Selecting appropriate visualizations was initially explored in point ten of this post, Select the best, not the best looking, visualizations.
But, other than avoiding the temptation to be consumed by visual puffery, there are a number of other vital visualization best practices to abide by.
It’s imperative that you understand the difference between categorical and quantitative information, how they interrelate to produce meaning, and how this impacts the associated methods of data visualization:
Quantitative information (measures and metrics) is countable and measurable
Categorical information (dimensions) represent different sub-sets of data
For example, you might count the number of orders that leave a warehouse, whilst dividing those total orders into different categories based on a predetermined value or benchmark – small, medium or large.
Ensure your graphs are proportional and accurately represent the raw data. For example, If utilizing a bar chart to communicate the fact that there are twice as many large orders leaving the warehouse compared to small orders, the bar representing large orders must be exactly twice the length of the bar representing small orders.
It may sound obvious, but failure to accurately reflect the value of comparable data types can lead to complete misunderstanding of performance and total confusion. Oh, and of course subsequently poor decision-making. - See more at: http://www.yellowfinbi.com/YFCommunityNews-Top-Business-Intelligence-dashboard-design-best-practices-Part-Two-118725#sthash.b2f5i9LO.dpuf