The Tampa Bay Rays spend significantly less on payroll than some of the wealthier teams in Major League Baseball, but get results that are sometimes better than those that wildly overspend. Their success boils down to two things – understanding how to be a hedgehog, and continual application of statistics and analytics into daily processes.
Future NFL Hall of Fame quarterback Peyton Manning is tough to beat. What’s his secret? Is it accuracy, the ability to throw a “catchable ball” or capability to diagnose defenses quickly? The answer is probably all of the above, to some degree. Yet stated another way, Manning’s offensive excellence comes down to two things –simplicity and ability to execute.
Billy Beane’s “Moneyball” approach to developing and staffing a professional baseball team has come under intense scrutiny as long time major league scouts and analysts take delight in sub-par performances of the Oakland Athletics. Beane however seems undaunted in using statistical analysis to undercover market place inefficiencies. Indeed, it takes courage to solve problems in a whole new—perhaps heretical—manner. And while plenty of folks take pleasure in Beane’s recent comeuppance, there’s a good chance he’s already had the last laugh.
Back in the late 1970s, traders buying and selling mortgages were pushed aside for new masters of the universe—“quants” or individuals that used mathematics to slice and dice mortgages into debt tranches. And in the same way, today’s traditional Business Intelligence (BI) professionals must be looking over their collective shoulders as business and IT publications tout the emerging role of “data scientist”.
Technology is often hailed as innovation vehicle, productivity booster, and enabler of a higher standard of living for all global citizens. However, the field of finance provides an interesting backdrop for what happens when an industry is pushed to its technological limits in the pursuit of automation and speed. Since advent of the telegraph, and…