Imagine you’re in a typical office. Look around you. There are patterns everywhere. They’re in the stripes on the carpet, the tiles in the ceiling, and even in usual clutter of a workspace. What will you do about those patterns? Do you clean up the clutter or lay down new carpet? How much time and money would it take to make these changes? Is it worth it?
Informed business decisions are made the same way, by identifying patterns in data and drawing informed conclusions from them.
Collecting data takes time and effort. So why bother? Let’s use Walmart as our example. If Walmart — which generates more than $470 billion dollars in revenue each year — could more predict customer spending patterns and improve its sales by 5%, it would generate $23 billion more each year.
Data analysis is hardly new. Ancient Egyptian pharaohs conducted a census before building their pyramids. IBM built its empire with the Tabulating Machine, a punch card system for storing data. But data is more important today than it ever has been.
The answer is leadership. Moneyball, a movie about the birth of modern baseball management, begins by depicting scouts who relied on eyewitness opinions when drafting players. Billy Beane, the Oakland A’s general manager, realized that athleticism alone didn’t win games. It was the numbers that mattered. The team he built made the playoffs in 2002 despite fielding a much smaller payroll than many of his competitors, demonstrating the value of his data-driven approach.
Tennis is no stranger to data analysis. It’s become increasingly common for coaches to use statistical measures to analyze a player’s shot selection and consistency. This is generally more effective than traditional coaching methods, as players are able to better visualize areas of improvement with data.
The takeaway is that quantitative observations will beat qualitative ones. This is true in many disciplines beyond sports. One study pitted 87 Harvard law professors against a statistical model to see which side could more accurately predict the outcome of Supreme Court cases. Of course, the model won. Opinions are always biased, but data never is.
In order to better understand their market, businesses separate their customers into distinct groups. There are three ways to do this: clustering, network analysis, and text mining. Clustering measures similarities between customers. Network analysis determines how customers are interrelated. Text mining analyzes social media and other online content and extracts what the customers want.
Today’s competitive landscape makes data analysis a necessity rather than an optional benefit. Proper use of data can make an enormous difference. Every large company now utilizes extensive data analysis, making it a baseline standard for any business that wants to succeed. Businesses that use data analysis successfully can better understand their target markets, which helps improve the most important piece of data — the bottom line.