The most important thing is knowing what data you have, how best to collect it, and what it can (and can't) tell you. Just because you find correlations doesn't mean that they are real. It takes people with real expertise to help here, and just running your data on a cluster isn't going to help you. In fact, it could even hurt.
I didn't see anything wrong with the article at all.
He doesn't tell anyone to collect the "important data," and he doesn't insist FB or Yahoo are not web scale.
His concluding paragraph is relatively weak, but the main thesis -- most businesses can ignore the Forbes/BI crap and analyze their data sufficiently using normal tools -- is true and sound.
Imagine our data was 98% junk with 2% of the data consisting of sequential patterns. We may be able to spot this on a graph relatively easily over the whole dataset but our random sampling would greatly reduce the quality of this information.
We can extend that to any ordering or periodicity in the data.if data at position n has a hidden dependency of data at position n+/-1 random sampling will break us.
You're not going to know what correlations are important and which are not until you study the data. Telling people to just collect the "important data" is like telling someone who has lost his keys just to go back to where he left them.
It's also more than a little insulting to FB and Yahoo to insist they are not web scale. The problem of small jobs on MR clusters is real, but even with small jobs, Hadoop turns out to be a lot more cost-effective than various other proprietary solutions which are your only real enterprise alternative. The problem of small MR jobs is being solved by things like Cloudera Impala, which can run on top of raw HDFS to perform interactive queries.