This first walkthrough exhibited some of the base techniques to explore a data set visually:

• Parallel coordinates and other overviews let you get an overall glimpse of how your data sets are organized, what columns seem to be correlated, and what are the distributions of each column.
• 2D graphs, augmented with dynamic queries, let you focus on two or more numerical attributes and explore more in detail the relationships they have with one another, pinpoint the exceptions to overall trends, and spot additional characteristics of your data.
• Grid maps and treemaps are useful for categorical data, to slice and dice your data sets in categories of various kinds, and understand the relative weight or other characteristics of each category.
• Finally, multiple views exploration lets you establish parallels between numerical and categorical attributes, and so the interplays between each other.

You can test  these predefined visualizations with your own data sets to start seeing them under a new light. You may also want to explore now the data samples provided as examples to discover some more realistic applications of ILOG Discovery to data analysis.

ILOG Discovery offers many more features to let you get the most of your data: you can load multiple data sets, create sub tables to refine your analysis, store views and projects to retrieve them later or share them. 

Once you will start to get proficient in using ILOG Discovery, you will probably want to unleash its complete power by examining its unique advanced features.