It is important to discuss the techniques of DV as with the advent of big data, with increasing volume and velocity of data, emphasizes visualization as a technique to compress large volumes of data into digestible presentations and observe streaming data in motion.
Data cleanse is a first step for any visualization effort. The key, exporting to standard presentation software.
I agree with John Wilkerson . If you want to dive into more details:
1. Take care of the missing values
2. If your data is too high in dimensions, you can perform dimensional reduction processes.
3. Consider what are the insights you are thinking as useful for your visualizations.
Don't ask what data you want to see in your visualization, but ask what you want to do with the visualization, i.e. what information you try to extract from the visualization. Then you can decide on the needed steps for data cleansing, compressing, aggregating, filtering, clustering, sampling, etc. and what tools are needed for these steps.
Henning.
@Henning
I fully agree with your point that ' ask what you want to do with the visualization, i.e. what information you try to extract from the visualization'.
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