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Magic Quadrant Business Intelligence 2014

Over the last two years we have posted some visualization and interpretation of Gartner’s Magic Quadrant Analysis on BI companies. The previous articles in 2012 and 2013.

A Blog reader contacted me about the 2014 update; he sent me the {x,y} coordinate data for 2014 and so it was relatively straightforward to update the public Tableau workbook for it. Here is the image of all 29 companies with their changes from 2013 to 2014:

Gartner’s Magic Quadrant for Business intelligence, changes from 2013 to 2014

Gartner’s Magic Quadrant for Business intelligence, changes from 2013 to 2014

With the slider controls for Execution and Vision as well as the changes thereof, it is easy to filter the dashboard interactively. For example, there were a dozen companies who improved in their execution score (moving up in the quadrant):

Subset of companies who improved execution over the last year.

Subset of companies who improved execution over the last year.

Most of the companies improving their execution are niche players, with SAP being the only leader improving its execution score.

Most of the leaders improved in their vision score (moving right in the quadrant), including Tableau, QlikTech, Tibco and SAS.

Subset of companies who improved vision over the last year.

Subset of companies who improved vision over the last year.

 

7 companies, most of them leaders, lost ground on both execution and vision (moving to the bottom-left):

Companies who lost ground on both execution and vision in 2014

Companies who lost ground on both execution and vision in 2014

 

Lastly, I have updated the Public Tableau workbook with the Magic Quadrant as originally published in 2012 with the data for 2013 and 2014. (Click here for the interactive drawing.)

Public Tableau workbook with 7 years of BI Magic Quadrant data.

Public Tableau workbook with 7 years of BI Magic Quadrant data.

 
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Posted by on September 28, 2014 in Industrial

 

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Magic Quadrant Business Intelligence 2013

It’s that time of the year again: Gartner has released its report on Business Intelligence and Analytics platforms. One year ago we looked at how the data in the Magic Quadrant – the two-dimensional space of execution vs. vision – can be used to visualize movement over time. In fact, the article Gartner’s Magic Quadrant for Business Intelligence became the most viewed post on this Blog.

I had also uploaded a Tableau visualization to Tableau Public, where everyone can interact with the trajectory visualization and download the workbook and the underlying data to do further analysis. This year I wanted to not only add the 2013 data, but also provide a more powerful way of analyzing the dynamic changes, such as filtering the data. For example, consider the moves from 2012 to 2013 of some 21 vendors:

Gartner's Magic Quadrant for Business intelligence, changes from 2012 to 2013

Gartner’s Magic Quadrant for Business intelligence, changes from 2012 to 2013

It might be helpful to filter the vendors in this diagram, for example to show just niche players, or just those who improved in both vision and execution scores. To that end, I created a simple Tableau dashboard with four filters: A range of values for the scores of both vision and execution scores, as well as a range of values for the changes in both scores. The underlying data is also displayed for reference, which can then be used to sort companies by ordering along those values.

Here is an example of the dashboard set to display the subset of 15 companies who increased either both or at least one of their vision or execution scores without lowering the other one.

Subset of companies who improved vision and/or execution over the last year.

Subset of companies who improved vision and/or execution over the last year.

That’s more than 70% of platforms, with the increase in vision being more pronounced than that of execution. That’s considerably more than in the previous years (2013: 15; 2012: 6; 2011: 6; 2010: 3; 2009: 9) – making this collective move to the top-right perhaps a theme of this year’s report.

Who changed Quadrants? Who moved in which dimension?

Last year Tibco (Spotfire) and Tableau were the only two platforms changing quadrants, then becoming challengers. This year both of them “turned right” in their trajectory and crossed over into the leaders quadrant due to strong increases in their vision capabilities. (QlikTech had been on a similar trajectory, but already crossed into the leader quadrant in 2012. It also strengthened both execution and vision again this year.)

Another new challenger is LogiXML. Thanks to ease of use, enhancements from customer feedback and a focus on the OEM market its ability to execute increased substantially. From the Gartner report summary on LogiXML:

Ease of use goes hand-in-hand with cost as a key strength for LogiXML, which is reflected by its customers rating it above average in the survey. The company includes interfaces for business users and IT developers to create reports and dashboards. However, its IT-oriented, rapid development environment seems to be most compelling for its customers. The environment features extensive prebuilt elements for creating content with minimal coding, while its components and engine are highly embeddable, making LogiXML a strong choice for OEMs.

A few other niche players almost broke into new quadrants, including Alteryx (which had the biggest overall increase and almost broke into the visionary quadrant), as well as Actuate and Panorama Software.

The latter two stayed the same with regards to execution (as did SAP) – while all three of them moved strongly to the right to improve on the vision score (forming the Top 3 of vision improvement).

Information Builders and Oracle stayed where they were, changing neither their execution nor vision scores.

Microsoft and Pentaho stayed about the same on vision, but increased substantially in their execution scores.  This propelled Microsoft to the top of the heap on the execution score, while it moved Pentaho from near the bottom of the heap to at least a more viable niche player position. Microsoft’s integration of BI capabilities in Excel, SQL Server and SharePoint as well as leveraging of cloud services and attractive price points make it a strong contender especially in the SMB space. Improvements of its ubiquitous Excel platform give it a unique position in the BI market. From the Gartner report:

Nowhere will Microsoft’s packaging strategy likely have a greater impact on the BI market than as a result of its recent and planned enhancements to Excel. Finally, with Office 2013, Excel is no longer the former 1997, 64K row-limited, tab-limited spreadsheet. It finally begins to deliver on Microsoft’s long-awaited strategic road map and vision to make Excel not only the most widely deployed BI tool, but also the most functional business-user-oriented BI capability for reporting, dashboards and visual-based data discovery analysis. Over the next year, Microsoft plans to introduce a number of high-value and competitive enhancements to Excel, including geospatial and 3D analysis, and self-service ETL with search across internal and external data sources.

The report then goes on to praise Microsoft for further enhancements (queries across relational and Hadoop data sources) that contribute to its strong product vision score and “positive movement in overall vision position”. This does not seem consistent with the presented Magic Quadrant, where Microsoft only moved to the top (execution), not to the right (vision). Perhaps another reason for Gartner to publish the underlying coordinate data and finally adopt this line of visualization with trajectories.

Deteriorate2013

Dashboard with filters revealing two platforms deteriorating in both vision and execution

Only two vendors saw their scores deteriorate in both dimensions: MicroStrategy gave up some ranks, but remains in the leader quadrant. The report cites a steep sales decline in 3Q12 and the increased importance of predictive and prescriptive analytics in this years evaluation among the reasons:

MicroStrategy has the lowest usage of predictive analytics of all vendors in the Magic Quadrant. A reason for this behavior might be the user interface that is overfocused on report design conventions and lacks proper data mining workbench capabilities, such as analysis flow design, thus failing to appeal to power users. To address this matter, MicroStrategy should deliver a new high-end user interface for advanced users, or consumerize the analytic capabilities for mainstream usage by embedding them in Visual Insight.

The other vendor moving to the bottom-left is arcplan, which is now at the bottom of the heap in the niche players quadrant.

Who moved to the top-left?

With the dashboard at hand, you can also go back and do similar queries not just for the current year 2013, but any of the five previous years. For example, who has moved to the top-left – improved execution at the expense of reduced vision – over the years?

In 2013 those were Targit, Jaspersoft, Board International. All three of them had a sharp drop in Execution in the previous year 2012. A plausible scenario of what happened is that these companies lost their focus on execution, dropped the scores and in an attempt to turn-around focused on executing well with a smaller set of features (hence lower vision).

In 2012 the only vendor to display a move to the top-left was QlikTech. They had some sales issues the prior year as well, although their trajectory in 2011 was only modestly lower in execution, mostly towards higher vision.

In 2011 Actuate and Information Builders moved to the top-left. Both had trajectories to the bottom-left the prior year (2010), with especially Actuate losing a lot of ground. With the Year slider on the top-left of the dashboard one can then play out the trajectory while the company filters remain, thus showing only the filtered subset and their history. Actuate completed a remarkable turn-around since then and is now positioned back roughly where it was back in 2010.

Dashboard with analysis of top-left moving companies.

Dashboard with analysis of top-left moving companies.

 

(Click on the image above or here to go to the interactive Public Tableau website.)

In 2010 there were five vendor moving to the top-left: Oracle, SAS, QlikTech, Tibco (Spotfire) and Panorama Software. Although in that case none of them did show a decrease in execution the previous year. That focus on execution may simply have been the result of the economic downturn in 2009.

Such exploratory analysis is hard to conceive without proper interactive data visualization. Given the focus of all the vendors it covers in this report, it seems somewhat anachronistic that Gartner in its report does not leverage the capabilities of such interactive visualization itself. In the previous post on Global Risks we have seen how much value that can add to such thorough analysis. (Much of this dashboard should be applicable for risk analysis as well, just that the two-dimensional space changes from platform vision vs. execution to risk likelihood vs. impact!) If Gartner does not want to drop on its own execution and vision scores, they better adopt such visualization. It’s time.

 
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Posted by on February 12, 2013 in Industrial

 

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Olympic Medal Charts

Olympic Medal Charts

The 2012 London Olympic Games ended this weekend with a colorful closing ceremony. Media coverage was unprecedented, with other forms of competition around who had the most social media presence or which website had the best online coverage of the games.

In this post I’m looking at the medal counts over the history of the Olympic Games (summer games only, 27 events over the last 116 years, no games in 1916, 1940, and 1944). Nearly 11.000 athletes from 205 countries competed for more than 900 medals in 302 events. The New York Times has an interactive chart of the medal counts on their London 2012 Results page:

Bubble size represents the number of medals won by the country, bubble position is roughly based on a world map and bubble color indicates the continent. Moving the slider to a different year changes the bubbles, which gives a dynamic grow or shrink effect.

Below this chart is a table listing all gold, silver, bronze winners for each sport in that year, grouped by type of sport such as Gymnastics, Rowing or Swimming. Selecting a bubble will filter this to entries where the respective country won a medal. This shows the domination of some sports by certain countries, such as Diving (8 events, China won 6 gold and 10 total medals) or Cycling – Track (10 events, Great Britain won 7 gold and 9 total medals). In two sports, domination by one country was 100%: Badminton (5 events, China won 5 gold and 8 total medals), Table Tennis (4 events, China won 4 gold and 6 total medals).

There is also a summary table ranking the countries by total medals. For 2012, the United States clearly won that competition, winning more gold medals (46) than all but 3 other countries (China, Russia, Britain) won total medals.

Top 10 countries for medal count in 2012

Of course countries vary greatly by population size. It is remarkable that a relatively small nations such as Jamaica (~2.7 million) won 12 medals (4, 4, 4), while India (~1.25 billion) won only 6 medals (0, 2, 4). In that sense, Jamaica is about 1000x more medal-decorated per population size than India! In another New York Times graphic there is an option to compare medal count adjusted for population size, i.e. with the medal count normalized to a standard population size of say 100 million.

Directed graph comparing medal performance adjusted for country size

Selecting any node in this graph will highlight countries with better, worse or comparable relative medal performance. (There are different ways to rank based on how different medals are weighted.)

The Guardian Data Blog has taken this a step further and written a piece called “alternative medals table“. This post not only discusses multiple factors like population, GDP, or number of athletes and how to deal with them statistically; it also provides all the data and many charts in a Google Docs spreadsheet. One article combines GDP adjustment with cartographical mapping across Europe:

Medals GDP Adjusted and mapped for Europe

If you want to do your own analysis, you can get the data in shared spreadsheets. To do a somewhat more historic analysis, I used a different source, namely Wolfram’s curated data source accessible from within Mathematica. Of course, once you have all that data, you can examine it in many different directions. Did you know that 14853 Olympic medals were awarded so far in 27 summer Olympiads? The average was 550 medals, growing about 29 medals per event with nearly 1000 awarded in 2008 and 2012.

A lot of attention was paid to who would win the most medals in London. China seemed in contention for the top spot, but in the end the United States won the most medals, as it did in the last 5 Olympiads. Only 7 countries won the most medals at any Olympiad. Greece (1896), France (1900), the United Kingdom (1908), Sweden (1912), and Germany (1936) did so just once. The Soviet Union (which no longer exists) did it 8 times. And the United States did it 14 times. China, which is only participating since 1984, has yet to win the most medals of any Olympiad.

Aside from the top rank, I was curious about the distribution of medals over all countries. Both nations and events have increased, as is shown in the following paired bar chart:

Number of participating nations and total medals per Summer Games

The number of nations grew steadily with only two exceptions during the thirties and the seventies; presumably due to economic hardship many nations didn’t want to afford participation. 1980 also saw the Boycott of the Moscow Games by the United States and several other delegations over geopolitical disagreements. At just over 200 the number of nations seems to have stabilized.

The number of medals depends primarily on the number of events at each Olympiad. This year there were 302 events in 26 types of Sports. Total medal count isn’t necessarily exactly triple that since in some events there could be more than 1 Bronze (such as in Judo, Taekwondo, and Wrestling). Case in point, in 2012 there were 968 medals awarded, 62 more than 3 * 302 events.

What is the distribution of those medals over the participating nations? One measure would be the percentage of nations winning at least some medals. Another measure showing the degree of inequality in a distribution is the Gini index. Here I plotted the percentage of nations medaling and the Gini index of the medal distribution over all participating nations for every Olympiad:

Percentage and Gini-Index of medal distribution by nations

Up until 1932 3 out of 4 nations won at least some medals. Then the percentage dropped down to levels around 40% and lower since the sixties. That means 6 of 10 nations go home without any medals. During the same time period the inequality grew from Gini of about .65 to near .90 One exception were the Third Games in 1904 in St. Louis. With only 13 nations competing the United States dominated so many sports to yield an extreme Gini of .92 All of the last five Games resulted in a Gini of about .86, so this still very large amount of medal winning inequality seems to have stabilized.

It would be interesting to extend this to the level of participating athletes. Of course we know which athlete ranks at the top as the most decorated Olympic athlete of all time: Michael Phelps with 22 medals.

 
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Posted by on August 15, 2012 in Recreational

 

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London Tube Map and Graph Visualizations

London Tube Map and Graph Visualizations

The previous post on Tube Maps has quickly risen in the view stats into the Top 3 posts. Perhaps it’s due to many people searching Google for images of the original London tube map in the context of the upcoming Olympic Games.

I recently reviewed some of the classes in the free Wolfram’s Data Science course. If you are interested in Data Science, this is excellent material. And if you are using Mathematica, you can download the underlying code and play with the materials.

It just so happens that in the notebook for the Graphs and Networks: Concepts and Applications class there is a graph object for the London subway.

Mathematica Graph object for the London subway

As previously demonstrated in our post on world country neighborhood relationships, Mathematica’s graph objects are fully integrated into the language and there are powerful visualization and analysis functions.

For example, this graph has 353 vertices (stations) and 409 edges (subway connections). This one line of code  highlights all stations no more than 5 stations away from the Waterloo station:

HighlightGraph[london, 
  NeighborhoodGraph[london, "Waterloo", 5]]

Neighborhood Graph 5 around Waterloo

Since HighlightGraph and NeighborhoodGraph are built-in functions, this can be done in one line of code.

Export["london.gif",
  Table[HighlightGraph[london, 
    NeighborhoodGraph[london, "King's Cross St. Pancras", k]],
   {k, 0, 20, 1}]]

creates this animated GIF file:

Paths spreading out from the center

Shortest paths can easily be determined and visualized:

HighlightGraph[london, 
  FindShortestPath[london, "Amersham", "Woolwich Arsenal"]]

A shortest path example

There are many other graph functions such as:

GraphDiameter[london]   39
GraphRadius[london]     20
GraphCenter[london]     "King's Cross St. Pancras"
GraphPeriphery[london]  {"Watford Junction", "Woodford"}

In other words, the King’s Cross St. Pancras station is at the center, with radius up to 20 out into the periphery, and 39 the shortest path between Watford Junction and Woodford, the longest shortest path in the network.

Let’s look at distances within the graph. The built-in function GraphDistanceMatrix calculates all pairwise distances between any two stations:

mat = GraphDistanceMatrix[london]; MatrixPlot[mat]

Graph Distance Matrix Plot

For the 353*353 = 124,609 pairs of stations, let’s plot a histogram of the pairwise distances:

Histogram[Flatten[mat]]

Graph Distance Histogram

The average distance between two stations in the London subway system is about 14.

So far, very little coding has been required as we have used built-in functions. Of course, the set of functions can be easily extended. One interesting aspect is the notion of centrality or distance of a node from the center of the graph. This is expressed in the built-in function ClosenessCentrality

cc = ClosenessCentrality[london];
HighlightCentrality[g_, cc_] := 
   HighlightGraph[g, 
    Table[Style[VertexList[g][[i]], 
      ColorData["TemperatureMap"][cc[[i]]/Max[cc]]], 
        {i, VertexCount[g]}]];
HighlightCentrality[london, cc]

Color coded Centrality Map

Another interesting notion is that of BetweennessCentrality, which is a measure indicating how often a particular node lies on the shortest paths between all node-pairs. The following nifty little snippet of code identifies the 10 most traversed stations – along the shortest paths – of the London underground:

HighlightGraph[london,
 First /@ SortBy[
 Thread[VertexList[london] -> BetweennessCentrality[london]],
 Last][[-10 ;;]]]

10 most traversed stations

I have often felt that progress in computer science and in languages comes from raising the level of abstraction. It’s amazing how much analysis and visualization one can do in Mathematica with very little coding due to the large number of powerful, built-in functions. The reference documentation of these functions often has many useful examples (and is also available for free on the web).
When I graduated from college 20 years ago we didn’t have such powerful language platforms. Implementing a good algorithm for finding shortest paths is a good exercise for a college-level computer science course. And even when such pre-built functions exist, it may still be instructive to figure out how to implement such algorithms.
As manager I have always encouraged my software engineers to spend a certain fraction of their time searching for built-in functions or otherwise pre-existing code to speed up project implementation. Bill Gates has been quoted to have said:

“There is only one trick in software: Use a piece of code that has already been written.”

With software engineers, it is well known that productivity often varies not just by small factors, but by orders of magnitude. A handful of talented and motivated engineers with the right tools can outperform staffs of hundreds at large companies. I believe the increasing levels of abstraction and computational power of platforms such as Mathematica further exacerbates this trend and the resulting inequality in productivity.

 
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Posted by on July 11, 2012 in Education, Recreational

 

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Interactive Tournament Map

Interactive Tournament Map

I hadn’t followed the UEFA 2012 European football championship (called soccer in the US) and wanted to catch up on where things stand. Enter the interactive tournament map on the official UEFA website:

Row selection highlights games at that stadium

When you first enter the map it animates the timeline from left to right by drawing the colored lines for each team. The tabular layout shows time in daily columns from left to right and teams in rows by 4 tournament groups. Today’s day column is always highlighted. Here are some of the interactive elements:

  • Mouse over any of the colored lines highlights the corresponding team’s games along it’s timeline.
  • Clicking on a particular day column header highlights the games played on that date.
  • Clicking on the stadium symbol at the right end highlights the games played at that stadium.
  • Clicking on any circle brings up a dialog with details for that game.
  • Clicking on a row header on the left brings up a dialog with details for that team.
  • Selecting the tournament stage at the bottom (quarter-, semi-, final) moves to the date interval.

Detail for team Spain

Spain is the reigning football world champion, so they are clearly one of the favorites of this tournament and will actually play their semi-final against Portugal later this evening.

The final will be played in the Olympic Stadium in Kyiv, capital of participating host country Ukraine.

Detail with game schedule for stadium

From these details you can click on the games and get to yet more detail (videos, comments, etc.) for that particular game.

When I first looked at the map, the amount of information displayed had me a bit confused. The color scheme is often difficult to separate, for example the three orange-red tones in Group B. The black background feels attractive, although I could do without the pattern overlay, which doesn’t add information and only distracts. Lastly, I could do without the colorful advertisements around the map. On first glance I thought the stadium symbols on the right were also just colored ads.

The interactive nature made the map grow on me. It’s intuitive and the tabular layout easy to navigate. You may not have a screen wide enough to see the map in its entirety, but I suppose you wouldn’t want to see time down the vertical axis, would you?

Postscript 7/1/12: Sure enough, Spain beat Italy 4:0 in today’s final and went on to become the European football champion 2012.

 
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Posted by on June 27, 2012 in Recreational

 

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Venn Diagrams

Venn Diagrams

The private library Blog had a post with some word play relating to sound, spelling and meaning of words in the English language. From their post on Homographic Homophones:

English is one of the most difficult languages in the world for a non-native speaker to learn.  One of the reasons why this is so is that English has a large number of words that are pronounced the same as other words (i.e., they are homophones) even though they have quite different meanings.  Homophones such as parepair and pear, for example, have the same pronunciation but are spelled differently and have different meanings (heterographic homophones).  Other homophones — tender (locomotive),tender (feeling) and tender (resignation), for instance — are spelled the same and pronounced the same (homographic homophones) but have different meanings (i.e., they are homonyms).

Got all that?  Wikipedia has a nice Venn diagram that may help you sort it out:

Venn Diagram displaying meaning, spelling, and pronunciation of words (Source: Wikipedia)

Of course, you could also list the above combinations in a table. If you’re interested, Carol Moore has done just that on her Buzzy Bee riddle page.

A beautifully symmetric 5 set Venn diagram drawn from ellipses has been proposed by Branko Grünbaum and drawn by Wikipedia contributor Cmglee:

Symmetrical_5-set_Venn_diagram (Source: Wikipedia)

Such set-based diagrams invite a more mathematical notation. Cmglee annotates his image with this snippet:

Labels have been simplified for greater readability; for example, A denotes A ∩ Bc ∩ Cc ∩ Dc ∩ Ec (or A ∩ ~B ∩ ~C ∩ ~D ∩ ~E), while BCE denotes Ac ∩ B ∩ C ∩ Dc ∩ E (or ~A ∩ B ∩ C ∩ ~D ∩ E).

If you search the Wolfram Demonstration Project for ‘Venn Diagram’, you get several interactive diagrams.

Venn Diagram Demonstration Projects (Source: Wolfram Demonstration Project)

These diagrams are interactive. For example, they allow you to click on any subset and then have that set highlighted and the corresponding mathematical set notation displayed accordingly. Interesting and fun to learn.

Speaking of fun: Venn diagrams are also effectively used in many different areas, two of which I’d like to leave you with here:

Data Science Venn Diagram (Source: drewconway.com)

And last but not least, Stephen Wildish’s Pancake Venn Diagram:

 
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Posted by on June 10, 2012 in Linguistic, Scientific

 

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