I am on a bit of kick on visualization of data now. It ranges from clipping interesting art work on asset allocation, performance or risk data; as well as geometric approaches to working with this type of data. I am thinking I'll try and put together a bit of a gallery at some point. I am hoping to include a lecture on this topic for the Financial Informatics class at BU, it seems a pretty natural fit.
Speaking about the class, this semester I think we'll be more hands on with some excel plugins, I'm putting one together from my java analytics using ikvmc and excel-dna, both excellent open source projects, together they make it a piece of cake to put together a really easy to use Excel add-in. When I get it packaged I'll put it up on sourceforge with the rest of my code.
Max Golts has a paper at ssrn, and he's done a few presentations on the topic for the Boston and New York QWAFAFEW groups which have been interesting. One of his thrusts is that if you look at the eigenvectors of the covariance matrix you want to align your asset weights with the more significant ones, rather than the least significant ones. If you're asset allocation lines up with the least significant eigenvectors you are working with the noise. It has taken me far longer than I hoped to understand what he's doing, but I am on track to eventually have some code to implement his approach.