July 7, 2020 | Read Time: 6 minutes
In this week’s edition of Ask an Expert, we answer reader questions about cleaning up a donor database.
Consultant T. Clay Buck, founder of Tactical Fundraising Solutions, and Robert Sanchez, vice president for corporate alliances at Miracle Flights — who is currently overseeing the group’s data cleanup and transition to a new database — provide the answers.
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Do you recommend cleaning up before, during, or after you transition to a new database?
— Vice president for development at an after-school enrichment program
Cleaning your data should be a part of the comprehensive plan to migrate to a new constituent relationship management system, or CRM, Buck says. It might be tempting to wait to tackle the cleanup until you’re in the new system, but that should be avoided, he says. “You don’t want to migrate and keep the same bad data and issues that you have in your current system.”
Think of migrating to a new system like you would moving to a new house, he says. “You likely don’t want to take old, worn-out stuff with you to your new home, and the same is true of your data.”
The main things to pay attention to are merging and purging duplicate entries, cleaning up inconsistent data entry, standardizing the process for entering data, and getting rid of incomplete donor records for which you have no relevant details. CRM providers should build data cleanup into the migration plan. Talk with them about the services they offer to mitigate these issues.
Sanchez agrees that the more cleanup you can do before the transition, the better off your organization will be when you go live with the new system. Still, he says, no conversion from one system to the next will be perfect. There will undoubtedly be some extra cleanup required when you’re live, but doing as much as you can before the migration will save your organization time, money, and stress.
Once your new CRM is online, says Buck, you’ll be so busy learning how to use it and revamping your processes that you probably won’t have time for a big data cleanup effort.
What measures can we implement now to prevent moving bad data habits to a new database?
— Major gift officer at an international humanitarian organization
The first step is to identify what is causing the data problems, says Buck. “Usually systemic data issues are caused by a flaw somewhere in the system by which data comes into the CRM,” he says. Ineffective or inconsistent data entry might be the culprit. That may be the result of a lack of standards or consistency in how gifts, names, or other information are entered into the database.
Standard operating procedures are critical to maintaining clean and healthy databases, Sanchez says. Formalize your procedures, have leaders stress that they are mandatory, and create a culture of positive feedback when employees follow the procedures properly.
Buck suggests creating a data–entry manual and a data–standards manual that document how your organization manages data and enters gifts. For example, does the organization prefer to address people with a formal salutation (Dear Mr. and Mrs. Smith, for example) or is it more informal (Dear Jack and Jill)? You may also need to create policies for consistency on gift entry, such as how you credit gifts that are part of pledges or link employee records to employers who match their gifts. While creating these standards is time consuming, this exercise yields huge dividends in ensuring you have clean, usable, accessible data, Buck says. It also helps strengthen your relationships with colleagues in departments like finance and marketing who may rely on the data as well. “Data is an asset, and it needs to be protected as such,” Buck says.
A data-usage and entry manual will help ensure that any staff members who enter data or have access to it adhere to standards and know they will be held accountable for following the procedures. At the same time, it’s important to make sure everyone is patient and flexible when implementing new data-entry procedures, Sanchez says. “It always takes some time and consistent corrective action for everyone to be on the same page.”
Any recommendations for smaller organizations where one or two people are taking on the task of data cleanup?
— Development associate at an HIV/AIDS advocacy organization
“How do you eat an elephant? One bite at a time,” Buck says. The most likely first step is to run a database audit to determine what the problems are. For example, how many duplicate records, incomplete addresses, missing emails, missing phone numbers, or other issues do you have?
Then determine your priorities and what you can handle in chunks, he says. It’s important to look at the project as a whole, but then manage it through small, easily accomplished tasks. For example, you may want to tackle your board and big-donor lists first as they’re typically smaller and you can clean and edit them with relative ease. Or you may want to approach your broader donor lists first so that you have access to those broader lists more quickly when needed.
The key is to create a work plan with deliverables and due dates with clear assignments as to who is responsible for what and when, he says. “Data cleanup is a big, important project,” Buck says. “It’s not going to happen overnight so do what you can with as many resources as you can.”
You may want to set aside a certain amount of time per week or per month to work on data and hold it sacred so that nothing can interrupt it.
Sanchez’s organization is in the same boat, with a small team working on a data cleanup ahead of moving to a new database. Consider the power of trained and trusted volunteers, he says. “They can help multiply your efforts when it comes to the potential manual cleanup you may have in front of you.”
He recommends just diving in and keeping your eye on the end goal of a clean database to stay motivated. “That will enable you to engage with your supporters in a more impactful way, thus raising more money to accomplish your mission.”
It’s a major endeavor, but it will be so worth it when you have it all done, Buck says. But remember, he says, clean data isn’t a one-time thing. “Once you clean the data, make sure you have a plan to keep it clean!”
Catch up on recent editions of the column.
