Professors pounded it into my head over and over again—businesses use data to carefully plan and execute their short- and long-term strategies. And that's what I believed until I started interviewing for jobs. It seemed like every company I spoke with was fixated on growth—not because it made good business sense, but because they were fixated on chasing numbers. Open 500 stores this year, 2,000 next year, and 1,000,000 in year three. Will the local market be able to sustain that growth? Considering that now-bankrupt Circuit City was one of the companies I spoke with, I'm guessing not. Were they carefully looking at feasibility studies, evaluating the size of the markets they were hoping to enter? Or were they too busy opening up across the street from their key competitor in just about every possible city?
With an almost limitless amount of information at our disposal (years of historical data, complex analytical models, competitor benchmarking, etc.), you'd think more decisions would be deeply rooted in data analysis than they would on gut instinct. Yet, that's often not the case.
MBAs, PhDs, and a limitless number of Excel models have taken number crunching to a whole new level and the analysis is becoming increasingly more complex. We need look no further than the difficultly Wall Street had in valuing the toxicity of their exotic financial instruments to understand that. At large organizations with multiple business units and thousands of employees each doing their own modeling and projections, the very thought of integrating data and analysis from dozens, if not hundreds, of quant jocks is incredibly daunting. Even if done effectively, facts and texture almost certainly get lost in conversations up, down, and across the organization.
Adding to the complexity of the sheer volume and sophistication of the analysis are good old fashion time constraints. You're faced with multiple hard deadlines and there clearly aren't enough hours in the day to go over all of the data. So, instead of missing a deadline, you decide to quickly glance over the information and make a hastily-formed recommendation. I'm not sure what the Vegas "line" is on this approach, but I'm betting the odds are good that it comes back to haunt you.
Some companies place more of a value on risk taking than others and operate under a "throw it against the wall and see if it sticks" mentality. Or, the organization might not have the internal resources to thoroughly and thoughtfully analyze data and use the results in their decision making process. As a result, they often perceive analysis as a roadblock to progress instead of a tool.
Is intuition-based decision making such a bad thing? That depends on the scope and ramifications of the decision: If it's a relatively minor decision and the impact on the organization is minimal, there's a good chance you can get away with it. If, on the other hand, the decision could impact other business units and the organization as a whole, you definitely want to rely on more than your gut.
With the proper data analysis procedures in place, such tools are essential in making critical business decisions. You ignore that information at your peril. Just ask the former Circuit City management team who are probably still wondering, "Whaaaaa happened?"
Shawn Graham is the author of Courting Your Career: Match Yourself with the Perfect Job (www.courtingyourcareer.com). Find Shawn on Twitter @ShawnGraham or via email at shawn(at)courtingyourcareer.com.