You can’t dig into Big Data storage without first discussing Big Data in general. Big Data is a concept that any IT professional or knowledge worker understands almost by instinct, as the trend has been covered so extensively.
Data has been growing exponentially in recent years, yet much of it is locked in application and database siloes. If you could drill into all of that data, if you could share it, if you could cross-pollinate, say, a CRM system with information from your marketing analytics tools, your organization would benefit. Easier said than done.
That, essentially, is the Big Data challenge.
Arguably, the concept of Big Data entered the public imagination with the publication of Michael Lewis’ Moneyballin 2003. Of course, the term “Big Data” is nowhere to be found in the book, but that’s what the book was about – finding hidden patterns and insights within the reams of data collected during each and every major league baseball game.
One statistic that has been buried – well, buried isn’t right; ignored is more accurate – was about drafting college players over high school players. College players have a track record. They have statistics that can be measured, and they played against at least a half-decent level of competition: