First of all, John Foreman is great. Read his book "Data Smart" and <a href="http://analyticsmadeskeezy.com/blog/" rel="nofollow">http://analyticsmadeskeezy.com/blog/</a><p>(disclaimer: I am in no way tied to John Foreman. Also, I work at a company that provides a data processing/collaboration SaaS...for big data! <a href="http://www.treasuredata.com" rel="nofollow">http://www.treasuredata.com</a>)<p>A quote from the OP:<p>>If your business is currently too chaotic to support a complex model, don't build one. Focus on providing solid, simple analysis until an opportunity arises that is revenue-important enough and stable enough to merit the type of investment a full-fledged data science modeling effort requires.<p>This is consistent with what we see in our customers. The use cases we see most with processing big data boils down to generating reports.<p>Generating reports may sound really prosaic, but as I learned from our customers, most organizations are very, very far from providing access to their data in a cogent, accessible manner. Just to generate reports/summaries/basic descriptive statistics, incredibly complex enterprise architectures have been proposed, built by a cadre of enterprise architects and deployed with obscenely high maintenance subscription fees billed by various vendors. That's the reality at many companies.<p>As bad and confusing the buzzword "big data" is, one good byproduct is that it has forced slow-moving enterprises to rethink their data collection/storage/management/reporting systems.<p>Finally, I am starting to see folks do meaningful predictive modelling on top of large-ish data (in the order of terabytes). Some of them are our customers at Treasure Data, some aren't, but they are definitely not "build[ing] a clustering algorithm that leverages storm and the Twitter API" but actually doing the hard work of thinking through how (or if) the data they collect is meaningful and useful.<p>And that's a good thing.