Outsourcing of analytics !

Context : This note from Bill Franks

Bill has aptly put analytics outsourcing in perspective and I could not stop myself from expanding it further to my earlier post.

Spot on Bill Franks! I wonder how the third party analytics (so called knowledge powerhouses) companies will take it as they claim to manage end-to-end analytics (but mostly end up in creating just the reports, huh !).

I have personally witnessed the gigantic mistakes made by some global corporations (some are still walking that path), only to painfully course correct after extensive damage done by blind outsourcing. In my view, apart from the obvious cost benefit (only on paper) narrative of outsourcing; the outsourcing mistakes are also made due to the ‘generalists given responsibility to manage analytics’ within the organization. On one hand these decision makers are clueless about the mechanics of setting up analytics on the other they find it very convenient to outsource ‘everything’. This buys them significant time to get incarnated as ‘analytics evangelists’ and at the same time the outsourcing partner gets all the crap of not been able to meet the expectations.

By the time the mistake is realized, the generalists now converted into analytics evangelists remember enough analytics jargon to sell themselves into some bigger role or get into some other organizations again to repeat the same modus-operandi and outsourcing business continues !

The irony of being a data scientist !

The most ironical fact is to explain what it  means to be a data scientist to almost everyone who think that it is Version 2.0 of reporting analyst or even worse a database administrator. Even the people who put money on these unusual professionals expect them to sort out the data shortcomings and produce reports every now and then !

Understanding data and being capable to make unruly data  worthwhile for decision making does not necessarily mean that it’s a data scientist job to bring discipline to the data chaos. In fact it’s a virtue that often gets misread as the core capability of data scientists and the real benefits of using these minds and skills to solve business problems or identifying new business opportunities often take backseat.