I couldn't really follow this thesis. This observation popped out to me:<p>> <i>[Redshift] was a brilliant move by AWS, because it immediately lowered the bar for a small company to start doing analytics.</i><p>Every sales organization I've ever seen collected too much data. What the kids today call "analytics".<p>And these orgs don't know what to do with it all. So much so that they delude themselves. Convincing themselves they're divining wisdom from noise. I have no idea what this phenomenon is called.<p>In fact, most recently, my last gig had decades of data hosted on Teradata, was migrating to Redshift. I worked on the Recommendations team, which was trying to evolve into Personalization.<p>Teams of data scientists. So much data. A cultural legacy of batch processing hidden behind ML pipelines. So much effort.<p>It took me a while to figure out most of the "work" my team did was completely fictional. Our most effective recommender algorithm was just showing people what they'd already looked at in the last 6 months. But this simple truth was hidden behind a massive rube goldberg machine.<p>So. What was true of CRM and ERP systems in the 90s remains true today. Collecting data without purpose, without a working hypothesis, without experiments validating the effort, is just wasted effort.<p>In my time, I've worked with two very smart marketing people. Knew how to design a survey, how to crunch the numbers, validated their own work. Once you see how a pro does it, you realize most everyone is just faking it, fooling themselves along with every one else.<p>Pretty much just like every other discipline.<p>--<p>Oh. How does my cynicism relate to the OC's prediction about a cloud shuffle?<p>For the users of cloud stuff, making data collection, aggregation, and analysis easier and more accessible is a net negative.<p>Which I suppose is great for cloud providers.