Ever since someone decided to stick the word “big” in front of “data,” there’s been a temptation among organizations accessing this treasure trove of information to harvest as much as possible, without the necessary consideration to which pieces of data are actually necessary and how high the quality of that data is. For nonprofits using data for prospect research, this presents two major problems.
The two major problems nonprofits face when using data in their prospect research:
1. Organizations waste valuable resources slogging through extraneous or low quality data.
Nonprofits are generally strapped for time, funding and resources, even before they attempt to mine and analyze data. Indiscriminate data collection simply adds to the burden, especially when it’s not tied to a specific strategic goal. Nonprofits must learn to cherry pick their data sets if they hope to leverage them effectively.
2. Organizations miss opportunities for donor recruitment because of incomplete information.
The more data you throw into your basket, the greater the chances that you pick up poor quality, incomplete or misleading information. And that can lead to a failure to recognize donor outreach opportunities. For instance, having incomplete alumni information could lead to missing a connection with a prominent local donor who shares the same alma mater as your executive director. You’re doing the work to organize and analyze the data; make sure it’s worth your time and resources.
“Nonprofits must learn to cherry pick their data sets if they hope to leverage them effectively.”
It’s understandable that nonprofits new to the data game might experience these kinds of tough lessons; call them growing pains. But relationship management platforms that fail to discriminate between high- and low-quality data are perpetuating the problem. A data set comprised of tens of millions of people is all well and good—until your fundraising director maps a connection to a prospect with a profile that has incomplete data, and as a result, fails to pick up on a key element of this prospect’s giving history.
Here’s where Relationship Science differs. RelSci captures and maintains the people data comprising its robust 4 million people profiles and 1 million organizational profiles because we:
- Establish third-party partnerships with data providers
- Link to thousands of data sources
- Employ 600 human researchers to oversee and verify data collection
- Use an ingestion system that can flexibly and (relatively) easily be connected with new data sources
“Relationship management platforms that fail to discriminate between high- and low-quality data are perpetuating the problem.”
The bottom line: No matter how big “big data” gets, quality of information will beat quantity every time, and that includes relationship mapping technology.