Our March blog post comes from Kristin Richardson, Director of Development Research Analysis at The University of South Carolina

Data analytics—it’s the buzzword that prospect research and management can’t seem to stop using.  But what does it really mean?  And is it something that is even possible for everyone?

Well, a lot of that depends on your shop.  If you are a bare bones shop using Office products to get your analysis done, you’re going to have a few more hurdles to clear.  Larger shops, with more software and staff, will certainly have an easier go of it.  But that doesn’t necessarily translate into robust data analytics output.  Regardless of the size of your shop, there are two key components that both need in order to succeed with data analytics: human resources and functional data.

To fully implement a strong data analytics strategy, you have to have the right staff.  Not everyone is comfortable with linear regressions, ANOVA, and all the other statistical formulas that are used in data analysis.  There are still plenty of us in prospect research who can barely use the basic functions available in Excel!  In order to build an analytics component in any size research shop you have to have the personnel with the requisite skills to make it happen.  For those shops without an existing data component, this will mean evaluating the strengths of your existing team and possibly rearranging daily functions.  Even though all team members may have the same title, not all will have the same skills.  In our shop, we have one researcher who is most proficient with the research and writing, updating the CRM, and identifying strong prospects that have already been prescreened or modeled.  Our other researcher, while having similar skills, is also very talented at statistical analysis; thus, her annual plan is weighted more heavily on the data side.  It’s also important to realize what duties make the members of your team most productive. If someone is a whiz kid at manipulating pivot tables, but enjoys writing profiles more, let them do more of that; it’ll make everyone happier and more productive.  If you are in the position to hire new staff, and don’t have someone with strong data skills in place, use this opportunity to rethink the job description.  By overlaying some of what you want data analysis outcomes to be for your shop onto the prospect research job description, you can try to find someone with both skill sets.  And if you’re really fortunate, you can make the case to leadership to hire a true data analyst, someone whose sole function will be to perform data analysis projects as part of your prospect research deliverables.

The other key component, functional, data is not necessarily within your sphere of control.  There are plenty of factors that keep data from being “clean”—the number of users for the CRM, training, data cleansing, report writing and gathering, and the list goes on.  What is working for us in getting better data into and out of the CRM actually happened as by-products of other initiatives.  A combination of in-person training with online tutorials is helping condition the CRM users in the proper way to input data into the system—what fields to use, when to use them, when not to use them, and the like.  By continual reinforcement from the prospect management and research teams of how the data is captured and utilized, we are finding that data is more functional now compared to 3-4 years ago. What has really made the biggest difference?  Getting better at articulating what we want the data to tell us and communicating that with the information services team members who are going to extract that data.  Filling out a request form with a simple statement such as “All donors with giving over $100,000 and ratings under $100,000” doesn’t really tell that data extractor much, does it?  But telling them you are looking to identify those donors who were rated lower than gifts received to help model future donors by looking at key indicators such as education, marital status, giving outside the institution, etc., will certainly help them build a better report.  And an added bonus is you’ll also get more data that you won’t have to pull out manually because they know which tables to pull this data from in the CRM. Additionally, forging a strong relationship with the information team will go far—never underestimate the power of respectful questioning of what they do and how they do it.  You know, the more you know!

Data analytics isn’t going anywhere—if anything, we are going to see more and more of it being expected from any size prospect research department.  Keeping in mind the key components which are under your control will help you get there with less stress.  Even if you don’t know the difference between a data set and a linear regression, remember this: human resources and functional data are all under your sphere of influence.  Even if you don’t know the difference between a data set and a linear regression, remember this: you can influence your human resource capital and work within your department and with the information systems team to improve your data.  Bringing data analytics to your institution can now be a reality keeping these key factors in mind.