You've witnessed it. There's the consultant who heard that a certain sample size was good, and went on to use it for everything. And the engineer whose attraction to the elegance of linear models led to myriad elaborate data transformations in the name of linearization. Analysts who grumble during seminars, pooh-poohing any suggestion that new techniques have value.
In dating, there may be good reasons to stick with just one special someone, but in data analysis, it's ridiculous.
I'm not suggesting that you dump your favorites. But it's not uncommon to get so comfortable with certain tools or techniques that you use them out of habit, without giving much thought to whether that comfortable approach is really the best fit for your needs. Smart and experienced analysts are sometimes spotted using methods that are not right for the data in question.
You may have heard about the Netflix Prize: The video rental and streaming giant offered a $1 million bounty for a better model to predict movie ratings. One thing about that competition grabbed my attention -- one that I've not heard anyone else mention.
Netflix movie ratings are star ratings -- one star for a movie you detest, five for a movie you adore. These ratings are ordinal (suitable for ranking but not for adding, dividing, and so on). Yet the metric used to judge the competitors (root mean square error) isn't appropriate for ordinal metrics. This is a fact stated in thousands of textbooks, though often ignored in practice.
People treat ordinal ratings as ratio variables (variables you can use in numeric calculations) all the time. I'm no purist. If it works for them, it's usually OK with me. But that's acceptable when a rough solution is good enough, not when your corporate lifeblood depends on it. Not when you employ a staff of analysts. Not when you're offering a million-dollar bounty for a better model.
Think it over. Does the method you're using really fit the application? You just might find an alternative that gives better results, more defensible results, results that can give you a competitive edge. Want to learn some new tricks? Get a fresh point of view by researching how professionals in fields other than your own approach problems similar to yours. Attend a new conference, or just search online for presentations that many speakers post on conference sites or sharing sites (such as SlideShare). Even YouTube has some thought-provoking content on data analysis.
Today, give some thought to whether you might be using some methods due to habit instead of for better reasons. Is it time for you to play the field with some new analytic methods?