Fundraising

If your organization is like most, many people enter data into your fundraising systems. Accounting, finance, marketing, operations, and program leaders all add names, addresses, and contact information to the system.

Unfortunately, over time, people make mistakes. They fail to follow consistent naming and abbreviation conventions, for example, which leads to Susan Smith at 123 State Street being entered into the system as S. Smith, Sue Smith, and Susan Smith—and her address as 123 State St. and 123 State Street. In this simple example, Susan could have multiple listings in your database. That can lead to costly wasted contacts, especially if you use direct mail alongside digital marketing for your fundraising efforts.

Add AI into the mix, and you can see the problems compounding in missed opportunities, exhausted donor lists, and inaccurate reports.

Data Hygiene Is Everyone’s Job

Smart nonprofits don’t rely on a single person to ensure they have clean data. They treat data hygiene as everyone’s job. The best way to get started if your data is, well, a bit dusty, is to form a team to work on the problem. That doesn’t mean the team has to actually do the data cleansing, just help organize it. There are companies that can help you update and clean data (we’ll get to that in a bit).

Leadership should organize a team or committee with representatives from each department to begin the process of consolidating and cleaning the organization’s data. This team gets the ball rolling. Once the data cleaning is underway, a smaller subsection of the team can continue to monitor, manage, and maintain the data standards for the organization. It’s kind of like doing a deep cleaning or spring cleaning and then weekly dusting. The initial work of the committee will be deep cleaning, and the smaller subset continues with the data version of dusting and maintenance.

The Data Dictionary Is Your Friend

A data dictionary is an invaluable tool. It’s a set of standards or guidelines that tells anyone entering data what should be collected and how it should be entered into the system. The data team or a subset of the larger team can create the organization’s data dictionary.

The data dictionary, for example, would indicate whether Susan Smith’s address should be entered as “123 State St” or “123 State Street.”  While such decisions may seem trivial, imagine that Susan Smith is entered twice into the database under “State St.” and “State Street.” Now, multiply that by 10, 100 times, and you’ll see how quickly a small, seemingly inconsequential mistake can add up to many hundreds of errors in a database.

Once decisions have been made for each major piece of data the organization collects, the data dictionary can guide both new data entry and cleaning up old data.

Inventory Your Existing Databases and Spreadsheets

Next, to fully clean old data, you need to know what you are working with. This means taking an inventory and collecting all potential data sources in your organization. At first, you may assume that data only exists in recognized organization wide databases, such as the marketing department’s fundraising database or the accounting department’s system. However, the data team should go back to their respective departments and ask each individual for any data they collect. You may be shocked at how many people maintain their own spreadsheets apart from the company’s official databases. These spreadsheets may be rife with errors, outdated information, or duplicate records.

When taking an inventory of data sources, ask each person:

1. What do you use this database or spreadsheet for?
2. Who adds new information?
3. Who updates it when something needs to be corrected, like a new email address?
4. When was the last time you used it?

This exercise can be eye-opening on many levels. For example, one nonprofit found that several departments kept their own contact spreadsheets because they didn’t trust the information obtained by marketing. They felt that their spreadsheets were more accurate than the lists marketing acquired from a major list vendor! It took a while, but they were able to convince the department to give up their separate spreadsheet and use the main fundraising database once they proved that the contacts were indeed in the main database.

The Costs of Outdated Data, and the Benefits of Cleaning It

Many organizations have never conducted a data hygiene or cleaning effort, and if your organization is one of them, don’t despair. Even the messiest, oldest database can be fixed.

If it’s been a while since your database was cleaned, you may want to find a third-party company that performs list hygiene or database hygiene work. Such work usually takes several steps.

First, the company will ask for all the data sources to be cleaned. Next, they will ask for your data dictionary or help you create one. The company will move all data sources into a standard format and compare their information to your database to remove old, outdated information. For example, if you rely on direct mail for fundraising, data companies can compare your data with published lists from the postal service of mail that has been forwarded or address changes. The National Change of Address (NCOA) system enables them to quickly update the list for you. They can also access lists of deceased people and remove them from your donor lists.

Good list scrubbing also includes flagging those pesky potential duplicates and sending them back to the team to manually check them. The company should flag all the possible permutations of Susan Smith (using our example) and send them to a human being to check. Then, your team or a member of your team can indicate which one should be used, and you can retire the duplicates.

Is it worth scrubbing a list clean like this using an outside company? One nonprofit conducted this exercise and saved over $100,000 on postage, printing, and mailing costs by removing duplicate records. They were so pleased with the cost savings, they used a fraction of that amount each quarter moving forward to continually update and cleanse their data, helping to stop unnecessary expenses.

AI Requires Clean Data

As more and more fundraising systems incorporate AI into their platforms, nonprofits are finding that the old adage, “Garbage in, garbage out,” is very true. AI inputs rely upon clean data. If the data is stale, outdated, duplicative, or incorrect, you’ll waste money, time, and effort. AI accelerates marketing, the good and the bad.

But just as garbage in, garbage out holds true, the converse is equally true: good in, good out. When you clean your data, AI can then use it efficiently, improving fundraising through personalization, customization, and better reports.

It all starts with clean data and data hygiene best practices. If you need help getting started, or you feel overwhelmed by the number of data sources that need to be fixed, we can help guide you in the right direction to get started.

Welter Consulting

Welter Consulting bridges people and technology together for effective solutions for nonprofit organizations. We offer software and services that can help you with your accounting needs. Please contact us for more information.