One person's junk is another's treasure; one person's "normalized data" is another person's "you removed the one data point I cared most about!" One thing this article is reinforcing to me is the value of domain knowledge to an analyst. I am deeply skeptical of "one size fits all" analysis tools, services, and consultancies for exactly this reason. Making insight actionable requires knowing what actions can be taken,…
I got hung up early on by the use of "aggregation"... these visualizations still aggregate data, by the necessity of mapping to a fixed number of pixels! However, the principle is strong: the author is proposing visualizations that make full use of the pattern matching over 2.5 dimensions that our eyes afford us, and by using that range they are able to make fewer assumptions about which summaries of data are sufficient.
Domain knowledge is still essential, both to pick meaningful projections of the data and to drill into patterns once observed. But since domain knowledge is always limited, it's nice to have techniques that allow you to notice patterns you didn't know well enough to summarize.