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Dear Analyst 9: Bad Data Blues

Mon, December 12, 2016 9:43 AM | Deleted user

By Elisa Shoenberger, Benchmarking Analyst, Grenzebach Glier and Associates; and Katherine Ingrao, Associate Director of Prospect Management, Rush University Medical Center

Dear Analyst,

As a prospect researcher, I’m expected to put together a bunch of reports on our prospect pool and major gift donors on a regular basis. I work with a lot of data in donor records to help enhance my profiles and other prospect researcher. But the data quality isn’t as good as I was hoping. There’s stuff that’s missing or just plain wrong in contact reports, proposals, and so much more. And the worst part, it’s not even my data. How do I work to get better data into our system without ending up doing it all myself?


Bad Data Blues 

Dear Bad Data Blues,

You’ve hit upon a major difficulty that anyone who deals with data has faced. We don’t want any data; we want good data. If bad data is put into a system, all you’ll get is bad data from the system. In other words “Garbage in, garbage out” We need to have good data in our database in order to do the great work.

However, what exactly is “good” data? What does “good” data look like in our database? How do we ensure we are entering “good” data into our systems? These are really important and sometimes hard questions to answer but are crucial conversations to have within your office. Ideally, the natural result of these conversations would be the creation of an office guideline for data entry and quality control thus ensuring that everyone has a shared understand and expectation of your data and its quality when retrieved through reporting. During these conversations, it’s important to remember that you’re aiming for prudent policies not perfect ones.

A good starting point when developing policies or reviewing your current ones is, contact reports. What does the content look like and how are people entering it? For some institutions, it’s a decent summary of the encounter with the donor and it is filed in a timely manner. A decent summary could mean 1-3 sentences while another shop could require several paragraphs. A timely manner could mean within a week of the contact or within a month. It all depends on the institution and what your shop can maintain over a long period of time and honestly, what is actually useful in the ability to raise more funds! Whatever you come up with, it’s important that your institution create concrete definitions, document them and stick to them! People need consistency in data and policies for them to be effective!

The next step is disseminating the knowledge. People need to be trained on what is expected of them. Having the documentation is key and can be useful as a takeaway when onboarding new staff. I frequently held trainings with new gift officers and other staff about how to enter data correctly and it was important to remember that some gift officers may outsource their data entry to their administrative staff so don’t forget to include them in any trainings!

One hazard you have to watch for is that you don’t want to be the one who ends up entering people’s data for them. Previously, Dear Analyst 6 discussed this with respect to proposal data. This is very tempting to do. After all, you work with data all day and know how it should be. But this won’t help the situation. People won’t learn how to do it correctly if they aren’t a) required to enter their own data or b) have to correct it when it is wrong. We don’t want gift officers to rely on us to enter data. Some organizations do require their prospect researchers to be responsible for entering proposal data but if it’s not part of your responsibilities already, you probably don’t want to make it one.

While training and documentation are essential, they alone are not enough. Managers must make good data a priority. They must hold people responsible for having their data up to date. Often times contact reports and proposals can be tied to performance metrics so there is an added incentive to have this information up to date in the system. There must also be a way to review the information to see if it is being entered correctly as well. Otherwise, you won’t know if the data is being entered correctly. Reports and audits are useful to check whether data is being entered correctly.

Finally, it is important to be a model of good data in your institution. Prospect Research needs to set an example to others about good data in the system. Make sure any data that your department is directly responsible for is correct. For instance, make sure to upload research profiles in a timely manner. If there are mistakes, fix them. Keep your prospect ratings up to date based on recent research.

These are just a few things to help in obtaining good data. We’d love to hear how your organizations manage this process. Let us know at Dear Analyst.

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