Data Health Best Practices for Advanced Analytics and AI Readiness

Data Health Best Practices for Advanced Analytics and AI Readiness

Your data is so valuable that nearly every website you access asks you to set permissions for how it’s collected and sold. Data is precious. Clean, consistent data is the foundation for better analytics, and it’s essential for reliable AI-assisted work. IBM reports poor data quality costs organizations $5 million a year, with 7% reporting losses of $25 million or more.

This tip sheet will help you create a plan so you can keep bad data from spoiling your use of predictive AI tools and automation. Sound daunting? Tackling data health is more doable than you might think—especially when we break it down into a few manageable steps.

1. Audit your data before you automate anything.

A quick annual audit (yes, every year) helps you spot the issues that undermine reporting, segmentation, automation, and AI outputs. Poor data leads to wasted staff time, weaker decision-making, and missed opportunities. AI hallucinations are real, but if your data is poorly coded, your tool might not be hallucinating; it could just be telling you it’s time to fix your data.

• Review duplicates, missing key fields, outdated contact records, and inconsistent codes.
• Create a simple scorecard so you can track progress over time, such as your “bad data costs” and “good data savings.”
• Prioritize the issues that affect fundraising, reporting, and donor experience most.

2. Standardize names, addresses, and core fields.

AI and analytics depend on patterns they can interpret consistently. If the same type of information is stored in multiple formats, your team will spend more time cleaning data and less time using it.

• Break names into distinct fields instead of storing them as one free-text value; opt for table types other than text whenever possible (free text fields tend to go rogue).
• Use consistent naming conventions for salutations, phone types, addresses, campaigns, and constituent codes.
• Check that your code tables use clear names and a reasonable number of distinct entries, so your data stays usable for reporting, automation, and outreach.

3. Keep addresses current to reduce waste and improve match rates.

Mail hygiene still matters. USPS guidance requires CASS-certified processing for automation-priced mail, and NCOA processing should be completed close to major mailings to support deliverability and postal discounts. Note: Addresses that fail certification will not be delivered.

• Standardize addresses at entry or in a regular monthly process.
• Run NCOA before major mailings and confirm updated addresses are written back to your database.
• Suppress undeliverable records from campaigns until they are reviewed.

4. Fix duplicates and confusing codes before they distort your reporting.

Duplicate records and messy code tables lead to inaccurate counts, fragmented donor histories, and awkward donor experiences. Clean structures make segmentation, automation, and AI recommendations far more dependable.

• Run regular duplicate checks and establish clear merge rules.
• Consolidate overlapping code values such as multiple versions of “home phone” or “active donor.”
• Review whether each code or field still serves a reporting or fundraising purpose.
• Automate what can be automated! Setting defaults in a batch is a simple step that can improve your overall data health.

5. Focus first on the data that directly supports fundraising.

Not every field deserves equal attention. Strong fundraising data usually starts with the basics: complete contact information, accurate giving history, and a few meaningful details that improve segmentation and personalization.

• Prioritize email, phone, mailing address, giving history, and preferred contact fields.
• Add enrichment fields only if your team will actually use them for cultivation or analysis.
• Append data that aligns with your fundraising strategy, not just what data is available to buy. For instance, birthdate might be pertinent if you see widely different responses from Gen Z vs. millennials vs. Gen X.

6. Set a realistic update schedule tied to your campaigns.

Data health improves when it becomes routine instead of reactive. Timing updates around appeals, events, phone campaigns, or major imports helps your team work more efficiently and catch problems before they spread. Consider making an “event” out of the cleaning process to recruit users to the verification process—data health happy hour, anyone?

• Schedule recurring reviews for addresses, duplicates, deceased records, and missing fields.
• Time append or cleanup work ahead of campaigns, when the payoff will be highest.
• Build in a human review step, even when automation is doing the heavy lifting.

For organizations using Blackbaud Raiser’s Edge NXT, the Data Health Agent can help identify potential data issues and prioritize recommendations for your team to review.

7. Document the rules so data quality becomes a team habit.

Good data management is an operational discipline. Strong governance, shared standards, and clear ownership are essential if your organization wants trustworthy insights from AI.

• Create simple standards for required fields, naming conventions, and update responsibilities.
• Document recurring processes so they do not live only in one person’s head.
• Share the schedule and standards broadly so staff members understand how their data entry affects reporting, fundraising, and AI outputs.

Before your organization can expect more from analytics or AI, your data must be clear, current, and consistent. Start with a few repeatable habits, document them well, and build from there.