Big Data, Crowdfunding and the Fundraising Technology Craze

“They see that if they do it correctly, they get more return out of the data,” she adds. “It was an unintended consequence, not necessarily a goal, but we saw that the data wasn’t as clean as we thought. This helped encourage clean data.”

This has all led to a more efficient donor data process, which in turn has helped JDRF’s fundraisers learn more about their donors — and send them more relevant, more targeted messaging.

Lessons learned
Embarking on this process, JDRF has gleaned many things when it comes to managing and utilizing big data. Martin shares some of the major takeaways:

One size does not fit all. Not everyone is able to digest data the same way, and when you are a larger organization like JDRF, there are a lot of people looking at data. That means you must provide a balance between giving turnkey information to people who aren’t that data-savvy, Martin says, but then also giving analytic capabilities to those who want to dig deeper into the data.

“Data can be overwhelming, and if you are not careful, it can be distracting,” she says. “Sometimes people focus on the nitty-gritty of the numbers and tackling exact numbers day by day instead of looking at the bigger picture of using data to inform strategy.”

Business process has a huge effect on data analysis. Due to the nature of silos and data entry, sometimes there is a lag time between different data sets. For instance, when an event sponsor registers vs. when the check actually gets recorded into the finance system can differ, which means the dashboards may not match due to this lag. And sometimes one department might pull reports once a week while another pulls reports once a month, leading to more lag.