Ops Insights #088 - Data Trust, Start with Duplicates
April 30, 2026 | Read Time: 3 minutes | Written by Jenny Kleintop
Fundraising leaders often say to me, "Our data is a mess." They are really saying something deeper. They do not trust the data, which means they do not trust the decisions coming from it. I see this pattern across teams raising $2M to $25M. Reports are pulled but questioned, lists are built but triple-checked, and metrics are shared but not fully believed.
So teams create workarounds like spreadsheets, side systems, and manual tracking. Over time, the database becomes a system of record but not a system of truth.
Clean data is about confidence. Confidence that a report tells an accurate story. Confidence that lists are correct without having to triple-check. Confidence that a fundraiser can move forward without second-guessing.
The goal is not perfect data. The goal is trusted data.
Because when trust goes up, speed and clarity follow. How do you get trusted data? By fixing and maintaining good quality data, often referred to as data integrity. It is time to get your house in order, and you can start by clearing out duplicates. Go run the duplicate checker in your database. How many do you have?
A handful, such as 100 or less? Plan to tackle them within the next 1-2 months. A decent size, such as less than 1,000? Find a super user or two and make a plan to tackle them within the next 3-6 months. A large amount, such as more than 1,000? Bring in volunteers, interns, or recruit more super users. Plan to tackle them over the next year. Add it as one of your yearly goals to tackle to ensure it will get done.
Tips:
You will always have duplicates. That is a normal thing with databases.
Keeping a handle on them is part of managing data integrity.
Be cautious of using mass-merge tools. They have always made me nervous by merging the wrong records, such as father/son or woman/business owner.
Don’t do it alone. Make it part of your yearly goals and enlist others to help.
After running the vendor’s duplicate checker, run a second check. Export all data to Excel. Do comparisons on last name and partial address. Use Google or AI to help with creating the formulas for this.
Here’s how I led the cleaning up of 4,700 duplicates:
Enlisted 4 people across the development department and called them super users.
Led bi-weekly sessions to collaborate and tackle data integrity projects together.
Made the merging of duplicates a goal as part of our strategic plan for Dev. Ops.
Ran a Useless Records query and knocked out 1,000.
Asked each super user to tackle 925, aiming for 75 a month, me included.
Each month, gift cards were passed out, and the person who tackled the most that month was celebrated.
What happened? We finished them all, and 5 months ahead of schedule.
Take Action
To work toward clean data and trusted data, follow these 3 steps:
1 ➡ Run the duplicate checker in your database.
2 ➡ Create a plan to tackle those duplicates.
3 ➡ Tackle those duplicates, celebrate the success, and add this to your ongoing maintenance plan.
You’ve got this!
👋 See you next time,
Jenny
P.S. Here are the comments I gathered from team members across the development department, and I surprised the super users with a collage:
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