Category Archives: Industrial

Gartner’s Magic Quadrant for Business Intelligence

Gartner’s Magic Quadrant for Business Intelligence

Note: See also the more recent update on the Magic Quadrant for Business Intelligence 2013.

The Gartner group publishes an annual report called Magic Quadrant of Business Intelligence. It compares various vendors in two dimensions: Ability to Execute and Completeness of Vision. These two dimensions span up four quadrants (leaders, challengers, visionaries, niche players).

The key graphic in the Gartner reports is the so called Magic Quadrant diagram. Here is the 2012 version (click the image to see the full report):

Magic Quadrant of BI 2012 (Source: Gartner)

Similar charts have been published for 2011, 2010, 2009, and 2008 (source: Google Image Search).

From these snapshots in time one can create a time-series and compare relative movement of vendors. Here is an interactive version of such a chart created with Tableau Public: (Click on chart below to interact.)

Interactive BI_MagicQuadrant 2008-2012

Disclaimer: There are at least two caveats here: One is the limited quality of the data. The other is the limited applicability of this type of visualization.

Quality: I have contacted two of the authors at Gartner and asked for the (x,y-coord) data of those Magic Quadrants. However, Gartner’s policy is to not disclose these data. Hence I screen-scraped the coordinates off the publicly available images. This brings with it limited accuracy to measure the positions from the images and the possibility of (my) clerical error in entering that data in a spreadsheet.

Applicability: The contacted authors (James Richardson and John Hagerty) both emphasized that due to subtle changes in the way the dimension score is calculated each year such sequential comparisons are not supported by Gartner. In other words, the data may show misleading or unintended conclusions.

Discussion: Of course the original Gartner reports provide a tremendous amount of detail, both around the methodology (which factors contribute to Vision and Execution scores) and on the various vendors, their products and other relevant business aspects like sales channels etc. One also needs to bear in mind that some of these companies emerge or disappear over time.

That said, the interactive time-series chart has many advantages over the individual snapshots:

  • You can select a subset of companies (for example all public companies)
  • Companies are identified by label and color
  • History can be traced for consecutive years
  • Trends are more easily detected (see also Disclaimer above)

For example, smaller but rapidly growing companies like Tibco (Spotfire) and Tableau have somewhat vertical trajectories leading them into the “challenger” quadrant with strong increases in the ability to execute. Tibco and QlikTech are the only 2 (of 24) companies to change quadrants in the last 5 years, from visionary to challenger (Tibco) and leader (QlikTech), respectively.

MQ trajectory for Tableau, Tibco, and QlikTech

Some big public companies like IBM, SAP and Microsoft have invested heavily over the last years in the BI space. This has resulted in a more horizontal trajectory within the leader quadrant as they have increased the completeness of their vision, among others through acquisitions of smaller companies (SAP bought Business Objects, IBM bought Cognos).

MQ trajectory for IBM, SAP, and Microsoft

Some individual trajectories are more dynamic than others. For example MicroStrategy has had strong increases first in vision (2008-2009) and then in their ability to execute (2010-2012). By contrast, Actuate has fallen behind relative to others in both execution and vision in the first 3 years, only to stop (2011) and revert (2012) that trend in recent years.

MQ trajectory for Actuate and MicroStrategy

Bottom Line: Data presented via Interactive Charts invites exploration, discovery, and better understanding. Through Tableau Public these charts can easily be shared with others. The Magic Quadrant data is originally curated and presented by Gartner in the traditional snapshot moment-in-time format. IMHO, in this interactive time-series format the data comes to live and yields additional insight. I’d be interested to hear your thoughts and comments on the caveats from the authors about the limited applicability of the time series animation?


Posted by on February 20, 2012 in Industrial


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Side by Side: Apple, Microsoft, Google, Amazon

Side by Side: Apple, Microsoft, Google, Amazon

Ed Bott at wrote a post with the title: Microsoft, Apple, and Google: where does the money come from? He looked at the quarterly reports of these companies (links to sources in the article) and displayed a pie-chart of the revenue mix for each of them. Inspired by that, I added a fourth company – Amazon (source: 10-K for 2011) – and aggregated those pie-charts into one graphic.

Revenue Mix for Apple, Microsoft, Google, and Amazon

All four are large consumer-oriented technology companies; like millions of customers, I use many of their products and services every day. They each operate with different businesses models:

  • Microsoft: Software
  • Apple: Hardware
  • Google: Advertising
  • Amazon: Retail

Yet as a consumer I rarely think about these differences. All of them use state-of-the-art technologies like cloud-computing and mobile devices to achieve integrated end-to-end experiences geared to increase revenues in personal computing (Microsoft), smart mobile devices (Apple), online search (Google) or shopping (Amazon). And arguably all of them derive major competitive advantage from their software, such as Apple’s iOS which introduced the touch interface.

Perhaps most surprising is Google’s almost singular reliance on advertising, which makes it a very different business model. They offer all their technology for free – from search to mapping to operating systems and social media – to grow and retain online attention as enabling condition for advertising revenue. For a business this big the near complete dependence on one source of revenue is unusual; perhaps its time for Google’s leadership to seriously consider a diversification strategy? Without it Google is arguably more prone to disruption (such as from Facebook) than the other companies. Speaking of disruption: Apple derives almost 3/4 of its revenue (73%) from iPhone and iPad, neither of which existed 5 years ago. As Ed Bott points out, those two products now drive an astonishing $33.5b revenue per quarter!

To compare the companies by their absolute numbers, here is a bar chart of market capitalization, revenue and profit: (all in billions of Dollars and for Q4 2011, market cap as of 2/3/12)

Market Cap, Revenue and Profit for Apple, Microsoft, Google, and Amazon

Market cap of these four companies combined is approaching $ 1 trillion. Much has been written about the differences in market valuations relative to revenue and most importantly profit. The markets undervalue Apple and overvalue Google and Amazon. Let’s compare these dimensions (and number of employees) in the following radar plot:

Relative business performance for Apple, Microsoft, Google, and Amazon

The plot shows the relative performance of all with the highest in each dimension normalized to 100%. Amazon shows by far the smallest profit in the last quarter. Given it’s retail nature, it’s profit margins have always been smaller; and CEO Jeff Bezos has long emphasized the strategy of investing in future growth at the expense of present profits. Microsoft continues to enjoy very solid profit margins in a large, well diversified business. Google has incredible talent and for now is the undisputed king of online advertising. But Apple leads in all three factors, and it achieves 2x Microsoft’s results with less than half the number of employees! Apple’s profit is 1.5 times that of the other three combined! And it makes more than 60% of the profit with less than 20% of the employees. In fact, Apple’s market capitalization is now higher than $10m per employee! It must feel pretty special to be one of them these days…

Postscript: On Feb-13 analyst Horace Dediu at published an article with time-series data for the above companies (except Amazon) over the last 18 quarters (since 2007). It shows the evolution over time as depicted in this chart:

Apple Microsoft Google - Revenue and Operating Income 2007-2011

The article is called “The World’s Biggest Startup“. It’s main point is this: Microsoft and Google both grew their businesses steadily, but did not change their type of business. Apple did some of that in its established business segments, but more importantly and disruptively it added new categories (iPhone and iPad) for dramatic growth. That’s what startups do. Just so happens that Apple – whose stock today for the first time hit $500 – is also the most highly capitalized company in the world (around $460B). If Apple is a startup now, what will they look like when they are fully established?


Posted by on February 6, 2012 in Financial, Industrial


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Futuristic TouchScreen Visualization

Futuristic TouchScreen Visualization

Glass manufacturer Corning has published the second YouTube video in its series “A Day Made of Glass”. It provides a glimpse into the future of ubiquitous touchscreen glass displays, from the car dashboard to the kitchen refrigerator and wall-to-wall home display, the large school community table to the medical laboratory, even the glass wall in an outdoor theme park.

Corning Day Of Glass 2

Mashable writes in its story about the video that it “will blow your mind”. Hyperbole aside, it is worth watching (click on image above). The script goes through a typical day and shows various display applications; then it pauses the scenes and mentions the underlying technological challenges and whether the depicted displays are possible and feasible with today’s technology. From the video:

“Of course, this is not just a story about glass. It’s a story about a shift in the way we will communicate and use technology in the future. It’s a story about ubiquitous displays, open operating systems, shared applications, cloud media storage and unlimited bandwidth. We know there are many obstacles to be overcome before what we’ve just seen will become an attainable, reliable reality. But at Corning, we believe in this vision – and we are not waiting.”

Besides being a great corporate promotional piece, the 11 min video is a great example of how interactive, even immersive visualizations can change how we consume and interact with information and with one another.
Apple created a video back in 1987 titled “Knowledge Navigator” which seemed similarly futuristic at the time. Today, 25 years later, the iPad is in common use. Interactive touch screens have become the norm for smart phones since Apple launched the iPhone in 2007, just 5 years ago. Larger form factors exist, but are still expensive to build.

Regardless of how long it will take for touch screen displays to get bigger and become ubiquitous, the notion of interactive data visualization will only become more valuable.

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Posted by on February 5, 2012 in Industrial, Medical, Recreational, Scientific


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Inequality Comparison

Inequality Comparison

In previous posts on this Blog we have looked at various inequalities as measured by their respective Gini Index values. Examples are the posts on Under-estimating Wealth Inequality, Inequality on Twitter, Inequality of Mobile Phone Revenue, and how to visualize as well as measure inequality.

Here is a bubble chart comparison of 14 different inequalities:

Comparison of various Inequalities



  • P1: Committee donations to 2012 presidential candidates (2011, Federal Election Commission)
  • P2: US political donations to members of congress and senate (2010, US Center for Responsive Politics)
  • A1: Twitter Followers (of my tlausser account) (2011, Visualign)
  • A2: Twitter Tweets (of my tlausser account) (2011, Visualign)
  • I1: Global Share of Tablet shipment by Operating System (2011,
  • I2: Mobile Phone Shipments (revenue) (2009,
  • I3: US Car Sales (revenue) (2011,
  • I4: Market Cap of Top-20 Nasdaq companies (2011, Nasdaq)

    The x-axis shows the size of the population in logarithmic scale. The y-axis is the Gini value. The “80-20 rule” corresponds to a Gini value of 0.75. Bubble size is proportional to the log(size), i.e. redundant with the x-axis.


    Most of the industrial inequalities studied have a small population (10-20); this is usually due to the small number of competitors studied or a focus on the Top-10 or Top-20 (for example in market capitalization). With small populations the Gini value can vary more as one outlier will have a disproportionately larger effect. For example, the Congressional Net Worth analysis (top-left bubble) was taken from a set of 25 congressional members representing Florida (Jan-22, 2012 article in the Palm Beach Post on net worth of congress). Of those 25, one (Vern Buchanan, owner of car dealerships and other investments) has a net worth of $136.2 million, with the next highest at $6.4 million. Excluding this one outlier would reduce the average net worth from $6.9 to $1.55 million and the Gini index from 0.91 (as shown in the Bubble Chart) to 0.66. Hence, Gini values of small sets should be taken with a grain of salt.

    The studied cases in attention inequality have very high Gini values, especially for the traffic to websites (top-right bubble), which given the very large numbers (Gini = 0.985, Size = 1 billion) is the most extreme type of inequality I have found. Attention in social media (like Twitter) is extremely unevenly distributed, with most of it going to very few alternatives and the vast number of alternatives getting practically no attention at all.

    Political donations are also very unevenly distributed, considerably above the 80-20 rule. The problem from a political perspective is that donations buy influence and such influence is very unevenly distributed, which does not seem to be following the democratic ideals of the one-person, one-vote principle of equal representation.

    Lastly, economic inequalities (wealth, income, capital gains, etc.) are perhaps the most discussed forms of inequality in the US. Inequalities at the level of all US households or citizens measure large populations (100 – 300 million). One obvious observation from this Bubble Chart is that capital gains inequality is far, far higher than income inequality.

    Tool comment: I have used Excel 2007 to collect the data and create this chart. Even though it is natively supported in Excel, the Bubble Chart has a few restrictions which make it cumbersome. For example, I haven’t found a way to use Data Point labels from the spread-sheet; hence a lot of manual editing is required. I also don’t know of a way to create animated Bubble-Charts (to follow the evolution of the bubbles over time) similar to those at GapMinder. Maybe I need to study the ExcelCharts Blog a bit more… If you know of additional tips or tweaks for BubbleCharts in Excel please post a comment or drop me a note. Same if you are interested in the Excel spread-sheet.

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    Posted by on February 3, 2012 in Industrial, Socioeconomic



    Treemap of Top 1 Percent Occupations

    Treemap of Top 1 Percent Occupations

    On Jan 15, 2012 the New York Times published an interactive Treemap graphic with the title: “The Top 1 Percent: What Jobs Do They Have?”

    Treemap of Top 1 Percent Professions (Source: New York Times)

    It is a good example of the Treemap chart we have covered in previous posts (Treemap of the Market and Implementation of Treemap). From the chart legend:

    “Rectangles are sized according to the number of people in the top 1 percent. Color shows the percentage of people within that occupation and industry in the top 1 percent.”

    There are approx. 1.4 million households in the top 1 percent; they earn a minimum of about $500k per year, with an average annual income around $1.5m (according to this recent compilation of 10 fun facts about the top 1 percent).

    The largest and darkest area in the Treemap are Physicians. Chief Executives and Public Administrators as well as Lawyers are also doing very well, not surprisingly, especially in Security, commodity broker and investment companies. The graphic nicely conveys the general notion that big money is in health, financial and legal services.

    One thing to keep in mind is that the chart counts the number of individual workers living in households with an overall income in the top 1 percent nationwide. This skews the picture a bit, since an individual with a low-earning occupation can still live in a top 1 percent household through being married to a top-earning spouse. If you looked at individuals only, the number of top 1 percent earners in occupations such as teacher, receptionist, waiter, etc. would certainly be much smaller.

    P.S: I stumbled across this particular chart from Sha Hwang’s “UltraMapping” at PInterest, which is a great collection of maps and other graphics for design inspiration.

    UltraMapping collection of maps (source: Sha Hwang via Pinterest)

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    Posted by on February 2, 2012 in Industrial, Socioeconomic


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    Global Trends 2025

    Global Trends 2025

    If you like to do some big-picture thinking, here is a document put together by the National Intelligence Council and titled “Global Trends”. It is published every five years to analyze trends and forecast likely scenarios of worldwide development fifteen years into the future. The most recent is called “Global Trends 2025” and was published in November 2008. It’s a 120 page document which can be downloaded for free in PDF format here.

    To get a feel for the content, here are the chapter headers:

    1. The Globalizing Economy
    2. The Demographics of Discord
    3. The New Players
    4. Scarcity in the Midst of Plenty?
    5. Growing Potential for Conflict
    6. Will the International System Be Up to the Challenges?
    7. Power-Sharing in a Multipolar World

    From the NIC Global Trends 2025 project website:

    Some of our preliminary assessments are highlighted below:

    • The whole international system—as constructed following WWII—will be revolutionized. Not only will new players—Brazil, Russia, India and China— have a seat at the international high table, they will bring new stakes and rules of the game.
    • The unprecedented transfer of wealth roughly from West to East now under way will continue for the foreseeable future.
    • Unprecedented economic growth, coupled with 1.5 billion more people, will put pressure on resources—particularly energy, food, and water—raising the specter of scarcities emerging as demand outstrips supply.
    • The potential for conflict will increase owing partly to political turbulence in parts of the greater Middle East.

    As interesting as the topic may be, from a data visualization perspective the report is somewhat underwhelming. I counted just 5 maps and 5 charts in the entire document. The maps are interesting, such as the following on World Age Structure:

    World Age Structure 2005

    World Age Structure 2025 (Projected)

    These maps show the different age of countries’ populations by geographical region. The Northern countries have less young people, and the aging trend is particularly strong for Eastern Europe and Japan. In 2025 almost all of the countries with very young population will be in Sub-Saharan Africa and the Arab Peninsula. Population growth will slow as a result; there will be approximately 8 billion people alive in 2025, 1 billion more than the 7 billion today.

    In this day and age one is spoiled by interactive charts such as the Bubble-Charts of Gapminder’s Trendalyzer. Wouldn’t it be nice to have an interactive chart where you could set the Age intervals and perhaps filter in various ways (geographic regions, GDP, population, etc.) and then see the dynamic change of such colored world-maps over time? How much more insight would this convey about the changing demographics and relative sizes of age cohorts? Or perhaps display interactive population pyramids such as those found here by Jorge Camoes?

    Another somewhat misguided ‘graphical angle’ are the slightly rotated graphics on the chapter headers. For example, Chapter 2 starts with this useful color-coded map of the Youth in countries of the Middle East. But why rotate it slightly and make the fonts less readable?

    Youth in the Middle East (from Global Trends 2025 report)

    I don’t want to be too critical; it’s just that reports put together with so much systematic research and focusing on long-range, international trends should employ more state-of-the-art visualizations, in particular interactive charts rather than just pages and pages of static text…


    Posted by on January 4, 2012 in Industrial, Socioeconomic


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    Inequality on Twitter

    Inequality on Twitter

    A lot has been written about economic inequality as measured by distribution of income, wealth, capital gains, etc. In previous posts such as Inequality, Lorenz-Curves and Gini-Index or Visualizing Inequality we looked at various market inequalities (market share and capitalization, donations, etc.) and their respective Gini coefficients.

    With the recent rise of social media we have other forms of economy, in particular the economy of time and attention. And we have at least some measures of this economy in the form of people’s activities, subscriptions, etc. Whether it’s Connections on LinkedIn, Friends on FaceBook, Followers on Twitter – all of the social media platforms have some social currencies for attention. (Influence is different from attention, and measuring influence is more difficult and controversial – see for example the discussions about Klout-scores.)

    Another interesting aspect of online communities is that of participation inequality. Jakob Nielsen did some research on this and coined the well-known 90-9-1 rule:

    “In most online communities, 90% of users are lurkers who never contribute, 9% of users contribute a little, and 1% of users account for almost all the action.”

    The above linked article has two nice graphics illustrating this point:

    Illustration of participation inequality in online communities (Source: Jakob Nielsen)

    As a user of Twitter for about 3 years now I decided to do some simple analysis, wondering about the degrees of inequality I would find there. Imagine you want to spread the word about some new event and send out a tweet. How many people you reach depends on how many followers you have, how many of those retweet your message, how many followers they have, how many other messages they send out and so on. Let’s look at my first twitter account (“tlausser”); here are some basic numbers of my followers and their respective followers:

    Followers of tlausser Followers on Twitter

    Some of my followers have no followers themselves, one has nearly 100,000. On average, they have about 3600 followers; however, the total of about 385,000 followers is extremely unequally distributed. Here are three charts visualizing this astonishing degree of inequality:

    Of 107 followers, the top 5 have ~75% of all followers that can be reached in two steps. The corresponding Gini index of 0.90 is an example of extreme inequality. From an advertising perspective, you would want to focus mostly on getting these 5% to react to your message (i.e. retweet). In a chart with linear scale the bottom half does barely register.

    Most of my followers have between 100-1000 followers themselves, as can be seen from this log-scale Histogram.

    What kind of distribution is the number of followers? It seems that Log[x] is roughly normal distributed.

    As for participation inequality, let’s look at the number of tweets that those (107) followers send out.

    Some of them have not tweeted anything, the chattiest has sent more than 16,000 tweets. On average, each follower has 1280 tweets; the total of 137,000 tweets is again highly unequally distributed for a Gini index of 0.77.

    The top 10 make up about 2/3 of the entire conversation.

    Again the bottom half hardly contributes to the number of tweets; however, the ramp in the top half is longer and not quite as steep as with the number of followers. Here is the log-scale Histogram:

    I did the same type of analysis for several other Twitter Users in the central range (between 100-1000 follower). The results are similar, but certainly not yet robust enough to statistical sampling errors. (A larger scale analysis would require a higher twitter API limit than my free 350 per hour.)

    These preliminary results indicate that there are high degrees of inequality regarding the number of tweets people send out and even more so regarding the number of followers they accumulate. How many tweets Twitter users send out over time is more evenly distributed. How many followers they get is less evenly distributed and thus leads to extremely high degrees of inequality. I presume this is caused in part due to preferential attachment as described in Barabasi’s book “Linked: The new science of networks“. Like with all forms of attention, who people follow depends a lot on who others are following. There is a very long tail of small numbers of followers for the vast majority of Twitter users.

    That said, the degree of participation inequality I found was lower than the 90-9-1 rule, which corresponds to an extreme Gini index of about 0.96. Perhaps that’s a sign of the Twitter community having evolved over time? Or perhaps just a sign of my analysis sample being too small and not representative of the larger Twitterverse.

    In some way these new media are refreshing as they allow almost anyone to publish their thoughts. However, it’s also true that almost all of those users remain in relative obscurity and only a very small minority gets the lion share of all attention. If you think economic inequality is too high, keep in mind that attention inequality is far higher. Both are impacting the policy debate in interesting ways.

    Turning social media attention into income is another story altogether. In his recent Blog post “Turning social media attention into income“, author Srininvas Rao muses:

    “The low barrier to entry created by social media has flooded the market with aspiring entrepreneurs, freelancers, and people trying to make it on their own. Standing out in it is only half the battle. You have to figure out how to turn social media attention into social media income. Have you successfully evolved from blogger to entrepreneur? What steps should I take next?”


    Posted by on December 6, 2011 in Industrial, Scientific, Socioeconomic


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