Create a Data Glossary: A Step Towards Data Literacy

One common barrier to data literacy is simply knowing where to start. Sending everyone on a general data literacy training course isn’t always feasible or practical, as it may not capture the specific nuances of how data is used in your organization. However, I’ve found that most organizations already have reports or dashboards, and some teams are already using data from systems generating their own reports. These metrics and KPIs are an excellent starting point for improving data literacy.

Customizing Your Data Glossary

Before diving into the creation of a data glossary, keep in mind two important points:

1. Specificity: The definitions of metrics and KPIs will be unique to your organization.
2. Dynamic Nature: The glossary should be a living document, constantly updated by a select group of people as needed.

Building Your Glossary

A glossary of data terms should clearly describe each metric in business-friendly language. It should explain how metrics are calculated and interpreted, with examples if necessary. If your organization already has reports or dashboards, use them as a foundation for your glossary.

Creating the Data Glossary Table

Start by creating a table with four columns:

• Column 1: Metric or KPI - Name it as stated in the report.
• Column 2: Description - Use simple language, avoiding technical jargon.
• Column 3: Example - Provide an example if the metric or KPI involves variables (e.g., marketing channels, store names).
• Column 4: Alternative Names - List any other names the metric might be known by outside your organization or in third-party systems.

Implementing and Expanding the Glossary

Initially, attach or link the glossary to relevant reports or dashboards. As new metrics or KPIs are introduced and old ones retired, update the glossary accordingly. Keep old terms for reference during historical data discussions.

You can also start from scratch by identifying the most common metrics and expanding the glossary as new ones emerge in reports.

Achieving Consensus

Aiming for company-wide consensus on these terms can be challenging and time-consuming. Instead, as a data leader, start the glossary yourself, consult with your team, and refine it. Present the glossary to the business, but only share relevant metrics and KPIs with the appropriate teams to avoid unnecessary feedback loops.

Handling Disagreements

Disagreements about definitions are inevitable, both within and between departments. Embrace these disagreements as opportunities to demonstrate the need for a glossary. Use them to show the benefits of having clear definitions, despite the initial effort required.

Conversely, if there’s little feedback, promote the glossary when presenting reports and dashboards. This can encourage engagement and prompt feedback on unfamiliar or disputed terms.

Maintaining a Living Document

A data glossary should be regularly updated and reviewed. Version control is crucial for tracking changes in definitions, calculations, or metrics, allowing for accurate comparisons between old and new reports.

Practical Tools for Collaboration

With cloud-based document creators like Google Docs, Sheets, Microsoft Office Word, or Excel, creating and maintaining a glossary is easier than ever. Share the document organization-wide, allowing some to make changes, others to comment, and most to review.

Determine who can make changes and add comments. Ideally, only a few members of the data team should make changes, with select individuals in each department allowed to comment. These comments should be reviewed and validated by the data team before updating the glossary.

Questions to Validate Changes

When considering changes to the glossary, ask these questions:

• Where does the metric or KPI originate?
• Has it been adjusted in any way?
• Do we have access to the raw data?
• Where did the definition come from?
• Has the entire team agreed on this definition?

If the answers are satisfactory, make the changes. If not, conduct further investigation to ensure accuracy.

Leveraging Collaboration Software

For organizations using collaboration software like Atlassian’s Confluence, create a glossary template and maintain a live glossary within it. These tools offer built-in workflows, allowing you to assign review tasks to the appropriate people, ensuring accuracy and consensus.

In conclusion, a well-maintained glossary of data terms is essential for improving data literacy within your organization. By starting with existing reports and dashboards, achieving team consensus, and using collaborative tools, you can create a dynamic, valuable resource that supports informed decision-making across your organization.

Contact me at [email protected] to discuss how we can help you achieve data literacy in your organization and become a data driven organization.

Also, to learn how to build an effectrive data driven organization get my book, Data Culture. Avaialble at Amazon and the publishers website.