Data quality is a critical factor for organizations of all sizes, and nonprofits are no exception. Poor data quality can lead to inaccurate business decisions, missed opportunities and even financial losses. Further, poor data quality can impact contributions negatively in several ways. It can obfuscate the nonprofit’s achievements year after year, which can erode donors’ trust or describe fewer accomplishments to its contributors. Poor data quality can lead to marketing campaigns that fail to appeal to first-time donors or are insufficient to recapture previous donors.
Why Data Quality is a Challenge
If data quality is a pervasive issue with real consequences, why have most organizations not solved it? This is the case because assessing and remediating data quality is fraught with challenges, such as:
- Data is an intangible asset and, unlike other assets, does not give detectable signals, such as changing color, giving off smoke or changing smell. In fact, a single erroneous record normally can only be detected when a knowledgeable individual notices the value is not correct in the data.
- It is not cost effective to confirm the accuracy of every data record. It requires real-world observations or corroboration by another source. These are expensive endeavors.
- So much data is collected and processed so quickly now that bad records are not perceived as worth the effort to correct, as they will just be replaced soon.
- Unless the root cause of data quality issues is discovered and resolved, organizations will continue to admit poor data quality into their systems.
Another challenge to data quality is defining what it means for data to be fit for purpose. That definition can change not only across different nonprofits, but within a single nonprofit’s departments as well. In general, high-quality data tends to be defined as:
Accurate. A data record presents what is found in reality without distortion (e.g., the ZIP code is the correct ZIP code for a donor).
Valid. The data values follow the correct format (e.g., a U.S.-based ZIP code has five digits with no letters or special characters).
Complete. A data record has no missing values where values are mandatory (e.g., a ZIP code is present for all donor address records).
Unique. There are no duplicate data records for the same entity or event (e.g., the list of valid ZIP codes in a system presents each ZIP code only once per entity).
Consistent. There are no contradictions within a data record or across data records (e.g., a ZIP code is the correct ZIP code for the city and state in a donor record).
Timely. The data record represents the most current known information (e.g., the ZIP code in a donor record exists for the donor’s current address, not the prior one).
Auditable. The parentage of the record can be traced so that the user knows whence the value was derived (e.g., the donor’s ZIP code was pulled from the contributions database after a donation was made last week).
What Can Be Done
Nonprofits can take one of three stances with regard to data quality:
Do nothing. Consider poor data quality a cost of doing business and accept the inherent risk.
Reactive remediation. When data quality problems are discovered—often too late to prevent a damaging business outcome—fix the problem and the class of problems it represents. Over time, data quality will improve.
Proactive remediation. Pick the data records that are most critical to the nonprofit, usually meaning they are used by more than one department more than once. Define the data quality rules for those data records. Codify those rules into a dashboard that searches for data record violations and aggregates them into a scorecard. When a rule breaks a threshold value —say 15% or more data records have missing values —take action and fix that class of problems.
The proactive remediation approach requires resources and should be taken if the perceived cost of data quality issues is greater than its remediation. In this vein, the approach should not treat all data as equal, but instead consider only the critical data of the entity.
What Questions to Ask
Nonprofits’ leadership should find out what they can about their data quality. Some questions leaders should ask include:
- Is the nonprofit’s data considered trustworthy overall?
- How much time do staff spend cleaning data in preparation for a business analysis exercise?
- No organization has perfect data. Do the managers know where the data quality problems lie? Do they know why the problems occur?
What steps have the managers taken to detect poor data quality? When found, do the managers fix the data record, fix the problem at the source or both?
The answers to these questions may suggest that leadership devote resources toward not only the assessment and remediation of data quality issues, but in identifying the root cause of those issues and remediating them as well.
Data quality is a critical component of good governance and effective oversight. Nonprofits need accurate and timely information to make informed decisions about their donors and strategy. Poor data quality can distort decision-making, lead to missed opportunities, lower fundraising outcomes and even cause compliance issues. Data quality is also important for risk management, as poor data quality can increase the risk of fraud and cyberattacks and create other business disruptions. Nonprofits should support data quality programs that identify the most critical data records, monitor those records for problems and address problems at the source when they occur.