Reading pointers below I borrowed from my Google+ microblogs “Data Visualization” ( , 7000+ followers) and “Data Visualization with Tableau” ( , almost 1000 followers). Sometimes the reading is more important then doing or writing. The reading on the beach (like below) can be even more…


  1. How Scalable Do Analytics Solutions Need to Be?
  2. The Data Visualization Catalogue. and
  3. The Evolution of SQL Server BI,
  4. Abela’s Folly – A Thought Confuser.
  5. TIBCO Spotfire Promotes an Insidious Myth.
  6. User Ideas Turned into Product Features:
  7. Is Data Is, or Is Data Ain’t, a Plural? and
  8. Talk: How to Visualize Data,
  9. Pillars Of Mapping Data To Visualizations,
  10. Radar Chart can be useful(?),
  11. Visualization Publication Data Collection,
  12. Visual Representation of SQL Joins, and


  1. Example of stupidity of the crowd:
  2. Reviving the Statistical Atlas of the United States with New Data,
  3. Exploring the 7 Different Types of Data Stories:
  4. Set Your Own Style with Style Templates:
  1. A Look at Choropleth Maps ,
  2. Mountain Chart for different categories (profiles) of web visits:!/vizhome/MountainChart/MountainChart


  1. To the point: 7 reasons you should use dot graphs,
  2. Rant: A Tableau Faithful’s View On Qlik ,
  3. Too Big Data: Coping with Overplotting,
  4. Too much data to visualize? Data densification in Tableau 9 ,
  5. The Architecture of a Data Visualization, , also see
  6. Filter Views using URL Parameters ,


  1. Building a Visualization of Transit System Data Using GTFS ,
  2. A Look At Box Plots ,
  3. Custom Tableau Server Admin Views ,
  4. Circular and Hive Plot Network Graphing in Tableau ,
  5. Hexbins in Tableau ,
  6. Tableau Public Goes Premium for Everyone; Expands Access to 10 Million Rows of Data ,


2 year ago the IPO instantly created almost $3B of market capitalization for Tableau Software Inc. and since then it almost tripled, making Tableau the most “valuable” Data Visualization company (click on image to enlarge):


Tableau more then doubled the number of its Full-Time employees (almost 2200 now, roughly the same (or more?) as QLIK has) and more then doubled its Revenue (again, roughly the same as QLIK has). Tableau’s YoY growth still in range of 77%-100% per year, which is far, far more then any competition:


Combination of that growth with technological progress and new features of Tableau’s products led to huge growth of its share price – it reached in 1st week of June 2015 $115, while Qlik’s share price is hovering around $37 or even below (click on image to enlarge):


Visitors to this blog kept asking me of what is most impressive (for me) about Tableau and what are my concerns. I will list just 3 of each:

  • most impressive: YoY (Year-over-Year growth ratio); migration to 64-bit (finally) and performance improvements; and  increasing capacity of Tableau Public to 10 million rows and 10 GB storage.
  • concerns: rumors that price of Tableau Server will be increased (I heard doubled; that can slow down the growth and the popularity of Tableau); moving CEO to Europe away from HQ (repeating of mistake of Spotfire and Qliktech, who had/have R&D in Europe – away from american HQ);  and limited capacity of Tableau Online (basically it can be good only for small workgroup).

Not all of its huge success can be contributed to Tableau itself:

QLIK for example did not release Qlikview version 12 for last 4 years (but kept updating the last version, recently with release 11 (!) of Qlikview version 11.2). Another help Tableau got from TIBCO, who kept Spotfire inside strict corporate cage and went private with little change for Spotfire to be a spin-off. As a result, competition for Tableau during last 2 years was weaker then before its IPO and we are witnessing a massive migration to Tableau from competitive products.

Don’t assume that Tableau is slowing down: I visualized (using Tableau Public of course, see it here:!/vizhome/Data2Months/TableausMarketCap ) the Tableau’s Market capitalization during last 52 business days and it keeps growing at least as fast as last 2 years:

Tableau's Market Cap

Update 6/7/15: finally, just check the number of Job Openings @Tableau – 344 (as of today 6/7/15), @QLIK – 116 (3 times less then Tableau!), and only 1 (ONE!) opening for Spotfire… If you still think that Microstrategy can compete with Tableau, then please keep this in mind: as of today Microstrategy’s total number of Job Openings is … 50.



My best wishes in 2015 to visitors of this Data Visualization blog!

2014 was very unusual for Data Visualization Community. Most important event was the huge change in market competition where Tableau was a clear winner, QLIK lost it leadership position and Spotfire is slowly declining as TIBCO went private. Pleasant surprise was Microsoft, who is finally trying to package Power BI separately from Office. In addition other competitors like Microstrategy, Panorama and Datawatch were unable to gain bigger share in Data Visualization market.

2014 again was the year of Tableau: market capitalization exceeded $6B, YoY growth was highest again, sales approaching $0.5B/year, number of employees almost the same as @QLIK, LinkedIn index exceeded 90000, number of Job Openings increased again and as of today it is 337! I personally stopped comparing Data Visualization products for last few months, since Tableau is a clear winner overall and it will be difficult for others to catch-up unless Tableau will start making mistakes like QLIK and Spotfire/TIBCO did during last few years.

2014 was very confusing for many members of QLIK community, me included. Qlik.Next project resulted in new Qlik Sense Product (I don’t see too much success for it) and Qlikview 12 is still not released, while prices for both QLIK products are not public anymore. Market Capitalization of QLIK is below $3B despite still solids sales (Over $0.5B/year) and YoY growth is way below of Tableau’s YoY. Qlikview’s LinkedIn index now around 60000 (way below Tableau’s) and Qlik Sense’s LinkedIn index is only 286…  QLIK has only 124 Job opening as of today, almost 3 times less then Tableau!

Curiously, BI Guru Mr. Donald Farmer, who joined QLIK 4 years ago (a few months before the release of Qlikview 11) and who was the largest propagandist of Qlik.Next/Qlik Sense, was moved from VP of Product Management position to new “VP of Innovation” @QLIK just before the release of Qlik Sense and we hear much less from Donald now. Sadly, during these 4 years Qlikview 12 was never released, and QLIK never released anything similar to free Tableau Reader, free Tableau Public and Tableau Online (I am still hoping for Qlikview in Cloud) and all Qlikview prices were unpublished…

As a member of Spotfire community, I was sad to see the failure of Spotfire (and its parent TIBCO) to survive as public company: on December 5, Vista Equity Partners completed the acquisition of TIBX for $4.3 billion. I estimate Spotfire sales around $200M/year (assuming it is 20% of TIBCO sales). LinkedIn index of Spotfire (is way below Tableau’s and Qlikview’s) is around 12000 and number of Job Openings is too small. I hope Vista Equity Partners will spinoff the Spotfire in IPO as soon as possible and move all Spotfire’s Development, Support, Marketing and Sales into one American location, preferably somewhere in Massachusetts (e.g. back to Somerville).

Here is a farewell Line Chart (bottom of Image) to TIBX symbol, which was stopped trading 3 weeks ago (compared to DATA and QLIK Time Series (upper and middle Line Charts) for entire 2014):


While on Cape Cod this summer and when away from beach, I enjoyed some work-unrelated fun with Tableau. My beach reading included this article: by Andrew Ball and I decided to create my own Radar. When I show it to coworkers later, they suggested to me to publish it (at least the fun with Polygons, Path and Radars) on my blog. I may reuse this Radar chart for our internal Web Analytics. CloudsOverAtlantic

Natural Order of Points and Segments in Line.

Many visualization tools will draw the line chart, its datapoints and connecting line segments between datapoints in natural progressing order – repainting them from left to right (horizontal ordering by Axis X) or from bottom to upside
(vertical ordering by Axis Y) or vice versa.

Path as the method to break the Natural Order.

Some demanding visualizations and users wish to break the natural repainting and drawing order and Tableau allows to do that by using the Path as the method to order the datapoints and line segments in Lines and Polygons. A Collection of increasing Ordering Numbers (Pathpoints) for each Datapoint in Line defined a Path for drawing and connecting datapoints and segments of that Line (or Polygon). Each Pathpoint can be predefined or calculated, depends on mplementation and business logic.
Changing the Natural Order can create “artificial” and unusual situations, when two or more datapoints occupying the same pixels on drawing surface but have very different Pathpoints (example can be a Polygon, when Line ends in the same point it starts) or when two or more Line Segments intersecting in the same Pixel on screen (example can be the Center of the letter X ).


Radar Chart has 2 parts: Radar Grid (background) and Radar Polygons (showing repetitive Data Patterns, if linear timeline can be collapsed into circular “timeline”). Radar Grid has Radials (with common Center) and Concentric Rings.
Polygons optionally can be filled with (transparent) color. For future Discussion let’s use the RMax as the maximal possible distance between the Center of Radar Grid (in case of Radar Grid) or the Center of Radar Polygon (in case of Radar
Polygon) and the most remote Datapoint shown in Radar Grid or Polygon respectively. We will use the “normalized” statistics of Visits to typical Website to visualize the hourly and daily (by day of the week) patterns of Web Visitations. By
normalization we mean the removal of insignificant deviations from “normal” hourly and daily amounts of Web Visits. For complete obfuscation we will assume for Demo purposes that RMax = 144.

Radar Radial Grid.

Radial Grid contains a few Radiuses (equidistant from each other) and we will draw each Radius as 3-point line where Starting and Ending points of each line are identical to each other and collocated with the Center of Radar. For Demo Web Visitation Radar we will use Radial Grid with 8 Radiuses, corresponding to the following hours of the complete 24-hours day: 0, 3, 6, 9, 12, 15, 18, 21:
For example see the Radius, corresponding to HOUR = 3 (below in light brown, other Radiuses greyed out on that image):
And for that Radius we are using (redundantly) the following 3 datapoints:

Concentric Rings for Radar Grid.

For Demo Radar we will use 4 Concentric Rings, corresponding to 25%, 50%, 75% and 100% levels of maximum visitation per hour:
Each ring is a line with 25 datapoints, where Starting and Ending Points collocated/equal. For example, dataset for external Ring (red line above) looks like this:
When Radials and Concentric Rings collocated and overlaid they represent the Radar Grid, ready to be a background for Radar Chart:

Radar Polygons.

For Demo purposes we use only 2 Polygons – one (largest) representing average Hourly Visits during Weekday and 2nd Polygon representing average Hourly Visits during Weekend day. For Websites which I observed the minimum number of visits happened around 1 AM, so you will see both Polygons are slightly rotated clockwise and slightly shifted up from the Center of Radar Grid to reflect the fact that the minimum number of visitors (even around 1 AM) is slightly more then 0. Each Radar Polygon (in our Demo) has 25 Data Points with Starting and Ending Points collocated at 1AM. Here is a Weekday Polygon, overlaid with Radar Grid:
Here are the data for Weekday Polygon: 


Here is a Polygon for Weekend day, overlaid with Radar Grid:


Radar Chart.

When Radar Grid and Radar Polygons overlaid (Polygons transparent but on top of
Grid) we will get the Radar Chart. Please note that Centers of Radar Grid and Radar
Polygons can have different locations:



I published Tableau workbook with this Demo Radar Chart and Radar Data here:!/vizhome/radar/Radar

Visitors to this blog keep asking me to estimate Tableau Software prices (including for Tableau Online), even Tableau published all non-server prices on its website here: However this does not include discounts, especially for enterprise volume of buying no pricing for servers of any kind (at least 2 kinds of server licenses exist) and no pricing for consulting and training.

Thanks to website of Tableau Partner “Triad Technology Partners” we have a good estimate of all Tableau prices (they are always subject of negotiations) in form of so called GSA Schedule (General Services Administration, Federal Acquisition Service, Special Items: No. 132-33 Perpetual Software Licenses, No. 132-34 Maintenance of Software as a Service, No. 132-50 Training Courses) for Tableau Software Products and Services, see it here: here (for example it includes prices for IBM Cognos and others): and specific Tableau Prices here:

I grouped Tableau’s Prices (please keep in mind that TRIAD published GSA schedule in April 2013, so it is 1 year old prices, but they are good enough for estimating purposes)  in 5 groups below: Desktop, Server with licensing for Named Users (makes sense if you have less then hundred “registered” users), Core Licenses for Tableau Server (recommended when you have more then 150 “registered” users), Consulting and Training Prices:

Google sheet for spreadsheet above is here:
and image of it – for those who has misbehaved browsers is below:

Again, please keep in mind that above just an estimate for prices (except for Tableau Online), based on 2013 GSA Schedule, and a good negotiator can always get a good discount (I got it each time I tried). You may also wish to review more general article from Boris Evelson here:

Note about choice between Core License and Server License with Named Users: I know organizations who choose to keep Named Users Licensing instead of switching to Core License even with more then 300 registered users, because it allows them to use much more capable hardware (with much more CPU Cores).

Observing and comparing multiple (similar) multidimensional objects over time and visually discovering multiple interconnected trends is the ultimate Data Visualization task, regardless of specific research area – it can be chemistry, biology, economy, sociology, publicly traded companies or even so called “Data Science”.

For purposes of this article I like the dataset, published by World Bank: 1000+ Measures (they called it World Development Indicators) of 250+ countries for over 50+ years – theoretically more then 10 millions of DataPoints:

Of course some DataPoints are missing so I restricted myself to 20 countries, 20 years and 25 measures (more reasonable Dataset with about 10000 DataPoints), so I got 500 Time Series for 20 Objects (Countries) and tried to imitate of how Analysts and Scientists will use Visualizations to “discover” Trends and other Data Patterns in such situation and extrapolate, if possible, this approach to more massive Datasets in practical projects. My visualization of this Dataset can be found here:;:showVizHome=no

In addition to Trends Line Chart (please choose Indicator in Filter at bottom of the Chart, I added (in my Tableau Visualization above) the Motion Chart for any chosen Indicator(s) and the Motion Map Chart for GDP Indicator. Similar Visualization for this Dataset done by Google here: .

As you can see below with samples of just 6 indicators (out of 1000+ published by World Bank), behavior of monitored objects (countries) are vastly different.

GDP trends: clear Leader is USA, with China is the fastest growing among economic leaders and Japan almost stagnant for last 20 years (please note that I use “GDP Colors of each country” for all other 1000+ indicators and Line Charts):


Life Expectancy: Switzerland and Japan provide longest life to its citizens while India and Russian citizens are expected to live less then 70 years. Australia probably improving life expectancy faster than other 20 countries in this subset.


Health Expenditures Per Capita: Group of 4: Switzerland, Norway (fastest growing?), Luxemburg and USA health expenses about $9000 per person per year while India, Indonesia and China spent less then $500:


Consumer Price Index: Prices in Russia, India and Turkey growing faster then elsewhere, while prices in Japan and Switzerland almost unchanged in last 20 years:


Mobile Phones Per 100 Persons: Russia has 182 mobile phones per 100 people(fastest growing in last 10 years) while India has less then 70 cellular phones per 100 people.


Military Expenses as Percentage of Budget (a lot of missing data when it comes to military expenses!):  USA, India and Russia spending more then others – guess why is that:



You can find many examples of Visual Monitoring of multiple objects overtime. One of samples is where over 7000 objects (publicly traded companies) monitored while observing hundreds of indicators (like share prices, Market Capitalization, EBITDA, Income, Debt, Assets etc.). Example (I did for previous blog post):

Data Visualization Readings, Q1 2014, selected from Google+ extensions of this blog: and


Data Visualization Index (using DATA+QLIK+TIBX+MSTR; click on image above to enlarge):
Since 11/1/13 until 3/15/14: DATA stock grew 50%. QLIK 11%, MSTR – 6%, TIBX – lost 1%.
Current Market Capitalization: Tableau – $5.5B, QLIK – $2.6B, TIBCO – 3.5B, Microstrategy – $1.4B
Number of Job Openings Today: Tableau – 231, QLIK – 135, Spotfire (estimate) – 30, Microstrategy – 214
However during last 2 weeks of March of 2014 DATA shares lost 24%, QLIK lost 14%, TIBX and MSTR both lost about 10%

Why use R? Five reasons.

Studying Tableau Performance Characteristics on AWS EC2

Head-to-head comparison of Datawatch and Tableau

Diving into TIBCO Spotfire Professional 6.0

TIBCO beats Q1 2014 estimates but Spotfire falters

Qlik Doesn’t Fear Tableau, Oracle In Data Analytics

Best of the visualisation web… February 2014

Datawatch: ‘Twenty Feet From Stardom’

Tableau plans to raise $345M — more than its IPO — with new stock offering

TIBCO Spotfire Expands Connectivity to Key Big Data Sources

Tableau and Splunk Announce Strategic Technology Alliance

The End of The Data Scientist!?


Data Science Is Dead

Periodic Table of Elements in TIBCO Spotfire

Best of the visualisation web… January 2014

Workbook Tools for Tableau

Tapestry Data Storytelling Conference ReadingLogo

URL Parameters in Tableau

Magic Quadrant 2014 for Business Intelligence and Analytics Platforms

What’s Next in Big Data: Visualization That Works the Way the Eyes and Mind Work

What animated movies can teach you about data analysis

Tableau for Mac is coming, finally

Authenticating an External Tableau Server using SAML & AD FS

Visualize this: Tableau nearly doubled its revenue in 2013

Qlik Announces Fourth Quarter and Full Year 2013 Financial Results


Tableau Mapping – Earthquakes, 300,000,000 marks using Tableau 8.1 64-bit

Data Science: What’s in a Name?

Gapminder World Offline

Advanced Map Visualisation in Tableau using Alteryx

Motion Map Chart

One of Bill Gates’s favorite graphs redesigned

Authentication and Authorization in Qlikview Server

SlopeGraph for QlikView (D3SlopeGraph QlikView Extension)

Revenue Model Comparison: SaaS v. One-Time-Sales

Scientific Data Has Become So Complex, We Have to Invent New Math to Deal With It

Posting data to the web services from QlikView

It’s your round at the bar

Lexical Distance Among the Languages of Europe


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