Signing up for a new donor management platform is exciting. Getting all of your old donor data cleaned and ready so that you can move it into the new system? Not so much. While migrating donor data is one of the most important steps in implementing a new CRM, it’s also the part that’s most time-consuming, mind-numbing, and prone to error.
Here’s the bad news: This is not a step that can be avoided! Without a good plan in place, you’re going to spend your first month (or two) in the new system dealing with improperly formatted records that leave your nonprofit at a standstill.
Now for the goods news: We’re to help! In this article, we’ll cover how to create a donor data migration strategy with deep dives into data mapping, data cleaning, and testing. Sound exciting? No? Well, we’re starting anyway!
Build a Donor Data Migration Strategy in 10 Steps
Migrating your donor data into your new CRM sounds like a daunting task. While it’s going to require a lot of hard work and careful attention to detail (you’ll get way too familiar with proper USPS street address formatting), the actual strategy part of the equation is pretty simple.
So simple, in fact, that we’ve broken down the process of building a donor data migration strategy into 10 simple steps. Just follow these, and you’ll be ready to rock once your new system is up and running.
- Assess your needs: Conduct a thorough analysis of your current donor database’s shortcomings, challenges, and the goals you aim to achieve with the new system. Define specific metrics and outcomes you expect from the migration to guide your strategy.
- Form a migration team: Form a dedicated migration team involving fundraising professionals and data management specialists with clearly defined roles and responsibilities. You may also want to look for an IT professional; we’ll touch on that later on.
- Set a timeline and milestones: Develop a realistic timeline that breaks down the migration process into manageable phases. Set milestones and deadlines to keep the project on track and assess progress regularly.
- Build a data mapping plan: Understand your current data structure comprehensively, identifying the fields and attributes you use at your organization. Map each field from the old database to its corresponding field in the new system, accounting for data format differences and potential transformations.
- Perform data cleaning: Weed out redundancies, inaccuracies, and inconsistencies from your data. Develop a data cleaning plan to standardize formats, eliminate duplicates, validate information, and ensure data accuracy. This is a great time to clean up duplicates, fix typos, adjust salutations, etc.
- Do testing and validation: Perform test migrations with a subset of data to validate your data mapping and cleaning efforts. Identify discrepancies or errors and refine your strategy accordingly.
- Make a communication plan: Develop a communication strategy to keep everyone in your organization in the loop about the migration. Address concerns, provide updates, and ensure a transparent transition process.
- Prioritize training and adoption: Train your staff on the features, functionalities, and best practices of the new system. Foster internal adoption to guarantee efficient usage and accurate data entry.
- Implement security measures: Implement robust security protocols to protect donor data during the migration. Back up data regularly to prevent loss and ensure data integrity.
- Post-migration, evaluate your success: After migration, evaluate the success of the process against predefined goals. Gather feedback from staff and donors to identify areas for improvement.
An effective donor data migration strategy is the cornerstone of a successful transition between database systems. In the rest of this article we will discuss the heart of the data migration process—steps 4, 5, and 6—in greater detail.
What is Data Mapping?
A thorough data mapping plan is pivotal in the success of a nonprofit’s donor data migration strategy. Having a good plan in place will ensure accurate importation of data, fostering a seamless transition from one system to the other.
So what is a data mapping, exactly? Data mapping is a plan that outlines how information from one system corresponds to information in another during migration. It specifies which fields in the old system translate to fields in the new one, addressing format differences and transformations.
In other words, data mapping is the process that lets you take the data set in your old database and migrate it into the new one. Without data mapping, you’d pretty much have to move everything over by hand, and nobody’s got time for that, least of all your average nonprofit staffer.
How to Create a Data Mapping Plan
Creating a data mapping plan isn’t actually that complicated. You’ll simply create a document that matches each field from the old database to its equivalent in the new one. You’ll want to ensure accuracy by identifying matching fields, even if they have different names or structures.
This is the time to address any differences in data format or structure between the two systems.
In many cases, information that takes up one field in an older, less-advanced system will need to be translated into multiple fields in the new one.
Here are some examples of how data fields would need to be translated from one system into another:
| Old System | New System |
|---|---|
| “Full Name” (single field) | “First Name” and “Last Name” (separate fields) |
| “Email Address” | “Primary Email” |
| “Address” (single field) | “Street Address,” “City,” “State,” “ZIP Code” |
| “Level of Involvement” (coded as ‘L1’, ‘L2’, ‘L3’) | “Engagement Level” (‘Low’, ‘Medium’, ‘High’) |
| “Total Donations” | “Lifetime Giving” |
| “Designated Funds” (comma-separated list) | “Designation 1,” “Designation 2” (individual fields) |
Once you have your data mapping plan in place, you’ll want to make sure it’s documented, including field mappings, transformations, and validation rules. This serves as a reference point for your team during the migration.
Now you’re ready to move onto the next phase: Cleaning your data so that it’s ready to migrate.
Cleaning Your Donor Data
Amid the intricacies of donor data migration, an often underestimated yet crucial step is data cleaning. This process involves identifying and rectifying discrepancies, duplicates, and inaccuracies within your donor data.
A well-executed data cleaning plan significantly enhances the success of your migration strategy. Here’s a detailed guide on creating and executing such a plan:
Assess, Document, and Set Goals
Begin by comprehensively assessing your current donor data. Identify duplicates, outdated records, incomplete entries, and inconsistencies. Document these issues to guide your cleaning process and prioritize the data elements that require attention.
So what should your priorities be? Focus on fields crucial for donor communication, transaction history, and engagement levels. Tackle essential data points before addressing less critical ones.
Set clear, measurable goals for your data cleaning efforts. Are you aiming to eliminate duplicates, standardize formats, update contact information, or correct errors? When should all data be ready for migration? Clearly defining your goals to streamline your cleaning approach.
Standardize and De-Duplicate
Ensure consistency by standardizing data formats. Uniformity in entries like addresses, phone numbers, and dates enhances accuracy and readability. Plus, if you have any automated functions in your CRM (like tokens that place names and titles into your emails), that uniformity will prevent any errors or embarrassing slip-ups.
Duplicate accounts are one of the most common data challenges. You could end up with duplicate records due to any number of factors: Manual data entry errors, a sloppy data import or consolidation from multiple sources, inconsistent data formats, and different variations of names or contact details.
During your data cleaning process, one of your top goals should be to identify and merge duplicate records, preserving relevant information while eliminating clutter. Your donors will thank you, especially that one guy who no longer gets three seperate emails addressed to “Stephen,” “Steve,” and “Steph.”
Validate, Update, and Enrich Information
Validate contact details, such as emails and phone numbers. “Validation” is really just a technical data management term for “make sure that the info you have is correct.”
Update outdated or incorrect information using reliable sources. A good place to start would be looking at your email bounces and returned mailings to see if any email or physical addresses are out of date.
You may also consider using something like a National Change of Address (NCOA) update service, especially if you have lots of addresses to review.
While cleaning, consider enriching your data. Append missing information, such as social media profiles or demographic data, to enhance donor insights. The more data you have on your donors, the easier it is to analyze trends and segment your communications.
Use Some (Free) Data Cleansing Tools
Cleaning data is careful work that’s going to require long hours, but you can utilize data cleansing tools or software to automate the process. These tools can help identify and correct duplicates, standardize formats, validate information, and enhance data accuracy during a data migration.
If your nonprofit’s old database is in a spreadsheet, you can use Google Sheets add-ons (such as Remove Duplicates) or Microsoft Excel’s data cleaning functions. OpenRefine is a free, open-source tool that you can download and put to work cleaning up your data.
Test a Bunch, Migrate Once
The last thing you want to have happen during a data migration is a necessary re-do of the migration itself. That’s why you should be implementing data validation rules and testing them before the migration occurs to catch any problems
Run a Test Migration
Before your nonprofit commits to a full migration, it’s a good idea to conduct a test migration (or two, or three) to ensure a smooth transition of donor data. In this dry run, a subset of data is migrated to identify potential challenges or errors.
Once this sample data is loaded into a new system, you can comb through the data to ensure accuracy, formatting, and relationships in the new database.This is called “sample testing.”
In addition to sample testing, two other tests you can run on your dry-run data set are:
- Query testing: Run database queries to retrieve specific data and ensure accurate results.
- Functional testing: Test key functions like creating reports, sending emails, and generating donor lists.
These tests will help you fine-tune the migration process, minimizing issues during the actual migration.
Implement Validation Rules
Validation rules are formulas that you incorporate into your system to ensure that new data entered conforms to set standards and prevent incorrect or incomplete data from being added. For example, a validation rule might flag incorrectly formatted email addresses, ensuring that only correctly formatted addresses are transferred to the new database.
Have you ever bought something online, entered your shipping address, and encountered a pop-up message that asks you to confirm that address but with slightly different formatting? That’s data validation.
Remember earlier in this article where we mentioned that you should have an IT or data systems professional on-hand to help with your data migration? Well, implementing validation rules is one area where that advice really holds true; they’re often uniquely qualified to help with that process.
At the very, very least, you should have someone who’s very comfortable with Googling and searching for the correct info.
After the Migration, Test Again
Once your data has been imported, test your data subsets again in your new database environment. These tests will help you validate that the data migration and any new integrations proceed smoothly.
In the prior section on test migrations, we mentioned sample testing, query testing, and function testing. Those are all tests that you should run post-migration as well! Here are additional methods you can use to test your new datasets:
- Integration testing: Ensure integrations with your other systems work seamlessly.
- User acceptance testing: Involve staff to ensure data supports daily tasks and meets their needs.
- Validation checks: Employ automated scripts to validate data against predefined rules.
- Comparative analysis: Compare data in the new database against the original to identify discrepancies.
Hopefully, you’ll have followed all the steps laid out in this article, and your post-migration tests will show a squeaky clean set of donor records.
How Data Migration Works in Neon CRM
If you’ve reached the end of this article and still feel that migrating your donor data into a new system sounds daunting, we totally understand! That’s why we’ve included a dedication migration support function into our onboarding process for Neon CRM.
For a one-time fee (starting at $300), our Professional Services team works with new Neon CRM clients to audit, clean, and re-format their donor data prior to import. While it means slightly higher upfront costs, we’ve found that organizations really reap the benefits in the long term. We can also help guide you while you clean and import your own data, if you’d prefer!
Do you want to know more about what it would look like for your nonprofit to switch to Neon CRM? We do too! Just click the button below and request a free demo to see all the system can do (spoiler: it’s a lot). We look forward to hearing from you!
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