Four Modeling Don’ts
Pitfall #4: Inconsistency in Data Storage and Deployment—The wrong data easily can be pulled into a model when the files receive inconsistent coding scores. Some business use a scale of 1 to 10, with 1 being the worst score and 10 being the best; other firms reverse the scale. Still yet, computer programmers often use 0 through 9 for data storage because it takes up a consistent number of bytes, says Herlihy. Analysts who don’t sufficiently study the database and find out what tagging methodologies are being employed (especially those who are new and inexperienced) are likely to pull low-performing deciles by mistake and build the wrong model. The best practice for businesses to learn is to stick to the same tagging methodology.
A final word of advice from Herlihy: “If you don’t have at least 1,000 of whatever you are trying to model, it is best to collect more data before you invest in the model.”
Herlihy can be reached at (972) 664-3600.