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How much data analysis is too much?

How much data analysis is too much?

If you thought data analysis was all tech, you’d be wrong: this science will change the way you think about every sector, from marketing to manufacturing, politics to sport. Is big data the next big thing, or is it a big distraction from getting on with business?

Brad Pitt starred in a movie about a data analyst who changed the game of baseball. Nate Silver is the toast of the data geeks after correctly predicting the results of 2012 US election. Clicks, traffic, links and shares drive anything online. The things that underpins them all? Sound data analysis.

In Moneyball, the 2011 Oscar-nominated film about how sabermetrics—the analysis of baseball statistics—changed the way players were valued, Billy Beane, played by Brad Pitt, faces the prospect of fielding a baseball team with the lowest payroll in the league. There’s no salary cap, so the poor Oakland Athletics club is up against teams with very deep pockets. Says Beane: “The problem we're trying to solve is that there are rich teams and there are poor teams. Then there's fifty feet of crap, and then there's us. It's an unfair game.”

Beane hires Paul DePodesta (Peter Brand in the film, portrayed by Jonah Hill), an economics graduate from Harvard (Yale in the film), to turn things around. The statistician looks past apparent talent, instead talking about the numbers required to win a game.

Says Brand: “People who run ball clubs, they think in terms of buying players. Your goal shouldn't be to buy players, your goal should be to buy wins. And in order to buy wins, you need to buy runs… It's about getting things down to one number. Using the stats the way we read them, we'll find value in players that no one else can see. People are overlooked for a variety of biased reasons and perceived flaws. Age, appearance, personality. Bill James and mathematics cut straight through that. Billy, of the 20,000 notable players for us to consider, I believe that there is a championship team of twenty-five people that we can afford, because everyone else in baseball undervalues them.”

The success of the Oakland A’s in the first year of ‘moneyball’ and the subsequent profile of data analysts such as Nate Silver have pushed big data into the limelight. Data allows us to see things our biases hide from us; it gives hard evidence to what we’ve only previously speculated on. But it is not a remedy for those ills. Just because data can diagnose what ails us, it does not mean it can cure us with a brush of mathematics.

Professor Michael Wade, of executive education provider IMD, puts it this way:
“To explain, let’s use a golfing analogy. If, like me, you tend to slice the ball, you might decide to visit a golf pro to take lessons. The golf pro will observe how you hit the ball and try to fix your swing. In doing so, he is unlikely to say, ‘your slice comes from a fear of commitment that resulted from the insecurity you felt as a child’; nor will he tell you, ‘your slice is due to a variance in deltoid muscle mass between your dominant and non-dominant sides’, despite the fact that both of these reasons may be true. In fact, the cause of the slice is irrelevant; what matters is how to correct the problematic behaviour. You are more likely to hear him tell you something like this, ‘when you swing, rotate your hips and place 80% of your weight on your left leg. Now, go and practice that 100 times.’ When it comes to a golf swing, causes are mostly irrelevant. What matters is changing behaviours.”

The biggest problem with big data is that it tells us too much about the things we don’t need to know. The skill of an analyst is knowing which bits are important and which aspects to, not so much ignore, but put on the backburner as not relevant for a particular research task. Data serves to inform decision-making, it does not dictate.

If you have (or want) an organised mind that can make sense of large sets of data, whether you’re seeking an analyst or researcher role, brand new courses such as Data Science can teach you the skills you need to sift, examine and apply data to assist in modelling and decision-making.

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