The 6 Most Common Data Mistakes Fundraisers Make—And How to Fix Them!

August 15, 2018

Over our three decades of work with clients, we have seen many mistakes that fundraisers make with their data that significantly hamper the accuracy and productivity of their work.

If you’re a fundraiser (or anyone who works with nonprofit data), take a look at the common mistakes we’ve noticed and our suggestions on how to fix them:


A frequent mistake we see with our clients is the use of “shadow databases.” By shadow databases, we mean places where individuals are storing data that are not accessible to the organization, such as employee’s Excel spreadsheets or Access databases.

When too many employees start tracking their own data, it’s far too easy to miss important information, which leads to a lack of transparency and inability to see the full picture.

You can only report on the data you have—so fundraisers must enter in ALL relevant data to the proper source system that is accessible across the organization.

How to fix it:

In short, keep all your data in one place!

First of all, decide on what source system will be the source of truth. And always be consistent about using that source of truth for your data reports, because if you put all the right data into the source system, you’ll get the right data out.

It’s also essential to have different types of data in one place for reference. In addition to the information you’ve collected about your donors and their transactions, we also highly recommend having additional campaign data, like budget goals, and data collected from a third-party, like demographic overlays, in the same system for clean reporting and analysis.

If you have multiple CRMs where you put different types of data (Raiser’s Edge for donor information and Galaxy for ticketing, for instance), and you want to report on both systems, it’s important to have one consistent place (such as a data warehouse) to store information.

A third-party system to store your data can be an essential tool for clean reporting and analysis when you have multiple types of data to keep track of in your organization. When you adopt a third-party system with extensive knowledge of your source systems, you know the data will be right.

When reports are written in-house, they will only reflect the accuracy and consistency of those report writers, and it’s possible to get inaccurate results. If you use Raiser’s Edge and Galaxy, we recommend Answers to bring them together.



This is another huge one—if you enter data incorrectly, you’ll never get the accurate results you need.

Some common issues to watch out for:

– Using open fields and free text fields for information that should be chosen from drop-down fields (we notice this most with the “notes” field!)

– Using inconsistent naming conventions

– Using special characters that skew your output

– Characters that you can’t see but are messing up your data, such as tabs, hidden spaces and carriage returns

– Employees doing their own calculations in an Excel spreadsheet (see mistake number one!)

How to fix it:

In order to fix the bad data entry, you must routinely define what the “bad data” is, and use a reporting tool to audit your data.

While a reporting tool won’t fix bad data, it will make it obvious when you haven’t put all the data in one place, or in the correct place. It can show you if everything has been coded correctly or if any critical information is missing. And, when you build a report correctly, it can show you where your data errors are, so that people, or automation, can fix them.

To do this, create exception reports and actively have a process to correct them in your source system and database.

A common mistake we see is that people will look at their report in Raiser’s Edge (or a system like SSRS or Answers) and see that the report is messed up and just think that the report is wrong. But if the report is wrong, it’s highly likely because the data in the source system is wrong. So it’s important to check your data in the source system first before reviewing how the report was built.



When your staff is not trained well, this can lead to many of the data errors listed above. When all team members are not on the same page, this leads to differences in data entry and reporting. Without having everyone following the same protocols, your organization’s data will be messy, insufficient, and perhaps even go against organizational policies for proper handling of data.

How to fix it:

At the base level, your staff should understand the fundamentals of the source system, how to use basic reports, and be familiar with the policies and procedures around data.

Build a consistent training program for your staff. Don’t just train the new employee once when they come in, meet periodically to get them up to speed throughout the onboarding process, and then have scheduled follow-ups throughout each year.

Set up quarterly trainings and other trainings as needed to ensure everyone is up to speed when something changes (such as version upgrades and modified policies).

It also helps to train your team on what reports Senior Staff review. Letting your team know what data they’re adding to the system that their superiors are referencing motivates them to enter correct, and up-to-date, data.



There are quite a few issues with relying on manual reports in Excel. First of all, there is a large possibility for error—approximately nine out of ten spreadsheets contain errors. Consider that even one small error in one cell can be compounded and lead to many more errors that completely damage your report.

Another reason you shouldn’t rely on Excel for your reports is that those spreadsheets are editable—when you put data into Excel, you can still edit it. Even if you use an export tool that pulls directly from a source system, and even if you tell people not to edit that report, editing is still possible and therefore there’s always that potential for an inaccurate report.

On top of this, manual reports are incredibly time-consuming, requiring multiple exports, formatting, and hours of review that end up costing your organization.

How to fix it:

The best way to avoid issues is to make sure your reports are un-editable… and since Excel is editable, it should NOT be used as a report. Instead, use a third-party system like SSRS, Crystal Reports, Tableau, built-in Raiser’s Edge reports, and/or a product such as Answers, and build that into the policies of your organization. (Another note: Because of Excel’s editable nature, auditors should not accept reports that are from Excel!)



Before asking your donors for money, it’s essential to do your homework. You need to know how often to ask and how much to ask for. You wouldn’t want to lose out on funds by not asking enough and definitely want to avoid making them feel uncomfortable by asking for far too much.

How to fix it:

We suggest creating a donor profile and setting up a “solicitation readiness” score that takes into account all the relevant information of each donor and is then scored by industry standards. You can score each of your donors by looking at data points such as how much they’ve given recently, when they gave last, and how long have they been in this stage. This determines how ready they are to give and to be solicited by your team—and how much your team should be asking for.



This is generally caused by resistance to change on the operational level. Sometimes old operational practices come from old systems that you’ve stopped using. But it can confuse people to leave certain practices in place that were set because of the original system since they won’t mean anything anymore in a different system.

For instance, if an organization went from WealthPoint to WealthEngine, they would need to clean up their data for reports and data entry. There will likely be constituent attributes that they stopped using or shouldn’t be using because it’s redundant.

When you notice something complex that doesn’t match best practices or that you haven’t seen before, it’s common to question, “Why are you doing it that way?”

If your answer is “because that’s the way we’ve always done it”, you need to pause and rethink.

How to fix it:

This mistake is evident in common errors and an over-complexity of your data. And the failure to consider changing operations to improve results is a big mistake.

To fix this, be open to doing things in a different way. When you move to a new system, re-assess your practices and be willing to change them for better results.

If you’ve changed systems, you’ve likely changed systems for a reason—it met a need that the one before it didn’t. If you’re trying to make the new system work like the old system, then you’re ruining the benefits of moving to the new system!

To convince your organization’s leadership, we recommend presenting a report of the issues the current operational practices are causing, such as the amount of incorrect data, hours it takes staff to correct these issues, and all costs associated with it. Then, present an alternative operation or procedure that would lead to improved results. If need be, suggest to run a trial with this new procedure and report back to them with how results were improved and any time and costs saved.

Looking for more nonprofit data insights? Download the Fundraiser’s Guide to Data Analysis.



If you need help identifying and fixing these errors, we’re here to help! Explore our Data Enrichment Services, Our CRM Data Conversion Services, or send us a message describing your struggle and we’ll work with you to find the right solution for your organization.