### Where Pictographs Beat Bar Charts: Proportional Data

Pictographs are exceptionally good for some types of data. In this post, I show how useful they are for displaying proportions (e.g. rates, percentages, fractions).

Look at the pictograph example on the right. It shows the case fatality rate using colored stick figure icons. These quantities could be just as appropriately shown using pie or bar charts (see above). However, the pictorial representation makes this statistic intuitive: out of every 100 individuals infected with SARS, you can expect 11 to die.

Pictographs have an intrinsic scale

The icons give the pictograph an intrinsic scale. Compare the pictograph (right) to the barchart (below). Both charts show that SARS is 3 times more deadly than pertussis, but the advantage of using a pictograph can be seen when we compare the other diseases. The pictograph clearly shows that the fatality rate for SARS is an order of magnitude bigger than that for smallpox. By contrast, on the bar chart, all we can see in the absence of any labels is that SARs is much bigger than smallpox.

The finer resolution provided by the icons is especially useful for the smaller values. In the bar chart, the much larger fatality rate of SARS makes the variation between the other diseases hard to see. But in the pictograph, it is clear that the smallpox fatality rate is at least double that of malaria.

Pictographs show quantities visually

A well designed pictograph makes quantities easy to read. In the example on the right, the small scale and the large number of icons can potentially cause problems. I avoid this by arranging the icons into 10 by 10 squares. Even without explicitly counting each icon, quantities can be evaluated by comparing the area of the square which is red.

The example on the right shows data labels in order to provide a greater level of detail. However, the main message of the chart – the enormous difference between the severity of different diseases – is effectively conveyed by the icons alone.

Acknowledgments

Author: Carmen Chan

Carmen is a member of the Data Science team at Displayr. She enjoys looking for better ways to manipulate and visualize data. Carmen studied statistics and bioinformatics at the University of New South Wales.

### Big data and Data Visualization

Like many people of a certain age, my first exposure to the term dashboard was when I developed a one for monitoring for corrective and preventive actions!

I have realised that Dashboard design itself is now the essence of simplicity and cutting edge technology, and stylish with it too, arising passions about what makes a great interface for analysis.
When it comes to software applications and websites, dashboards are around us everywhere too!

The era of Big Data has arrived, but most organizations are still unprepared. Enterprises erroneously believe and act like big data is a passing fad, and nothing has really changed. But big data is not a temporary thing. By acting as if it is, companies are missing out on tremendous opportunities by not focusing on such a great technology.

So what it is?

Like many of us  know, an enterprise application dashboard is a one-stop shop of information. It’s a page made up of portlets or regions, grouping up related information into displays of graphs, charts, and graphics of different kinds. Dashboards visualize a breadth of information that spreads over a large range of activities in a application or functional area.

There are numerous case studies in explaining how visual representations are locating and leveraging valuable insights from a large set of structured or unstructured data, i.e., big data, are asking better questions, and are making better decisions.

Is it solves the purpose?

Yes! Dashboards when designed to aggregate sturctured and unstructured data into meaningful visual displays and representations, using analytical formulas over available data-sets at the backend to do the analysis and derivation work that users used to do with notepads, calculators or spreadsheets to find what out what’s changed or in need of attention.

Dashboards over a large amount of data enable users to prioritize work and to manage exceptions by taking light-weight actions immediately from the page, or to drill down to explore and do more in a transactional or analytics work area, if necessary.

The design of Dashboards on a very large amount of data, on the other hand, is much more open to interpretation. Most of these Bigdata Dashboards are simply a series of graphs, charts, gauges, or other visual indicators that a user has chosen to monitor, some of which may be strategically important, but others of which may not. Even if a strategic link exists, it may not be clear to the person monitoring the Dashboard, since the Objective statements, which explain what achievement is desired, are typically not present on Dashboards.

Why this?

I found interesting that there is an infographics and a data visualization categories. My interpretation is that the entries in the infographics section are static and illustrated, while those in the data visualization are generated and data-driven.

Nowadays, Bigdata can be used to gain a better insight over Data visualization using superior tools and techniques to present or analyze the available data.

On the other hand, it is economical in terms of space and would probably work in almost every case which are two things that dashboards should be good at. So while I wouldn’t have used it myself I can understand why this decision has been made. What makes a dashboard, or any other information-based design successful, is neither the design execution nor the clever information analysis and visualization technique.

These kinds of Dashboards, on the other hand eventually, are meant to be useful and to solve a specific problem. Dashboards for business users represent powerful means of communications nowdays when companies build large amounts of data. Those visually compressed representations of only the most important data are used for trackig.

DataViz on my view!

These data visualization can unintentionally bias the viewer as a result of the analyzed choices in visual method, sometimes visualization failing as a result of not understanding your viewers assumptions (cultural for instance, is RED a good or bad color?).

One interesting thing I always think of creating visualizations that discover something with the human eye that can't be discovered by a program. But there will be a challenge showing enough data to give a sense of context while providing enough detail to enable understanding.

What's then?

Whenever a Visualization is done based on Bigdata, once a data visualization designer is aware of simple principles of presenting data on a screen, they can apply them to any report or graph, data analysis or information dashboard without changing it's context or meaning. Only then will it provide a powerful means to make sense of data. When done properly, data visualization will make us think, compare data, read stories out of our data, will put data in the right context and ultimately help decision-makers to make the right decisions regardless of the available type or amount of data.

Do you have any thoughts on this? I am waiting to hear from you!