Preparing for Data Validation During Your Advancement CRM Implementation


Data validation checks the accuracy and quality of data before importing and processing during your advancement Constituent Relationship Management (CRM) implementation. Validating the accuracy, clarity, and data details are necessary to mitigate any project defects.


Without validation, you run the risk of using inaccurate data.


Additionally, having accurate data is an important step toward user adoption. If users cannot trust the data in the CRM, they may resist adoption of your new CRM. This is especially true if the promise of having accurate and reliable data is made as part of implementing a new CRM. Having reliable data is often a main motivation for doing a CRM project—and often one of the project goals.


Data validation is a form of data cleansing. It can be one of those things that can be a grueling process, but your institution must do it.


Here’s how you can prepare for data validation during your advancement CRM implementation.

Determine Your Data Validation Team

Having the right people as part of your CRM validation team is as important as conducting the data validation itself. Team members selected to validate data should be experienced in your new and old systems and keenly aware of the existing data.


When choosing who should be on your data validation team, engage users who are:


  • familiar with the data in your legacy system;
  • proficient in the new system and have participated in the CRM project;
  • expert navigators of your legacy system but also your new CRM system; and
  • known for their attention to detail.

Common Mistakes to Avoid

One of the most common mistakes organizations make during CRM data validation is assigning new staff members because they are available. Organizations may also try to hire temporary staff with the idea that getting more people to assist with the data validation is beneficial. In these scenarios, these individuals may essentially follow instructions, simply comparing the old system against the new. This will result in a limited ability to discern more nuanced issues, and more profound problems won’t be recognized because they are not familiar with the data.


Data validation is not a task for a newbie. Instead, the data validation team needs individuals with many years of institutional history and knowledge to effectively assess data and change the data quality.


Another common mistake organizations often make when it comes to data validation is they don’t define the effort. You could ultimately spend years on the tasks associated with data validation. Unfortunately, you don’t have that kind of time. Instead, define the goals and the amount of time your data validation team should have it completed.


For example, “X data needs reviewed and should be completed in two weeks.”


Institutions also forget to identify focus areas and assign tasks to specific team members.


Be explicit and intentional when identifying a focus and assigning tasks—only assigning the data validation to the folks who have willingly committed their time. Give detailed assignments with clear identifiers. This helps everyone fully understand how much work they need to achieve and guidelines, so they know when they’re “done.”


An example of identifying focus and assigning tasks includes, “we’re focusing on X area; here are your assignments that need to be completed in X weeks.”

How should we approach data validation?

There is a healthy amount of rigor to the process of data validation. “Looking” into the system is not appropriate data validation. Before beginning with your CRM implementation data validation, establish a set (or sets) of test data that can be validated during this process.


First, set a collection of test records—like board members, trustees, and major donors. Be intentional about the collection of data you’re validating.


Then, develop test cases or use scenarios. Outline what the user should be testing. Be very clear on what is considered success versus failure—it shouldn’t be a mystery to them. Your data validation team should have a deep understanding so they can look at the records and compare them. Once you’ve gathered all this information, assign a score of a pass, or fail—which should also be documented.


Finally, prioritize what you’re testing. At the outset, you might think you want all your data to be validated, but you usually don’t have enough people or time to do so. Instead, rank the data validation assignments in order of importance. This way if you run out of time to complete all your data validation, you’ve already completed your most important test cases.