Unfortunately, like most things in life, at some point there will be diminishing returns for the amount of data being collected. The illustration below shows how there is a “sweet spot” for the effectiveness of data relative to the amount of data collected.
Moving along the horizontal axis, as we collect more data (e.g., geographic information, demographic information, transactional information), the effectiveness and utility of that data increases, as well. For example, with more transactional data, we’re able to better target our marketing information to members and customers.
However, with the temptation to increase the types of data we collect (e.g., company size, length of time in career, professional level, etc.), the utility of that data actually begins to decrease (vertical axis). After all, how likely is it that we can develop a viable product that will be attractive to only doctors practicing in the state of Hawaii who have been in their career for less than ten years and have blue eyes?
So when do we know we’re collecting the right amount of data? One method is to look at all of the types of data you’re currently collecting and ask yourself “How often do we use that particular type of data?” For example, if you’re currently collecting level of expertise on your members (e.g., beginner, intermediate, advanced), ask yourself how often that data actually is put into use. Is it used at all? And if it is used, what is it used for?
Work through your database and look at all of the fields and determine their level of usefulness. You’ll be surprised at how much data you’re collecting that you’re not using. If you’re like most organizations, you’ll likely find data you no longer need to collect, and more importantly, data you can use more effectively.
One more thing to keep in mind: the data that you need today may not be the data you need tomorrow. As you add more data to your collection efforts, be sure to stop collecting data that is no longer needed.
Since storage is cheap, why shouldn’t you just keep collecting any and all data? As the chart illustrates, you will reach a point of diminishing returns, because collecting the data has “hidden” costs. These include aggravation to the customer (“Stop asking me these questions!”), analysis paralysis (“We have so much data we don’t know where to begin analyzing it.”), and the incremental costs of data entry (i.e., every field of data that a staffer has to enter is additional work). Remember, just because you can, doesn’t mean you should.
This article originally appeared in the May 2, 2008, issue of Association Trends. Reprinted with permission.
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