by Angela Guess
Alan Nicol recently weighed in on what he sees as the great debate of data modeling: “Though using models based on data continues to grow in practice, an old debate continues to rage. Some would use models based on strong correlation and forego the often-difficult quest to establish cause, while others insist that models that are not based on cause and effect are invalid. I am encouraged by the long-lasting trend by which businesses and design teams are using models based on data to predict outcomes and control processes with ever-greater confidence and frequency. I’m a strong believer in the power of modeling data to better prepare or influence our businesses.”
He continues, “I admit, though, that in my first few engagements into the argument over whether models should be used based solely on correlation, or whether cause absolutely must be identified, I convinced myself that as industries became more proficient with data modeling the argument would die and I wouldn’t need to convince people that correlation isn’t enough anymore. My crystal ball was wrong. Instead, I find that as industries more readily seek correlations and attempt to build models, the arguments are more frequent and even more heated. I don’t have an explanation for why, but I’ve had the debate so many times that I do feel I can share some concise insight to help our readers the next time they are party to it.”
photo credit: cpaparcuri
