Posted Wednesday, May 8th, 2013 at 12:48 pm by Marissa (51 posts)
Chances are you’ve heard the expression “big” data. And chances are, your organization is excited about the prospect.
But before you make a large investment in big data, have you exhausted everything that “small” data can do for you?
There are a lot of varying definitions of big (and small) data. The results of either should lead to segmenting and optimizing. The difference is how much. If you haven’t been doing that all along, you may not be ready for big data.
Here is how I see the difference between small and big data.
Small data is information about a specific behavior that you can use to optimize your site to encourage that behavior. It has been around (and underutilized) for years. In looking at a donation report, I can see that 70% of my end-of-year donors are repeat donors. And 60% of those who have been around for more than 3 years increase their giving level. Knowing this information, I can segment and optimize my calls to action. In this case, I’ve collected data about a behavior (donations) to help modify that specific behavior (donating). This data has always been there, and is likely all in one place.
Big data looks at a larger combination of data points that may come from a variety of sources. Sometimes these data points may only be tangentially related to the behavior you’re trying to modify. But when you pair the points together, patterns can emerge. For example, through surveys, data collection, and market research, I may find that a donor who is female, single, and in the 75% income bracket is 20% more likely to respond to an ask that features a young child than a male of the same demographic. And based on this information, you can segment and optimize your asks. You’re just segmenting and optimizing more precisely.
That level of precision can take a lot of time and effort, and is still very much an imperfect science. So before you start considering the implications of big data in your organization, ask yourself if you’ve invested all you can in small data. The better pathway to big data may be to start small.