Probably the best data visualization software since the abacus. No kidding.

defining visual analytics: first, what it's not

Let's start with what visual analysis is not: A graphical depiction of data. Virtually any software application can produce a chart, gauge or dashboard. Visual analytics offers something much more profound. Visual analytics is the process of analytical reasoning facilitated by interactive visual interfaces.

Visual analytics is a means of exploring and understanding data. It supports and accelerates the analysis process itself. Visual analytics allows you to go in any direction with your thoughts while leveraging your visual perceptual system to guide you down the most useful paths.

1. visual exploration

The first characteristic of a visual analytics application is the most important: The application unifies the steps of querying, exploring and visualization data into a single process. Do the data and the visualization work in tandem? It’s like the early days of the web and Mosaic, when people first starting getting the idea of stream of consciousness exploration via hyperlinks. It was a game changer.

2. augmentation of human perception

Genuine visual analytics applications encourage visual thinking by leveraging the powers of human perception. The human brain possesses an amazing capacity to process graphics faster than it can process tables of numbers. Unfortunately, most business intelligence packages and spreadsheets do not take full advantage of the brain’s perceptive capabilities. For instance, they use color and visual effects irresponsibly, and they ignore proven research on displaying data without bias.

In a visual analytics application, properly visualized data “pops.” For example, relationships, trends and outliers show up bright and clear—aiding both the user and the audience alike.

3. visual expressiveness

No aspiring painter would put up with a paint-by-numbers canvas. But that’s what many programs force on people when they use charting wizards and dashboards. Good visual analytics tools accommodate people’s need for depth, flexibility and expressiveness in the visual displays.

This is especially important when people need to look at more than two or three dimensions of a problem simultaneously. Imagine putting five dimensions of a problem (e.g., Year, Month, Region, Product Family and Units Sold) into a charting wizard: the result just doesn’t come out well. Visual analytics applications let people visualize multiple dimensions of a problem effortlessly, in formats that are easy to understand. Where cross-tabs and pivot-tables often confuse and overwhelm, multi-dimensional visualizations clarify. Visual analytics applications display complex problems with elegant simplicity.

4. automatic visualization

Imagine an application that tells you how you should look at the specific problem you have. For too long, analysts have been taught to think in numbers alone. A visual analytics application jumpstarts the analysis process itself. This includes automatically suggesting effective visualizations.

A key benefit of automatic visualization is not just that it reduces work time. It also helps people learn to think visually. If they can think in pictures, they can work faster and recall trends and patterns more easily.

5. visual perspective-shifting

There is never a single visualization that offers the best summary of every finding in your data. Typically people need to look at a variety of visualizations, depending on the tasks you want to achieve. Effective visual analytics applications should suggest a series of alternative visualizations which can be effortlessly flipped through. For example, if you’re trying to find outliers, look at a scatterplot. Trying to understand time-based trends in the data? Then a line or Gantt chart might be ideal. Trying to understand multi-dimensional geographic variation? Try a small multiple of maps. No one view can answer all questions.

People on the lookout for useful information visualizations like to forage freely in data. So the last thing people need is a tool that confines them to a single, linear path. An inflexible tool creates a dataset and a chart and tries to stick with it. A visual analytics application instead offers direct access to a myriad of visualizations, with no boundaries.

6. visual perspective linking

A logical but powerful addition to perspective shifting is perspective linking. Although the two topics are related, linking entails a different set of capabilities than perspective shifting. In short, it isn’t enough to look at multiple perspectives on a problem in rapid succession – or even simultaneously. Sometimes the perspectives need to be intimately linked. One visualization may display a set of outliers, for instance. Can a person select an outlier and instantly see another visualization that displays greater detail? As an example, a person may notice that sales for a particular state seems to be dominating. By clicking on the mark representing that state’s sales, he can instantly update a visualization regarding sales amount by company and see another visualization redraw a line chart showing sales by date using data for just that state. That interaction may shed light on what is driving sales in that state to be dominant.

 

7. collaborative visualization

Another defining capability of effective visual analytics applications is the ability to iteratively create useful information visualizations in a team setting. This process usually starts with a “hey, look at this” moment. But the real question is: Does the software support the involved collaborative process that should follow these moments?

Shared findings lead to solutions, action, and results. In fact, in most organizations unshared discoveries are useless. Some software packages, while meeting other criteria, fail here. Effective visual analytics software encourages collaboration by letting results be shared in whatever form the user prefers. The application’s architecture should be built explicitly for collaboration.

This article is extracted from a whitepaper Selecting A Visual Analysis Application. Download the whitepaper for a full discussion of these elements -- including questions you should ask of any visual analysis software vendor.