A picture paints a thousand words, and this is particularly true when trying to understand data and close the understanding gap. Faced with a million rows of data, there is often no better way to view it to understand what quality issues there are, how the attributes within it relate to one another, and whether there are other systematic features that need to be understood and explained.

There are many types of visualizations that can be used and it is also important to combine these with the use of descriptive statistics, such as the mean and standard deviation.

Examples include 2D and 3D scatter plots, density plots, bubble charts, series, surfaces, box plots, and histograms, and it is often important to aggregate data into summaries for presentation because the larger the data gets, the more time it takes to process. Indeed, it becomes mandatory to summarize data as the resource limits of the available computers are reached.

Some initial techniques are given in Chapter 3, Visualizing Data.