Sherlock Holmes - Big Data analyst

It has been recognized that the reason why Sherlock Holmes was so effective was that he observed and remembered everything. As such, he had access to an enormous data base. However, memory and observation alone are not enough. His strength was that he could access the data and put together apparently unconnected strands to create solutions. This was far from the concept of data mining. He had no idea of what he was looking for and no preconceptions of what he would find. He used patterns to identify those that were anomalies. This is the principle by which the future generation of Big Data analysis, such as Synerscope, will work. Pour in vast amounts of data, and simply by observing unusual or unexpected patterns, the user, not someone with an advanced degree in data analysis, can draw conclusions fast and easily.

If failure on Wall Street is simply bad luck, then is success only good luck?

Today's New York Times carries a story about Top Wall Street executives who have lost $ Billions becoming hot properties once they have been fired, sometimes being offered even better positions than those they lost. While middle ranking managers get fired and have problems being re-hired, top executives are assumed to have merely been unlucky. It seems that Wall Street sees them as smart simply because they have been in proximity to so much money.

It is rather surprising that failure on an enormous scale is seen as the result of bad luck, whereas success is never ascribed to good luck, but to skill and intelligence. History tells us that this is more so today than at any time in history.