I used to admin OLAP infrastructure for a large Fortune 100. I think the article makes a lot of great points, but ultimately there are many paths to the same re

I used to admin OLAP infrastructure for a large Fortune 100. I think the article makes a lot of great points, but ultimately there are many paths to the same result. I'm not entirely sure what that group is up to these days but using column stores is a big part of it I'm sure, but I think ultimately deciding between precalculated vs. dynamically calculated pivots is the big choice. Whatever system you have can do that, but the real work IMHO is understanding the org enough to cover the 80/20 of what people will want to see. Ideally you want to get to a higher level of abstraction such that you continuously codify your method of analysis or pivots to traditional tables if possible in order for maximum repeatability. Sometimes I wonder if graphs or trees really make more sense though and OLAP being a tree of sorts is just a symptom of the RDBMS ubiquity, but this is just a meandering notion perhaps.