3keys

Top 3 Keys to Long-Term Data Maintenance in The Raiser’s Edge

It may seem obvious, but data maintenance is such a crucial aspect of successful fundraising for so many reasons. Assuming that you HAVE already bought into that concept, I’d like to take this opportunity to share with you three of my top keys for maintaining your data over the long-term, and minimizing the need for constant database clean-up.

1. Consistency of Data Entry

I can’t stress this idea enough. Even if I put it in all capital letters, made it bold, and put stars around it, that still would not convey how critical consistency of data entry is to the health of any database.

If your data is not consistent, it is harder to extract. As an example, think about the places in Blackbaud’s The Raiser’s Edge 7 (RE) where you have to know exactly what you have in a field in order to query on it, or export it, or update it via Global Change. If the values aren’t all the same, and you are aware of this, then you may have to run the process in question multiple times. If you are not aware that your entries are inconsistent, you could miss records – either have them not show up in your query or export, or not get a needed change applied – and that outcome is far less than desirable!

Another often-overlooked side-effect of inconsistent data entry is how your organization presents itself to the outside world. The main example I think of here is address abbreviations. This is something I see a lot in my daily interactions with clients. Organizations will have a variety of data anomalies such as: Apt., Apt, Apt#, Unit, #, Suite, Ste, etc, on their constituent records.

The second example I can think of is titles and suffixes. Those two tables are often a mass of entries, with data entry staff often choosing the first applicable entry they come to, rather than the correct one, because they just need to get the data into the database. Omatic Software has a tool that can help you with these two tables – TableOmatic. It is worth your time to clean up these two tables, I promise.

If one donor sees the mailing to another donor, they could notice the anomalies in your addresses, and your titles and suffixes. I’m that type of donor. I look at the mailings my friends get from the same organizations I get mailings from, and I would definitely notice. The same thing could happen when you print reports that contain address information, for board members. They might wonder if this is what the mailing labels look like when they go out. A board member could leave the report where others can see it, or even share it with a third party with a vested interest in your organization (Or one you wish had a vested interest in your organization!). How do you want to appear to these people? Do you want to appear as though you run a streamlined, efficient, organization? Or do you want to appear as though it’s ‘anything goes’?

Inconsistent data also makes for a less automated process during an import via ImportOmatic (IOM). This is also a hurdle I see clients dealing with on a regular basis. IOM is very powerful, but it cannot overcome all data inconsistency issues. Given that one of the central tenets of the design of IOM is to avoid data duplication, you can see why data consistency would be important. I will refer back to the example of address abbreviation anomalies. The difference between St., St, and Street, can lead to IOM not being able to auto-pick constituent matches, and it can lead to undesired interaction with the Advanced Address Processing form.

When importing to RE via IOM, another great way to combat inconsistent data formatting is with the IOM Data and Address Service. This can really help to standardize your data and save you from some headaches down the line.

2. Documented and Enforced Data Entry Standards

This is how you achieve item #1.

Yes, it takes time to have the discussions about how your organization wants its data to appear.
Yes, it takes time to write up formal standards and distribute them to all data entry staff.
Yes, it takes time to enforce the documented standards.

In the long run, it is worth all of the time you invest in this part of the process.

Blackbaud provides lots of great information to get started with formalizing your organization’s standards and business rules. As an example, here is a detailed RE Constituent Data Entry Guide.

3. Regular Data Monitoring and Clean-up

The first time you go through your database after establishing the data entry standards, it could be a big project to clean up your existing data so it conforms to those standards. You may decide to draw a “line” in your data and only records that have donated in the past year (or two or three) will get cleaned up now. This is entirely reasonable, especially if your database is very large. If you choose to take this approach, I would suggest putting into place a policy that any time a record is touched, or a donor suddenly gives again after several years, that the record then needs to be updated to conform to the current data entry standards.

A policy of taking the time to clean up records any time they are touched is a good approach to the initial clean-up project. It helps make the project not seem quite so large, and engages all of the database users in the project. It is also an outstanding way to help keep your data consistent over time. Once your users get into the habit, they will do it automatically and it won’t seem like an extra step!

In addition to the initial clean-up project, and a policy of standardizing records as they are touched, you will need to routinely sample your data, and inspect it, to be sure the data entry standards are being maintained over time. Using query tools to gather together a random sampling of records, and then performing manual inspection of those records, is one of the best ways to accomplish this task. If you notice a particular standard is not being maintained, you can intervene by having a discussion with the responsible staff members. You may discover that a particular standard simply is not workable, in practice, due to the format of incoming data from your primary data source. In this case, the standard in question needs to be revised. It may be that you’ve had staff turnover and the standard did not get communicated to new employees. In this case, you will fill in the training gaps, and provide the new employees with the tools to do their jobs properly. Whatever the reason, the non-adherence to your organization’s documented data standards should be addressed early. Preferably before you have another large clean-up project on your hands.

Putting these items into place will take time, as will maintaining the standards, but it will pay off when your users are able to predictably pull the data they need, the first time around. It will pay off when your mailings all go out with consistent address information. It will pay off when outside parties see your reports, and they aren’t distracted by anomalous data. Think of it as just another step in keeping your database humming along smoothly!

Share this post