I used the following textbook as an undergrad student, and found it informative:
Information Visualization Design for Interaction by Robert Spence
Here are a few key high-level concepts:
- The way data should be represented and presented depends on the type of data and how it should be understood by the viewer.
For example, the way you would present population density and size respective to location might would very different from how you would present a public transit railway map. Both present information related to locations, but each has their own desired focus.
For the first example, you might use a true-to-scale map with colours to represent density and dot size to represent city size. With a railway map, true-to-scale accuracy might be abandoned for the sake of understandability and colour might be used to represent the different 'lines' of travel.
In both cases, they would be considered 'maps' but on inspection they would be very different things.
- The closer you can get the viewer's perception (think "at-a-glance") to the desired interpretation, the lower the likelihood of mis-interpretation of the data.
For example, with a million pieces of data plotted in a scatterplot graph, the use of colour could be employed to display 'clouds' of same-attribute data points. If there are under 8 colours used in such a manner, this could effectively display patterns data. If there are many colours used, however, this would be less effective due to human limitations on how we perceive colour differences. Dark-blue data and purple data effectively become the same thing in a larger (10+) group of colours, mis-leading the viewer into seeing clouds of data where there is none.
The book goes into more detail on more facets of Representation, Presentation, Perception and Interpretation. If your long-term goal is to create interactive visualizations, I recommend you use an resource such as this to understand the 'theory'.
If you're looking for something more "quick and dirty" to get the job done once - that is, if you're less concerned about understanding the theory behind presenting data, and more interested in a one-time presentation - a few more details on your specific data would be useful.