Business Bites: Data Dictionary

Welcome to Business Bites. My name is Sara. This is the podcast for busy professionals who want the quick hits of business terminology, historical context, and strategies for integration. This week we're going to be talking about a data dictionary. A data dictionary is one of the most critical components when you're thinking about data sets, data tables, and organizing all of the information. The term data dictionary was first coined in the early 2000s when we were thinking about different standards we wanted to set for data initiatives. Now, this doesn't mean that we weren't using data dictionaries earlier. It just means that we've really focused on being clear about what a data dictionary was after that point.

These dictionaries can be used for a broad spectrum of different use cases, whether we're thinking about structuring our data, documenting it, but in the end, it is around focusing and being really explicit about what specific data points we're looking at. When you're considering making a dictionary, it's important to think about each of the data fields that you have, whether you're the person entering it or you're the person retrieving that data. For example, you want to be able to consider the type of data, is it a specific type of field that you enter a certain way? Does it have a certain size, a unit of measure that needs to be consistent throughout the entry period? Also, consider if there are any options. Are there certain flow chart menus that you need to think about if this, then that, when thinking about that specific piece of data?

You'll also want to include in your data dictionary where that data element comes from or its source. Now, this may seem obvious in the moment, especially if you're the person entering or retrieving the data, but this is helpful when you step away from a project or other individuals need to come in. Including the source, as one of those features is important for other people to understand which pieces are interconnected to that specific data function.

Next, include in your data dictionary a frequency of measurement. How often do we look at this data and how often does it get updated? For example, some data is only entered quarterly or even annually. And it's important to know when that's happening. So if you're, for example, looking at a data set in June, if it hasn't been updated since January, you may want to consider the freshness of that data and if it's really providing you with an accurate representation of what's going on.

The last piece is to include who the owner of that data is. When we're talking about owner, yes, it could be a specific person, but more likely you should probably include the function or the specific role that is responsible for managing that data. That's important when we're thinking about transitioning, processes, workflows, and essentially the data from person to person or from role to role. This can also be really important when you're thinking about migrating data, understanding who owns that data or who is responsible for that data is an important part of the process.

When you're maintaining this dictionary, again, thinking about each of the elements that we covered earlier, the type, the size, options, the source, the frequency, and as well as the owner, this helps us understand when we're looking at the dictionary in total, who has ownership, who has accessibility, and who can access this document when we're thinking about ongoing use. Are we using it for documentation? Are we using it for communication? Are we using this data for analysis or even decision-making? These strengths and structure of your data dictionary is the thing that's going to help you be able to succeed in the future when you're thinking about using that data for operations.

This has been Sara with Business Bites. You can reach me at [email protected]. We would love to hear from you on what other terminology you'd like bite-sized. As always, give us a quick rating on your platform of choice and share this podcast with a friend. We'll see you next time.