“Quadrant” for Data Visualization Platforms

For many years, Gartner keeps annoying me every January by publishing so called “Magic Quadrant for Business Intelligence Platforms” (MQ4BI for short) and most vendors (mentioned in it; this is funny, even Donald Farmer quotes MQ4BI) almost immediately re-published it either on so-called reprint (e.g. here – for a few months) area of Gartner website or on own website; some of them also making this “report” available to web visitors in exchange for contact info – for free. To channel my feeling toward Gartner  to a  something constructive, I decided to produce my own “Quadrant” for Data Visualization Platforms (DV “Quadrant” or Q4DV for short) – it is below and is a work in-progress and will be modified and republished overtime:

3 DV Leaders (green dots in upper right corner of Q4DV above) compared with each other and with Microsoft BI stack on this blog, as well as voted in DV Poll on LinkedIn. MQ4BI report actually contains a lot of useful info and it deserved to be used as a one of possible data sources for my new post, which has more specific target – Data Visualization Platforms. As I said above, I will call it Quadrant too: Q4DV. But before I will do that, I have to comment on Gartner’s annual MQ4BI.

MQ4BI customer survey included vendor-provided references, as well as survey responses from BI users in Gartner’s BI summit and inquiry lists. There were 1,225 survey responses (funny enough, almost the same number of responces as on my DV Poll on LinkedIn), with 247 (20%) from non-vendor-supplied reference lists. Magic Quadrant Customer Survey’s results the Gartner promised to publish in 1Q11. The Gartner has a somewhat reasonable “Inclusion and Exclusion Criteria” (for Data Visualization Q4DV I excluded some vendors from Gartner List and included a few too), almost tolerable but a fuzzy BI Market Definition (based on 13 loosely pre-defined capabilities organized into 3 categories of functionality: integration, information delivery and analysis).

I also partially agree with the definition and the usage of “Ability to Execute” as one  (Y axis) of 2 dimensions for bubble Chart above (called the same way as entire report “Magic Quadrant for Business Intelligence Platforms”). However I disagree with Gartner’s order of vendors in their ability to execute and for DV purposes I had to completely change order of DV Vendors on X axis (“Completeness of Vision”).

For Q4DV purposes I am reusing Gartner’s MQ as a template, I also excluded almost all vendors, classified by Gartner as niche players with lower ability to execute (bottom-left quarter of MQ4BI), except Panorama Software (Gartner put Panorama to a last place, which is unfair) and will add the following vendors: Panopticon, Visokio, Pagos and may be some others after further testing.

I am going to update this DV “Quadrant”, using the method suggested by Jon Peltier: http://peltiertech.com/WordPress/excel-chart-with-colored-quadrant-background/ - Thank you Jon! I hope I will have time before end of 2011 for it…

Permalink: http://apandre.wordpress.com/2011/02/13/q4dv/

"Quadrant" for Data Visualization Platforms

For many years, Gartner keeps annoying me every January by publishing so called “Magic Quadrant for Business Intelligence Platforms” (MQ4BI for short) and most vendors (mentioned in it; this is funny, even Donald Farmer quotes MQ4BI) almost immediately re-published it either on so-called reprint (e.g. here – for a few months) area of Gartner website or on own website; some of them also making this “report” available to web visitors in exchange for contact info – for free. To channel my feeling toward Gartner  to a  something constructive, I decided to produce my own “Quadrant” for Data Visualization Platforms (DV “Quadrant” or Q4DV for short) – it is below and is a work in-progress and will be modified and republished overtime:

3 DV Leaders (green dots in upper right corner of Q4DV above) compared with each other and with Microsoft BI stack on this blog, as well as voted in DV Poll on LinkedIn. MQ4BI report actually contains a lot of useful info and it deserved to be used as a one of possible data sources for my new post, which has more specific target – Data Visualization Platforms. As I said above, I will call it Quadrant too: Q4DV. But before I will do that, I have to comment on Gartner’s annual MQ4BI.

MQ4BI customer survey included vendor-provided references, as well as survey responses from BI users in Gartner’s BI summit and inquiry lists. There were 1,225 survey responses (funny enough, almost the same number of responces as on my DV Poll on LinkedIn), with 247 (20%) from non-vendor-supplied reference lists. Magic Quadrant Customer Survey’s results the Gartner promised to publish in 1Q11. The Gartner has a somewhat reasonable “Inclusion and Exclusion Criteria” (for Data Visualization Q4DV I excluded some vendors from Gartner List and included a few too), almost tolerable but a fuzzy BI Market Definition (based on 13 loosely pre-defined capabilities organized into 3 categories of functionality: integration, information delivery and analysis).

I also partially agree with the definition and the usage of “Ability to Execute” as one  (Y axis) of 2 dimensions for bubble Chart above (called the same way as entire report “Magic Quadrant for Business Intelligence Platforms”). However I disagree with Gartner’s order of vendors in their ability to execute and for DV purposes I had to completely change order of DV Vendors on X axis (“Completeness of Vision”).

For Q4DV purposes I am reusing Gartner’s MQ as a template, I also excluded almost all vendors, classified by Gartner as niche players with lower ability to execute (bottom-left quarter of MQ4BI), except Panorama Software (Gartner put Panorama to a last place, which is unfair) and will add the following vendors: Panopticon, Visokio, Pagos and may be some others after further testing.

Permalink: http://apandre.wordpress.com/2011/02/13/q4dv/

Google keeps own Data Visualizations options open

Recently I had a few reasons to review Data Visualization technologies in Google portfolio. In short: Google (if it decided to do so) has all components to create a good visualization tool, but the same thing can be said about Microsoft and Microsoft decided to postpone the production of DV tool in favor of other business goals.

I remember a few years ago Google bought a Gapminder (Hans Rosling did some very impressive Demos with it a while ago)

and converted it to a Motion Chart “technology” of its own. Motion Chart (For Motion Chart Demo I did below, please Choose a few countries (e.g. check checkboxes for US and France) and then Click on “Right Arrow” button in the bottom left corner of the Motion Chart below)

(see also here a sample I did myself, using Google’s motion Chart) allows to have 5-6 dimensions crammed into 2-dimensional chart: shape, color and size of bubbles, Axes X and Y as usual (above it will be Life Expectancy and Income per Person) and animated time series (see light blue 1985 in background above – all bubbles will move as “time” goes by). Google uses this and other own visualization technologies in its very useful Public Data Explorer.

Google Fusion Tables is a free service for sharing and visualizing data online. It allows you to upload and share data, merge data from multiple tables into interesting derived tables, and see the most up-to-date data from all sources, it has  Tutorials, User’s Group, Developer’s Guide and sample code, as well as examples. You can check a video here:

The Google Fusion Tables API enables programmatic access to Google Fusion Tables content. It is an extension of Google’s existing structured data capabilities for developers. Developer can populate a table in Google Fusion Tables with data, from a single row to hundreds at a time. The data can come from a variety of sources, such as a local database, .CSV file, data collection form, or mobile device. The Google Fusion Tables API is built on top of a subset of the SQL querying language. By referencing data values in SQL-like query expressions, developer can find the data you need, then download it for use by your application. Your app can do any desired processing on the data, such as computing aggregates or feeding into a visualization gadget. Data can be synchronized when you add or change data in the tables in your offline repository, you can ensure the most up-to-date version is available to the world by synchronizing those changes up to Google Fusion Tables.

Everybody knows about Google Web Analytics for your web traffic, visitors, visits, pageviews, length and depth of visits, presented by very simple charts and dashboard, see sample below:

Less people know that Panorama Software has OEM partnership with Google, enabling Google Spreadsheets with SaaS Data Visualizations and Pivot Tables.

Google has Visualization API (and interactive Charts, including all standard Charts, GeoMap, Intensity Map, Map, DyGraph, Sparkline, WordCloud and other Charts) which enables developers to expose own data, stored on any data-store that is connected to the web, as a Visualization compliant datasource. The Google Visualization API also provides a platform that can be used to create, share and reuse visualizations written by the developer community at large. Google provides samples, Chart/API Gallery (Javascript-based visualizations) and Gadget Gallery.

And last but not least, Google has excellent back-end technologies needed for big Data Visualization applications, like BigTable (BigTable is a compressed, high performance, and proprietary database system built on Google File System (GFS), Chubby Lock Service, and a few other Google programs; it is currently not distributed or used outside of Google, although Google offers access to it as part of their Google App Engine) and MapReduce. Add to this list Google Maps and Google Earth

and ask yourself then: what is stopping Google to produce a Competitor for the Holy Trinity (of Qlikview+Spotfire+Tableau) of DV?

Permalink: http://apandre.wordpress.com/2011/02/08/dvgoogle/