to amplify themselves.

    Watson, Einstein and Rank Brain are just three of the big players battling out for world domination in artificial intelligence . Watson is owned by IBM, Einstein by Salesforce and Rank Brain by Google. But underneath these giants who mostly use it for internal use sits a growing range of apps and platforms that are using AI for niche marketing optimization.

    Chatbots were maybe the first obvious use of AI for marketing with their abilities to handle initial enquiries without the need for human inervention. But what are some of the other ways AI is being used and imagined for improving marketing performance and scaling?

    Robert Allen from CITU lists 15 ways in which AI can be used for marketing.

    But let’s take a closer look at just 3 ways you can use artificial intelligence .

    Content creation

    An AI program called WordSmith produced 1.5 billion pieces of content in 2016. The other challenge is not just creation but curation of content at scale. An AI tool like Rocco can recommend content from social media that your followers are likely to love.

    Pay per click advertising

    Making sure that you are making the best use of your budget when performing paid ad campaigns with millions of dollars in spend means AI becomes an attractive option.

    Albert and Frank are two marketing platforms that use machine learning to buy media and deliver ads for the best results. And this is done at scale and speed that no human could ever hope to achieve.

    Email marketing

    Making email smarter instead of a blunt tool for just broadcasting is something that AI can offer. AI can improve the delivery time for achieving the best open rates, target customers with the right emails and even product recommendations that they want to buy.

    McKinsey research estimates that Amazon generated 35% of its revenue with email product recommendations driven by AI.

    6. Video

    Video continues to emerge as a visual marketing trend that sits in a variety of buckets. For simplicity they can place them in 3 categories. Traditional 30 second ad style, live and social pre-recorded for social.

    Traditional

    The traditional 30 second ad style video 20 years ago was typically created 4 times a year with a 2 million dollar budget for each. This continues but the 2 trends that are transforming video today are driven by social media realities of live streaming and 6 second videos.

    Live streaming

    Live streaming is the hot new trend with Facebook (Facebook Live), LinkedIn, Twitter with Periscope and YouTube with its “Live Events”

    According to Globalwebindex.net “As ad-blocking continues to grow in popularity it’s more important than ever for brands to engage consumers via entertaining content and native advertising”

    And live streaming is one of those forms of content.

    Image source: GlobalWebIndex

    But the challenge with any form of video is doing it well.

    The 6 second pre-recorded video

    According to AdAge, Facebook has already been telling its video advertisers to hit people with faster messages. The 6 second video emerged as an ideal ad length in a recent test conducted with Tropicana.

    Tropicana compared the results from Facebook ads that were 6, 15 and 30 seconds long. The shortest saw “higher brand metrics across the board,” Sandberg said.

    Companies like Shuttlerock are using technology to scale 6 second Facebook video ad and are riding the wave of what the data is telling us is the most effective length for a video.

    But the challenge for all video is still the messaging. Video is now often watched in silence with subtitles running so people can read while viewing.

    7. Influencer marketing

    The rise of ad blocking means that reaching your audience via influencers is on the rise. A study by PageFair and Adobe shows that online consumers are becoming more and more frustrated with adverts interrupting their browsing experience. As of June 2015, 198 million people used ad-blocking browser extensions.

    Today’s consumers prefer to make their purchasing decisions based on either recommendations from friends and family, or from online influencers they admire and trust.

    Also the reason influencer marketing trend is becoming entranced is its performance. Just check out these stats on the effectiveness of influencer marketing:

  • Businesses earn approximately $6.50 for every $1 they spend on influencer marketing.
  • 81% of marketers who have used influencer marketing deemed it to be effective.
  • 86% of women turn to social media platforms before making a purchase.
  • Influencer marketing delivers 11 times higher ROI than traditional forms of digital marketing.
  • There are also 2 distinct categories of influencer marketing. B2C which is generally about building brand awareness and B2B which often is more about lead generation and measurable results.

    8. Storytelling

    Despite all the tech there is a real movement to making their marketing more human. Companies are backing causes. Taking a stand whether it is for equal opportunity, ageism or sexism. Marketers and entrepreneurs are using “live” video to share their ideas and insights.

    But the trend that I am enjoying is seeing the rise and application of storytelling being woven back into the digital marketing landscape. It is being used more in blog posts and online presentations. It is being written into emails to stand out from what is often an ocean of bland information and data.

    We have been distracted too long by the shiny new tech toys and forgotten some of their humanity in the process.

    It’s also time to tell better stories.


    Automation and Technology Increase Living Standards

    Many Americans worry that automation will significantly reduce the need for human employees. Historical experience should help to alleviate many of these concerns. Technological advances have eliminated specific jobs and reduced prices, but the historical record shows this has left consumers with more money to spend elsewhere, increasing the demand for human labor in other sectors of the economy. Some prominent economists suggest that this time is different. They fear that advances in computer technology will substantially reduce the demand for human labor, especially less-skilled labor.

    The data suggest that these concerns are similarly misplaced. Productivity growth has slowed over the past decade. The less-skilled employees who are often seen as endangered by automation have seen their employment and compensation grow at above-average rates. Automation is changing the type of work Americans do, but not the overall need for human labor. Technological progress continues to enable Americans to attain higher living standards.

    Long-Standing Concerns

    Many analysts fear that technological advances will soon make much human labor redundant.[1] They predict that many employers will soon lack productive tasks for less-skilled Americans. Historically, these concerns surface most often when cyclical unemployment is high. During the Great Depression, British economist John Maynard Keynes predicted impending mass “technological unemployment”:

    In quite a few years—in their own lifetimes I mean—we may be able to perform all the operations of agriculture, mining, and manufacture with a quarter of the human effort to which they have been accustomed.…

    …We are being afflicted with a new disease of which some readers may not yet have heard the name, but of which they will hear a great deal in the years to come—namely, technological unemployment. This means unemployment due to their discovery of means of economising the use of labour outrunning the pace at which they can find new uses for labour.[2]

    After World War II, the American and British economies recovered and those fears subsided. They resurfaced in America again after the 1957 and 1960 recessions. In 1961, Time magazine reported:

    How much has the rapid spread of technological change contributed to the current high of 5,400,000 out of work? Labor Secretary Arthur Goldberg last week set up a special group to find an answer. While no one has yet sorted out the jobs lost because of the overall drop in business from those lost through automation and other technological changes, many a labor expert tends to put much of the blame on automation.…

    In the past, new industries hired far more people than those they put out of business. But this is not true of many of today’s new industries.... Today’s new industries have comparatively few jobs for the unskilled or semiskilled, just the class of workers whose jobs are being eliminated by automation.[3]

    Shortly afterward, the economy began a prolonged expansion that raised incomes and created millions of new jobs. By 1968, the unemployment rate fell to 3.4 percent.

    Lump of Labor Fallacy

    Fears of mass technological unemployment are predicated on a “lump of labor” model of the economy—the belief the economy needs a roughly fixed amount of work performed.[4] In this economic model, machines automating work formerly done by people reduce the total amount of work remaining for humans, reducing total employment. Keynes forecast an impending crisis of unwanted leisure. He suggested future societies would establish three-hour workdays to give everyone enough work to avoid boredom.[5]

    Almost all economists reject this model today. Economists have found that an almost unlimited amount of potential work exists in the economy because people’s material desires continue to expand. Virtually all Americans today enjoy material living standards vastly better than the wealthy of 1900. Nonetheless, most Americans today would purchase additional goods and services if they received a raise or bonus.

    Automation does reduce the human labor needed to produce particular goods and services, but it also reduces production costs. Competition forces firms to pass these savings on to their customers through lower prices. These lower prices lead consumers to buy more of the now less-expensive product and leave them with more money to spend elsewhere, increasing the demand for labor in those sectors of the economy. The amount of work in the economy expands to use the available labor supply.

    Economists strongly agree on this point. The University of Chicago recently asked a panel of prominent economists whether they agree that “advancing automation has not historically reduced employment in the United States.” Over three-fourths expressly agreed with that statement, and only one of the economists disagreed.[6]

    America’s economic history illustrates how technology reallocates—but does not eliminate—human labor. In 1910, approximately one-third of all Americans worked on farms,[7] food was expensive, and the typical family spent almost half its budget on food. By 1960, technological advances such as the tractor had reduced the proportion of Americans working on farms to well under one-tenth.[8] This transition did not lead to mass unemployment. Instead, former farmhands began working in offices and factories. They enjoyed less expensive food and newly available manufactured goods.[9]

    Since then the manufacturing sector has also found new ways to automate tasks. Between 1960 and 2014, the proportion of Americans working in factories fell by two-thirds even as output dramatically increased.[10] Former manufacturing workers moved into the service sector. They enjoyed even more affordable food, less expensive manufactured goods, and newly available services. As of 2003 the average family spent just one-eighth of its budget on food.[11]

    Greater Living Standards

    Technological progress enables employees to produce vastly more goods and services with their labor. This increases their compensation because competitive labor markets compel employers to pay employees proportionately to their productivity. Technological advances would only reduce aggregate employment if Americans stopped spending their increased earnings on new goods and services—something that has yet to happen.

     

    Chart 1 illustrates this, showing average U.S. hourly labor productivity between 1973 and 2014. Over this period, technological advances enabled employees’ average hourly productivity to increase by 108 percent. During that time period, the average hourly compensation of American employees increased almost as much—85 percent.[12] Chart 1 also shows the employment-to-population ratio for prime-age workers (25-year-olds to 54-year-olds).[13] The huge increase in automation and technology had little effect on employment rates. Instead, employers found jobs for the millions of women who entered the labor force in the 1970s and 1980s. Historically, technological progress has increased wages with little effect on total employment.

    Is This Time Different?

    In the aftermath of the Great Recession, fears about automation have resurfaced. Most notably, MIT Professors Erik Brynjolfsson and Andrew McAfee have raised these concerns. They and likeminded economists worry that advances in computer technology mean this time may be different. They believe technological advances will enable computers to eliminate most of the workforce. McAfee argues:

    When I see what computers and robots can do right now, I project that forward for two, three more generations, I think we’re going to find ourselves in a world where the work as they currently think about it is largely done by machines.[14]

    In particular, McAfee and Brynjolfsson worry about automation eliminating the jobs of unskilled and middle-skill employees. They agree technological progress creates opportunities for highly skilled employees who build and operate machines, but they fear that the economy will hold far fewer opportunities for less-skilled employees. As Brynjolfsson puts it:

    There are lots of examples of routine, middle-skilled jobs that involve relatively structured tasks and those are the jobs that are being eliminated the fastest. Those kinds of jobs are easier for their friends in the artificial intelligence community to design robots to handle them.… [Technological advances are] always destroying jobs. But right now the pace is accelerating. It’s faster they think than ever before in history. So as a consequence, they are not creating jobs at the same pace that they need to.[15]

    Labor market statistics do not support this concern. Productivity data show that the pace of automation has actually slowed in recent years. Over the past generation the earnings of less-skilled Americans have risen faster than the economy-wide average.

    Slow Productivity Growth. Businesses do not appear to be automating human tasks at a faster rate than before. If they were, this would increase measured labor productivity growth. The Bureau of Labor Statistics estimates productivity by dividing U.S. economic output by the total hours worked in the economy. A substantial increase in the pace of automation would allow businesses to produce as many or more goods with fewer hours of human labor. This would appear in the labor statistics as faster productivity growth.

     

    This has not happened. Chart 2 shows the year-over-year percent change in labor productivity for the non-farm business sector over the past four decades, as well as a four-year moving average that smooths annual fluctuations. Productivity growth increased noticeably in the late 1990s and the early 2000s. From 2003 onward, however, productivity growth trended downward. Average productivity jumped in 2009 as businesses going through layoffs tried to lay off their least productive employees. That surge immediately subsided. Since 2010, productivity has grown at an abnormally slow rate. In the most recent year of data, labor productivity actually fell 0.1 percent. Although employees are more productive now than in the past, overall productivity is increasing more slowly.

    Concerns about rapidly accelerating computing power increasing productivity so much it reduces total employment are fears about a future possibility. Over the past decade, productivity growth has slowed even as computer power has increased exponentially.

    The Earnings of Less-Skilled Employees Increase. Concerns about automation eliminating employment opportunities for less-skilled employees also do not show up in the data. Over the past generation their total compensation has increased rapidly.

    The Congressional Budget Office measures total labor market compensation—cash wages, salaries, and non-cash benefits, such as health care and retirement contributions—for each quintile of the income distribution.[16] Chart 3 shows the percent growth in total inflation-adjusted labor compensation for non-elderly childless households between 1979 and 2011 (the most recent data available).[17]

     

    Since 1979, labor market compensation grew the fastest in the top quintile of these households—up 69 percent. Contrary to popular impression, the next fastest growth in labor market compensation occurred in the bottom quintile. The average labor market compensation of households in the bottom fifth of non-elderly childless households grew 58 percent between 1979 and 2011—more than 25 percentage points faster than any of the middle three quintiles.

    Chart 4 shows a similar dynamic at work. It comes from the research of MIT economist David Autor. The chart depicts income growth for the 10 major occupational groupings in the U.S. economy, with those occupations ranked from left to right by the required level of skills. This figure looks only at wages, not total household compensation. Consequently, it is not directly comparable with Chart 3. Nonetheless, it shows the same pattern of the fastest earnings growth occurring in high-skill and low-skill occupations, with slower wage growth in moderately skilled jobs.

     

    Over the past generation, individuals at the bottom of the income distribution have seen their economic opportunities expand significantly. This is hard to reconcile with hypotheses that automation is eliminating the least-skilled employees’ jobs. Instead, it points to more complex effects of technological progress on the labor market.

    Limits of Automation

    Computers have both more and less power than most people perceive. Autor explains that machines are incredibly good at doing repetitive tasks that do not require any judgment or variation, such as calculating sums in an accounting spreadsheet or fitting a bolt in place on the assembly line. Computers typically do these tasks faster and more accurately than humans can. Employment has fallen rapidly in such “routine” occupations as automation has replaced human labor.

    However, computers have great difficulty performing non-routine tasks. Although more fluid algorithms that take into account computer “learning” possibilities are being refined, computers still do what their program tells them to—and nothing else. Computer programmers must specify in detail every contingency that the machine might encounter. What often looks like computers adapting to their surroundings is in fact them following very detailed operating instructions.[18]

    Consequently, computers cannot handle many non-routine activities that most people find straightforward. They are simply too complex for their programs to account for every possibility. For example, Autor points out that Amazon.com and other online retailers use human “pickers” to identify, retrieve, and pack the goods that they ship their customers. The shape and size of goods being shipped changes constantly from package to package. Amazon has not been able to develop robots that can perform these seemingly simple but not entirely routine tasks. Instead, online retailers use large numbers of robots to bring palettes of particular goods to their human employees. Humans do all the labor involved in handling individual items, then the robots move the palettes away.[19]

    Even some of the apparent successes of automation are far less than they appear. Google’s advances in self-driving automobile technology have made headlines. However, the Google Car operates by comparing its location to very detailed maps of the road, street signs, and all known obstacles. Google employees must enter these data manually. The Google Car cannot operate over unfamiliar terrain. If it faces an unmapped road closure or detour, it shuts down and requires a human driver to take over. It will ignore newly erected stoplights not in its database. Google Cars have safely driven more than 700,000 miles—by driving over the same already mapped miles time and time again.[20] Computers can do routine tasks incredibly well, but struggle when confronted with non-routine work.

    Labor Market Polarization

    Autor’s research shows that this dynamic explains the counterintuitive pattern of compensation growth shown in Charts 3 and 4. Computers have automated many routine white collar and blue collar jobs. Excel spreadsheets and Outlook calendars have dramatically reduced the need for accountant and secretarial labor. Machines now do the work that was once performed by millions of manufacturing employees. These routine jobs tend to lie in the middle of the skill and income distribution. Non-routine tasks tend to lie at the top and bottom of the income distribution. As a result, employment demand and, consequently, earnings have risen more rapidly in non-routine jobs, particularly in the service sector.[21]

     

    Chart 5, reproduced from David Autor’s research, illustrates how increased automation has affected employment patterns. Since the late 1970s, employment has grown rapidly in high-skilled non-routine jobs, such as professional and technical occupations. It has grown rapidly in low-skill non-routine jobs, such as food preparation and personal care. Yet employment has grown more slowly—or contracted—in routine occupations requiring moderate skill levels, such as manufacturing or administrative record-keeping jobs. These are precisely the jobs that machines can perform.

    Many on the left blame the slower growth of middle-income jobs on U.S. policies. They point in particular to insufficiently pro-union labor laws.[22] However, Autor’s research shows that this is a global phenomenon. Relative employment in middle-skill jobs has shrunk in nearly every developed country. Chart 6 comes from Autor’s research and shows changes in low-skill, middle-skill, and high-skill employment for 16 European Union countries between 1993 and 2010. In almost every country, relative employment increased in high-skill and low-skill jobs and decreased in middle-skill jobs. Most of these EU nations have far higher taxes and far stronger unions than the U.S. does. Nonetheless, they experienced the same employment patterns. This evidence points to factors, such as technological advances and globalization, that cut across national boundaries and public policy choices. Robots have not eliminated work, but they have somewhat changed the types of jobs that humans do.

     

    Technology Can Increase the Need for Human Labor

    The relationship between technological progress and jobs is more complex than computers simply eliminating routine work. Many jobs incorporate both routine and non-routine tasks. Employees in these jobs do not necessarily need to fear automation. By eliminating routine tasks technological advances reduce the time and cost of completing their work. This increases output and can leave the overall need for human labor unchanged or even increased.

    The construction industry demonstrates this effect. Technology has made today’s construction workers vastly more productive than their predecessors two generations ago. Cranes and backhoes have replaced shovels and elbow grease, but those machines need human operators. Too many unpredictable events take place on a construction site to allow computers to operate the equipment autonomously. The lower cost of constructing buildings has also dramatically increased the quantity of construction work demanded. As a result, total construction employment has remained a relatively constant share of the overall workforce since the mid-1940s. From 1946 onward, construction employment has never constituted less than 4 percent or more than 6 percent of the U.S. workforce, despite enormous technological progress.

    A more modern example of this phenomenon comes from restaurant tablets. Applebee’s, Chili’s, and other casual restaurants have installed tabletop tablets for customers to order and pay for their food. The new technology might reduce payrolls by allowing each server to cover more tables. However, the tablets also boost sales. Customers are more likely to order appetizers and desserts when the tablets constantly display them. The ability to pay immediately also cuts the average meal time by about five minutes. Consequently, tablet-equipped restaurants can serve more patrons during busy periods. This increases demand for employees who cook the food to order, appetizingly plate it, interact with customers, and bus the tables afterward.[23]

    Whether or not these tablets will reduce the total need for human labor remains unclear. Applebee’s announced that it has not reduced total staffing since introducing the tablets.[24] Furthermore, tablets also increase tips by setting the default option to 20 percent, boosting servers’ take-home pay. Automation will change—but not eliminate—many jobs that combine routine and non-routine tasks.

    Future Developments

    Historical experience shows that individuals respond to technological changes by finding new jobs, typically jobs that pay more than before automation was introduced. However, technology will probably eliminate some existing occupations. Programmers will almost certainly learn how to render “routine” many tasks computers cannot currently handle. Many jobs that once appeared out of reach for automation are now being performed by machines:

  • Cleaning hotel rooms has long required human labor, supplemented by technology such as vacuum cleaners and washing machines. The tasks of making a bed or removing dirty laundry from the floor were sufficiently non-routine to frustrate attempts to automate them. However, a soon-to-open Japanese hotel will use robots to perform reception duties, carry luggage, and clean rooms. The hotel will charge $60 a night.[25]
  • Engineers have invented a machine that cooks 360 gourmet hamburgers an hour. The Alpha custom grills hamburger patties to order, sears the outside to maximize flavor, and cooks them in an internal oven. It then adds freshly sliced toppings and the desired condiments, places the cooked burger in a bun, and bags it. Humans only stock the ingredients and perform maintenance. The Alpha could save the typical fast food restaurant more than $100,000 per year in labor costs. The inventors are currently prototyping it at individual restaurants.[26]
  • Computers have begun writing routine news articles. Many events occur with little variation in the structure needed to report on them, such as economic news releases and sports events. Computer algorithms now analyze pertinent information for readers and translate it into prose. For example, Forbes.com uses such algorithms to summarize corporate earnings announcements. Computers cannot handle more complex stories and are unlikely to develop that ability, but they can summarize regularly recurring events well.[27]
  • IBM’s Watson computer is so sophisticated that it can detect correlations among research papers that human researchers have yet to discover. For example, in the field of cancer research, Watson analyzed more than 70,000 academic articles in 2014, leading to the discovery of six proteins that should be targeted for new research. One scientist noted that Watson made connections that he would have needed 38 years to make and only by reading five academic papers per day.[28] By making these types of groundbreaking causal links, Watson has demonstrated a marked technological advancement. However, it represents technology’s ability to find connections quicker than humans, not an ability to generate original research and new ideas.
  • Technological advancements like these will reshape the way that millions of employees do their jobs. Some jobs will disappear, but new tasks—primarily non-routine tasks—will replace jobs that have been automated. Such changes do not happen instantaneously, and most people will have time to adapt. Those who cannot adapt could be hurt, but automation will lower prices and raise living standards in the economy overall. Most Americans will prosper as a result.

    Responding to Technological Innovation

    Technological innovation will continue. Policymakers should respond to these challenges by promoting policies that make it easier for Americans to find new jobs.

    For example, one-third of jobs in the economy require a government license.[29] In some occupations this makes sense. Few customers would want an untrained pharmacist filling their prescription. Yet in many other occupations public safety does not require stringent licensing; it primarily exists to restrict access to a profession. For example, every state licenses barbers, requiring an average of more than a year of training before prospective barbers can cut hair.[30] These requirements have no obvious safety rationale: A bad haircut threatens no one’s life. Such excessive licensing makes it difficult for employees who lose their jobs to automation to switch occupations. State legislatures should restrict mandatory licensing to occupations with serious health and safety considerations. Potential cosmetologists, florists, interior designers, bartenders, and drywall installers should not need the government’s permission to change careers. Reducing these artificial barriers would make it easier for employees to adapt in a changing economy.

    State and federal policymakers can also make it easier for employees to switch jobs by eliminating unnecessary paper credentials for government positions. The K–12 education system is a large employer and continues to use paper credentials, such as master’s degrees, to structure compensation and determine access to the classroom. States should make it easier to enter the classroom by removing barriers to entry such as teacher certification requirements, but evaluate teachers more rigorously once they are in the classroom.

    Education Reforms

    Beyond helping individuals switch jobs, policymakers should reform the education system to help tomorrow’s employees gain the skills necessary to work in higher-paying non-routine jobs. Policymakers can do this in several ways.

    States should move toward competency-based learning for both K–12 and higher education. Competency-based learning enables students to progress in their education as soon as they can demonstrate content mastery, instead of using seat time as a proxy for learning. It also enables students with professional experience or training to test out of courses and expedite their entry into the workforce.

    Public policy reforms are needed to allow innovation to flourish in high schools, colleges, and career and technical fields. One of the keys to unlocking innovation is to get the federal government out of the higher education accreditation business and to hand that responsibility back to the market. The current regulatory barriers make it prohibitively expensive for most potential new education institutions to teach students. To foster a competitive marketplace of higher education content providers—be it academic or career-technical—federal policymakers should free the higher education regulatory environment so that businesses, industry, nonprofits, and colleges and universities can deliver content to prospective students from all walks of life to give them the skills needed to be successful in an ever-changing economy.

    Specifically, Congress should decouple federal financing (federal student loans and grants) from accreditation and enable states to allow any entity to accredit and credential courses. Senator Mike Lee (R–UT) and Representative Ron DeSantis (R–FL) have introduced companion proposals known as the Higher Education Reform and Opportunity Act (H.R. 1287 and S. 649), which would allow states to determine who can accredit and credential courses and, importantly, would allow individual courses to be credentialed. Reforms to remove the “gatekeeper” function of accreditation could also be achieved by amending the Higher Education Act to decouple federal financing from accreditation. As Senator Lee explains:

    [A]ccreditation could also be available to specialized programs, individual courses, apprenticeships, professional credentialing, and even competency-based tests. States could accredit online courses, or hybrid models with elements on- and off-campus… businesses, and trade groups could start to accredit courses and programs tailored to their evolving needs. Churches and charities could enlist qualified volunteers to offer accredited classes and training for next to nothing.[31]

    The current regulatory system stifles innovation and makes it harder for individuals outside the traditional college demographic to improve their skills. Such reforms would make higher education less bureaucratic and more responsive to individual’s needs.

    Conclusion

    Automation reduces both labor costs and prices. Lower prices leave customers with more money to spend elsewhere, increasing the demand for labor elsewhere in the economy. Automation changes where and how people work, but it has not historically reduced the overall need for human employees.

    Little empirical evidence suggests this time is different. Productivity growth slowed over the past decade after increasing in the late 1990s. The wages of the lowest-earning employees have also increased rapidly over the past generation. Instead of eliminating human labor, technological advances are reducing the need for humans in routine jobs and increasing the need in non-routine jobs. This pattern has occurred in America and around the world.

    Policymakers should respond to these changes by making it easier for displaced workers to switch jobs, such as by relaxing occupational licensing requirements and moving toward policies that allow for a more nimble K–12 and higher education system to flourish.

    —James Sherk is Research Fellow in Labor Economics in the Center for Data Analysis, of the Institute for Economic Freedom and Opportunity, at The Heritage Foundation. Lindsey M. Burke is the Will Skillman Fellow in Education Policy in the Institute for Family, Community, and Opportunity at The Heritage Foundation.


    The Best Self-Service Business Intelligence (BI) Tools of 2018

    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 their 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 their roundup. However, all three products received their 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 their 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 their 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 they 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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