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Data Integrity: What can organizations do to maintain their data more effectively

by: Heather Todd & Sara Swiatlowski

Critical business decisions rely on accurate, reliable, and timely data. Data can become out of date quickly, just as quickly as it is entered into your database. In the United States annually:

  • 30% of email users change their email address one or more times (Source: HubSpot)

  • 1 in 7 people change their postal addresses (Source: USPS)

  • 18% of all telephone numbers change (Source: Gallup)

  • 45% of people change job titles within their organizations (Source: US Bureau of Labor Statistics)

Data integrity refers to the accuracy and consistency of data over its lifecycle. Data integrity is a term to understand the health and maintenance of database information. Data integrity can refer either to a state or a process. Data integrity as a state defines a data set that is both valid and accurate. Data integrity as a process describes measures used to ensure the validity and accuracy of a data set or all data contained in a database or other construct. For instance, validation methods may be referred to as data integrity processes.

Inaccurate and inconsistent data can lead to lost revenue, poor decision making, missed opportunities, business in-efficiencies, just to name a few consequences.

Below are some best practices and tips to maintain your Blackbaud CRM™ data integrity:


  • Frequency

The frequency interval for de-duplication may vary depending on the size of your organization and how much data you have coming into your database on a regular basis. As you set up different types of merge possibilities you may find that some processes may need to run more frequently where others can happen less often. It is important to look at your complete picture of de-duplication process to develop a strategy that works best for your organization.

  • Merge on email

Most organizations look to de-duplicate their constituents using the basic name + address matching logic. Setting this up and running it on a regular basis is the first line of duplicate matching defense. In the digital age it is important to look for duplicates with email addresses in mind too. Since many platforms only require a name + email address to accept a donation, if you are only matching on a mailing address you may be missing out on many potential duplicates in your database.

Setting up a match on the exact name (100%) match + email address will help you to match constituents where you have partial information to those where you may have more complete information. Building on that using a search on a ‘fuzzy’ name + email address match will help you to match where the name isn’t a perfect match. This type of matching is useful for finding constituents where a name had changed through marriage or where a shortened name may have been used in place of a formal name. Within BBCRM, both of these email matching options will require a Global Change customization to help identify the pairs to be matched and this may be time well spent to keep your constituent data in order.

Data Audits

  • Leverage Blackbaud CRM™user-defined data lists to create constituent data audits for address, contact, name, and other data anomalies and updates.

  • Attribute Cleanup

    • Attributes can easily become the database junk drawer, so to speak. They’re easily created, easily added, and are often forgotten.

BrightVine has developed a Blackbaud CRM™ Attribute Cleanup Giveaway, a customized global change that can be run to delete attributes and their values so orphaned values don't remain in the database.

Data enrichment through the BrightVine Data Link

Data enrichment is not a “set it and forget it” action, you do once and then never do again. Constituent data, no matter how detailed, is fundamentally a snapshot in time. Jobs change, marital status may change, and email and physical address can alter. Even names may change, especially if there is a change in one’s marital status. Given the possibility of all these changes, data enrichment processes need to run on a continuous basis.

The two most common types of data enrichment are demographic and geographic. Both types of data enrichment can be processed through the BrightVine Data Link.

Demographic data enrichment involves acquiring new demographic data, such as marital status and employment updates, and adding that into an existing customer dataset.

Geographic data enrichment involves adding postal data or latitude and longitude to an existing dataset that includes customer addresses. This may include NCOA or another geographic data set.

For any data enrichment data set being imported into Blackbaud CRM™, there must be a common unique field identifier to link the two datasets. For constituents, this unique identifier could be a first name and last name, a mailing address, an email address, or constituent lookup id, or an alternative lookup id (if the id’s were exported to a third party). Without a unique identifier, the original dataset will not be enriched because there’s nothing to show that the two datasets refer to the same constituents.

Data compliance

Data compliance refers to any regulations that organizations must follow in order to ensure the sensitive data such as personally identifiable information and financial details, are guarded against loss, theft and misuse.

These rules come in a number of forms. They may be industry standards, state or federal-level laws, or even multinational regulations such a the EU’s General Data Protection Regulation (GDPR). Such rules and regulations will state what types of data need to be protected, what processes will be considered acceptable under the legislation, and what the penalties will be for firms that fail to follow the rules.


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