5 Common (Easily Preventable!) Mistakes Nonprofits Make with Data

By Sean Olds

Years ago I was introduced to an adage that has been largely attributed as being an old Chinese Proverb, indifferent to its origin the meaning is still sound. It says, “The best time to plant a tree was 30 years ago, the next best time is today.”

During Q&A sessions in speeches or panels I often get asked by #nonprofits when should we start collecting #data? I share that adage with them. We just went through Giving Tuesday and aside from end-of-year fundraising, this is where we see a lot of nonprofits make mistakes with their data.

This article highlights five of the biggest issues we see nonprofits encounter with their data.

Data is power — the more you know about your donor pool, the more effectively you can work with them to meet your fundraising goals.

Of course, collecting data … that can be a whole other story. Even in this age of unprecedented access to data, it can be hard to know exactly what you need to collect, what’s relevant and what’s not, and of course, what to do with it when you’ve got it.

To make things easier, we’re sharing the five most common mistakes we see nonprofits make with their data.

1. Messy data

Messy data is data that has duplicate entries, missing fields, or, often, both. This is really common among nonprofits, especially if you don’t have a coordinated data gathering and organization strategy. For instance, if you collect donor emails both from your website and from people who give via mail, you can easily end up with a duplicate entry for the same person.

2. Skinny data

Data’s called “skinny” when there’s just not that much dimensionality to it. Maybe you collect donors’ names and emails, but no more. That’s certainly better than nothing. But it’s way too skinny to be all that useful. Generally speaking, the more dimensions — aka types of information you gather — the better.

3. Short data

A short dataset is one where there’s not enough entries to be useful. Unlike the skinny dataset, where there’s not enough information about individual entries, with a short dataset there’s not enough entries, period. This is one of the easiest data issues to fix though — all you have to do is get more contact records! Easy right?

4. Badly labeled data

You might have a really robust data set, but if the data in it is inconsistently or incorrectly labeled, you’ve got problems. For instance, let’s say that you’re trying to use your data set to identify the people most likely to donate during this years’ funding drive. If you haven’t designated which of your contacts have actually donated before (that’s the label, btw), then you’ve got problems. Similarly, if you have multiple sets of labels that overlap, or you’ve got records inaccurately labeled, you’re really limited in what you can do with your data.

And finally, the 5th and — to us, most tragic — mistake … you’re not using your data!

The very best database in the world is useless if you don’t actually use it to make smart decisions about your fundraising. And yet, lots of nonprofits just don’t really use what they’ve got. They get overwhelmed, or they have other strategic priorities that take precedence, or they don’t know where to start, and so all that lovely data goes to waste.

Do you resonate with these mistakes? What will you do to prevent them? Let us know on the blog below, and please share this post with a colleague who may find it helpful.

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