Why I love data dictionaries...and you should, too! | The Data Coach | #data consultant and educator

Transcript:

Lindsay: [00:00:00] Got our Oscar Piastri support t-shirt. We have our iced coffee. We're ready to go.

Hi there. My name is Lindsay. I'm The Data Coach, and today we are going to be talking about one of my favorite topics, data dictionaries. Yeah, I know. I know how nerdy that sounds, but just run with me on this.

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Okay. Back to our topic. I'm currently working on a data dictionary for one of my clients. They are a fabulous organization that provides mental health services to LGBTQ plus youth. And I love creating these [00:01:00] dictionaries, particularly the way that I do them, because they can solve so many problems that nonprofits tend to have with their data.

Today we're gonna cover three ways that having a data dictionary can be a huge asset for your organization. But first, what is a data dictionary? A data dictionary is a document that typically includes the following, the type of data that your nonprofit is collecting, often called data fields. Think names, phone numbers, interest, volunteers, donors.

The definition of each of those data fields. For instance, volunteer means whether a person is a current volunteer at your organization, current meaning volunteered within the past 12 months, the source of the data, like an online form or a feedback survey, and where that piece of data lives: in an Excel spreadsheet, in a [00:02:00] database, in an email marketing software, wherever.

Most nonprofits don't have a reference like this, but it can really do wonders to solve common data problems.

So first, if you have more than one person doing data entry, there is a risk that those different people may be interpreting some of your data fields in different ways.

Let's take that volunteer example. Maybe one staff member thinks that volunteer means someone who has volunteered in the past month and another person thinks that it means someone who has ever volunteered at any time in the 30 year history of the organization.

When it comes time to report on our volunteers, it's gonna be really difficult to figure out how many we actually have. If we're not all entering our data in the same way, it makes our data less trustworthy. But since a data dictionary has clear [00:03:00] definitions for all of the information we collect, it helps ensure that staff and volunteers will enter in information consistently and correctly.

Plus, it serves as an easy reference guide in case there's ever any confusion.

Next, data dictionaries are a great training tool. In my early career as an analyst, I didn't get a ton of formal training on the databases that I had to use. There were often no references, no guides, no policies, nine times out of 10 I would just stand over somebody's shoulder for an hour and watch them do data entry, and then I had to figure out the rest for myself.

For whatever reason, nonprofits often don't train people formally on their data systems, procedures or reporting schedules. It's a little bit learn as you go, and while sometimes that works, a lack of training can lead to data entry mistakes, making things up as we go along, and other bad data [00:04:00] habits that can lead to having suspect data.

But a data dictionary is something you can give a new staff member or volunteer and say, "Hey, take a look at this first, familiarize yourself with the terms," before we jump into this spreadsheet, this database, whatever. It gives people time to absorb the new information and ask questions before doing that deep dive into the data systems.

Finally, having a data dictionary gives our data a purpose, which can help us stand out to our funders and supporters. This goes back to what I said before about how I like to make data dictionaries. In addition to field names, definitions, and locations, I also add a column called use.

The use column explains why each data field is being collected and how we're going to use that data once we have it. A lot of times nonprofits collect certain data [00:05:00] because they think they're supposed to or because someone else told them they have to, but that data doesn't really do anything to help the organization track its progress on their mission, goals, program objectives, things like that.

However, by adding this use column, everyone who deals with the data will see exactly how the data is being used to improve programs, internal operations, and ensure the organization is on track to meet its mission. So let's go back to that volunteer example. How might we use this data? What would go in the use column?

Well, we can use it to determine if we need more volunteers. We can use it to assess the success of any recent volunteer outreach campaigns, or we can use it to catch trends in volunteer drop off to see if any follow up is needed. So now we know we're not collecting this information just to stick it in an annual report at the end of the year, right? We're actively using the [00:06:00] data to strengthen our programs and better engage with our community.

An added bonus if you have funders who are asking how your nonprofit is using its data, you already have your answers ready. In fact, you can even show them this data dictionary to demonstrate your organization's planning and forethought into creating and growing a data culture. Not a lot of organizations have something like this, so it can really help you stand out to your funders, especially as competition for funding gets more intense.

So does your nonprofit have a data dictionary or reference guide? How has it helped you? And if not, is this something you're interested in putting together for your organization?

Please let me know in the comments. I'd love to hear from you. Thanks again for watching and we'll see you next time. Bye.