Contributed by: Ramakant Gawande (Lead UX Designer, Clarice Technologies)

Ramakant is a user experience (UX) designer specializing in interaction design (IxD), interested in human–computer interaction (HCI) and cognitive science, passionate about good design and creating products for multiple channels like handheld devices, PC, iTV etc. Currently at Clarice, he has designed consumer and enterprise applications for fortune 500 companies, which deals with big-data analytics and data visualizations.


Recently Google took inspiration from the 19th century chart and made it dynamic. The new Flow Visualization shows the march of users across a site, but it can be sorted to look at specific kinds of users. Web administrators can compare users on different browsers or systems, or from different referrers or countries, to see how their experiences differ on the site.

Yes, Google took inspiration from Charles Joseph Minard‘s 1869 graph of Napoleon’s campaign in Russia. ”It shows time, it shows motion, and it tells a story.”  ”But I can’t drill down. It’s static. I can’t make it dynamic.” To make this kind of visualization useful for Google Analytics, it has to have adjustable filters. “What happened to the people who had horses? What happened to the people who spoke Russian?”

To fully appreciate what information visualization is and what it offers, it’s worthwhile to quickly trace the historical highlights.

The tabular presentation of data has been with us since the 2nd century, when it was first used in Egypt to organize astronomical information and to aid navigation. The representation of quantitative data in the form of two-dimensional graphs, however, didn’t arise until much later, in the 17th century. Rene Descartes, the French philosopher and mathematician famous for the words “Cogito ergo sum” (“I think therefore I am”), invented 2-D graphs using X and Y axes, not originally for presenting data, but for perform- ing a type of mathematics based on a system of coordinates relative to the axes. It wasn’t until the late 18th and early 19th centuries that many of the graphs that we use today were invented or dramatically improved by a Scottish social scientist named William Playfair, including bar charts and pie charts. Over a century passed, however, before the value of these techniques was sufficiently recognized for the first university course in graphing data to be offered, originally in 1913 at Iowa State.

The person who really recognized the power of visualization as a means to explore and make sense of data was the Princeton statistics pro- fessor John Tukey. In 1977, Tukey introduced a whole new approach to analyzing data called exploratory data analysis. Later, in 1983, data visualization guru Edward Tufte, published his groundbreaking book The Visual Display of Quantitative Information, which showed us that there were effective ways of displaying data visually and then there were the ways that most of us were doing it, which did not communicate very well. One year later, in 1984, while watch- ing the Super Bowl, Apple introduced us to the first popular and affordable computer that emphasized graphics as a mode of interaction and display, a graphical user interface (GUI) that was originally developed at Xerox PARC (Palo Alto Research Center). This paved the way finally for the use of data visualizations that were interactive.

What is the Definition saying ?

“Information visualization is the use of computer- support interactive visual representations of abstract data to amplify cognition” definition from ___Readings in Information Visualization: Using Vision to Think

This definition places information visualization smack dab in the middle of business intelli- gence. It’s worthwhile to break it apart to highlight and clarify each of its components:

  • Computer-supported: The visualization is displayed by a computer, usually on a computer screen.
  • Interactive: The visualization can be manipulated directly and simply by the user in a free-flowing manner, including such actions as filtering the data and drilling down into details.
  • Visual representations: The information is displayed in visual form using attributes like the location, length, shape, color, and size of objects to form a picture of the data and thereby reveal patterns, trends, and excep- tions that might not be seen otherwise.
  • Abstract data: Information such as quantities, processes, and relationships, as opposed to visual representations of physical objects, such as geography (that is, a map) or the human body (for example, an MRI image).
  • Amplify cognition: These visualizations and the interactions they enable extend our ability to think by assisting memory and represent- ing the information in ways that our brains can easily comprehend.

In simple words Visualization is the graphical presentation of information, with the goal of providing the viewer with a qualitative understanding of the information contents. Information may be data, processes, relations, or concepts. Graphical presentation may entail manipulation of graphical entities (points, lines, shapes, images, text) and attributes (color, size, position, shape). Understanding may involve detection, measurement, and comparison, and is enhanced via interactive techniques and providing the information from multiple views and with multiple techniques.

Difference between Infographics & Data Visualisation :

You may have heard the terms infographics and data visualization used in different ways, or interchangeably in different contexts, or even casually by the same person in a single sentence. The difference between inforgraphics and data visualization may be loosely determine by the method of generation, quantity of data presented and the degree of aesthetic treatment applied.

Figure: 1 The distinction between infographics and data visualization is based on both FORM and ORIGIN

Infographics is a term useful for referring to any visual representation of the data which is –

  • Manually drawn therefore  a custom treatment of the information.
  • Specific to the data at hand and therefore nontrivial to recreate with different data
  • Aesthetically rich with strong visual content meant to draw the eye and hold interest
  • Relatively data-poor because each piece of information must be manually encoded

Figure: 2 Infographic Sample

By contrast, the terms data visualization and information visualiza- tion  are useful for referring to any visual representation of data which is-

  • Algorithmically drawn it may have custom touches but is largely rendered with the help of computerized methods
  • Easy to regenerate with different data (the same form may be re-purposed to rep- resent different data-sets with similar dimensions or characteristics).
  • Often aesthetically barren (data is not decorated)
  • Relatively data-rich (large volumes of data are welcome and viable, in contrast to infographics)

Figure: 3 Gapminder Data Visualisation

More complex visualizations sometimes generate animations that demonstrate how data change over time. In an application called Gapminder, bubbles represent the countries of the world, with each nation’s population reflected in the size of its bubble. You can set the and axes to compare life expectancy with per capita income, for example, and the tool will show how each nation’s bubble moves on the graph over time. You can see that higher income generally correlates with longer life expectancy, but the visualization also clearly shows that China doesn’t follow this trend—in 1975, the country had one of the lowest per capita incomes but one of the longer life expectancies. The animation also shows the steep drop in life expectancy in many sub-Saharan African countries starting in the early 1990s (corresponding to the AIDS epidemic in that part of the world) and the plummeting of life expectancy in Rwanda at the time of that nation’s genocide.

We will cover basics of Data Visualization elements and best practices in next post. Thanks You!


I look forward to your thoughts and comments…






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