When nonprofit leaders tell me their data isn't working, the conversation usually goes one of two ways.
Either: "The data is wrong. We have duplicates everywhere, the numbers don't add up, and nobody trusts what's in the system."
Or: "The data is probably fine, but nobody looks at it. We collect everything and use nothing."
These are different problems. They have different causes, different fixes, and different consequences if you mix them up.
The quality problem
A data quality problem means the data you have is inaccurate, inconsistent, or incomplete. Records have duplicates. Fields are formatted differently across staff and sites. Key information is missing. Numbers don't match when you pull from different systems.
The symptoms: staff spend time reconciling reports before they go out, leadership qualifies every number with a caveat, and nobody quite trusts what the database says.
The cause is almost always one of three things: no written entry standards, no one person responsible for data quality, or data that was never cleaned after being migrated from an older system.
The fix is a cleanup and a governance structure. Deduplicate the records, write down how things should be entered, assign an owner. It takes time and someone has to decide it matters enough to do.
The use problem
A data use problem means the data is probably fine, but it doesn't inform decisions.
Staff collect program participation data because the grant requires it. Finance tracks expenses in QuickBooks. The CRM has a reasonably clean donor list. But when leadership makes decisions about programs, fundraising strategy, or resource allocation, they go on instinct. Nobody looks at the data first. Nobody asks what the data says.
The symptoms: reports get produced and filed, not read. Board presentations are built from scratch each time rather than pulled from a live system. Data exists in the organization but not in the room where decisions happen.
The cause is usually one of two things: there's no regular reporting routine that puts data in front of leadership, or the data has let people down enough times in the past that they stopped trusting it, even after the quality improved.
The fix is a reporting routine and buy-in. A consistent cadence of pulling and reviewing the same metrics. Leadership committing to asking "what does the data say?" before making program and fundraising choices.
Why mixing them up is costly
| Quality problem | Use problem | |
|---|---|---|
| What it looks like | Duplicates, mismatched numbers, missing fields | Data exists but decisions happen without it |
| Root cause | No entry standards, no data owner, bad migration | No reporting routine, eroded trust in data |
| The fix | Cleanup, written standards, assign ownership | Reporting cadence, leadership buy-in |
| Wrong fix applied | Building dashboards on dirty data | Cleaning data nobody will look at |
The most common mistake: applying a quality fix to a use problem, or vice versa.
An organization that doesn't look at its data decides the problem is that reports aren't visual enough. They invest in a dashboard platform. Leadership still doesn't look at the data. The problem was never the format.
An organization with genuinely dirty data decides the problem is that nobody trusts it. They work on building trust and communication. The data stays dirty. The trust problem doesn't resolve because the underlying numbers are still unreliable.
Getting the diagnosis right first saves a significant amount of time, money, and frustration.
Most organizations have both
A quality problem that goes unaddressed long enough usually creates a use problem: leadership stops relying on data because the data has let them down. A use problem that persists long enough usually creates a quality problem: without anyone reviewing the data regularly, entry standards drift and errors accumulate unchecked.
If you're not sure which you're dealing with, start by asking two questions. Can you pull a reliable donor retention number right now, from a single source, without reconciling? And when was the last time that number influenced a decision in your organization?
The first question diagnoses quality. The second diagnoses use.
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Joshua Barillas is the founder of Prismatic Consulting, a data services firm built exclusively for nonprofits. Learn more about our services or get in touch at hello@prismaticconsulting.us.