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Data Quality for Nonprofits

Data Quality for Nonprofits

Poor data quality poses a threat to every organization, including nonprofits. When data is inaccurate, nonprofits can make bad business decisions, miss out on opportunities, and incur financial losses. Donors will lose trust when unclear data hides an organization’s accomplishments or obscures progress. Finally, marketing efforts based on poor data will fail to recapture previous donors or attract new donors.


What stops an organization from improving their data quality? It can be challenging to address issues with poor data quality, because…

  1. Data errors are hard to detect. There are no alarm bells when records become inaccurate, and often issues will not be identified until they are spotted by individuals with knowledge about the specific assets, information, or expected values.
  2. Data errors are expensive to check. It takes manpower and collaboration with outside sources to verify the accuracy of individual data records. This is not cost effective.
  3. Data is constantly changing. With current technology, data is being processed and collected so fast. Organizations do not see a need to address data that will be replaced as quickly as the corrections are made.
  4. Data errors are recurring. Organizations must invest time into identifying the cause of data quality issues, or they will be doomed to continue the cycle of bad data.

Before addressing concerns with data quality, it is important to understand how to identify high-quality data. Different nonprofits, and even different departments in a single nonprofit, need quality data for a variety of purposes. Regardless of purpose, all high-quality data should be:

  • Accurate: Data records should provide accurate information that is free from errors. (Ex. The donor’s phone number should be the accurate phone number.)
  • Valid: Data records should be formatted correctly. (Ex. The phone number should include ten digits without letters or mistyped characters.)
  • Complete: Data records should not have any missing required components. (Ex. The phone number for each donor record includes the area code and country code when applicable.)
  • Unique: Data records should not include duplicates within one event or entity. (Ex. The system displays each valid phone number once per entry.)
  • Consistent: Data records should be free from contradictions or errors. (Ex. Donor phone numbers include the correct area code that corresponds with their address.)
  • Timely: Data records should contain the most up-to-date information. (Ex. The donor’s phone number is their current phone number and not an out-of-date one.)
  • Auditable: Data records should always be easily traced, so that users know the source of the data. (Ex. The donor’s phone number was sourced from the donations database after last week’s donation.)


There are two approaches to data quality remediation for nonprofits.

  1. Reactivity. Address errors in data quality and the source of the error as they are discovered. While this will improve data quality over time, it will not avoid the initial damage to business outcomes.
  2. Proactivity. Before issues arise, develop data quality rules for the most critical data records for the nonprofit. Utilize a dashboard to evaluate records for violations of those rules and place them into a scorecard. Once rule breaks have exceeded a value of 15% of the record, identify and address the problems.

While the proactivity approach is the more effective choice long term, it necessitates more resources than any other approach. Only utilize this approach when the remediation cost would be less than the cost of the data quality issue. Additionally, prioritize only the most critical data in each entry, instead of focusing on all the data equally.

The Right Questions:

It is important for nonprofit leadership to understand the data quality of their organization. Start with asking these questions:

  • Overall, is your organization’s data trustworthy?
  • Before business analysis exercises, how much time does your staff invest in cleaning the data?
  • Do your managers understand what your data quality issues are and why those problems occur?
  • How have your managers addressed data quality issues in the past? Have they fixed the individual records, addressed the root of the issue, or both?

Asking these questions will allow for the proper allocation of resources to accurately remediate the source of data quality errors, and not just the individual issues.


Nonprofits cannot overlook how critical data quality is to their organization. High-quality data will give nonprofits the information they need to make accurate and timely decisions about their strategies and donors. When data quality is poor, it can lead to missed opportunities, compliance issues, lower fundraising outcomes, and distorted decision-making. It can also increase the risk of cyber-attack, fraud, or other business disruptions that prevent your organization from focusing on priorities. Nonprofits must develop and maintain data quality programs that focus on monitoring critical data records for problems and allow the organization to address the data quality issues at the source.

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