July 17, 2012
As one of the industry-renowned data visualization experts Edward Tufte once said, “The world is complex, dynamic, multidimensional; the paper is static, flat. How are we to represent the rich visual world of experience and measurement on mere flatland?” Indeed, there’s just too much information out there for all categories of knowledge workers to visualize it effectively. More often than not, traditional reports using tabs, rows, and columns do not paint the whole picture or, even worse, lead an analyst to a wrong conclusion. Firms need to use data visualization because information workers:
- Cannot see a pattern without data visualization. Simply seeing numbers on a grid often does not convey the whole story — and in the worst case, it can even lead to a wrong conclusion. This is best demonstrated by Anscombe’s quartet where four seemingly similar groups of x/y coordinates reveal very different patterns when represented in a graph.
- Cannot fit all of the necessary data points onto a single screen. Even with the smallest reasonably readable font, single-line spacing, and no grid, one cannot realistically fit more than a few thousand data points on a single page or screen using numerical information only. When using advanced data visualization techniques, one can fit tens of thousands (an order-of-magnitude difference) of data points onto a single screen. In his book The Visual Display of Quantitative Information, Edward Tufte gives an example of more than 21,000 data points effectively displayed on a US map that fits onto a single screen.
- Cannot effectively show deep and broad data sets on a single screen. Fitting in and analyzing hundreds or thousands of columns of attributes (dimensions in BI speak) is an enormous challenge. Imagine a typical drug trial conducted by a pharmaceutical company where each patient has thousands of attributes: physical, psychological, genetic, behavioral, etc. Analysts looking for patterns, dependencies, and correlations typically need to run the data through complex statistical models before they can find a pattern or correlation. Building such models and running them through millions of rows of data can be time-consuming and can tax even the most advanced software and hardware resources. But in a technique often used in the pharma industry, reducing each data point in a column to a single pixel and color-coding pixels according to their value ranges can let an analyst relatively easily visualize and identify a pattern and then quickly zoom in to research the details.
- Dynamic data content.
- Visual querying.
- Multiple-dimension, linked visualization.
- Animated visualization.
- Business-actionable alerts.
- Types of graphs, charts and other visualizations.
- Tufte’s microcharts.
- Cockpit gauges.
- Visual query.
- Visual exploration.
- Geospatial representations.
- Modes of interaction.
- Storyboarding fit for client and boardroom-level presentations.
- Data latency.
- Data granularity based on your requirements.
- What analytical engines does the ADV platform support? How does it access and process data?
- Is there an intermediate storage platform?
- How is the in-memory data model managed?
- What types of data can the ADV platform analyze?
- Does the ADV platform support write-backs?
- What platform/technology is the ADV output based on?
- What, if any, ADV-specific programming language is used?
- What are the ADV platform’s integration capabilities?
Last but not least, as you venture down the ADV road, Forrester recommends paying at least equal (if not more) attention to ADV best practices as you do to technology. In our other research, Forrester has identified multiple such practices including screen layouts, data-to-ink ratios, appropriate use of text and labels, using similar sequencing of objects, using parallel scales, minimizing the use of color, showing causality, and many more.