One of the most crucial components of fostering a healthy data culture is ensuring strong data literacy across your organisation. In our CEO’s book, Data Culture, he emphasises how essential data literacy is and provides practical strategies to enhance it. One of the most effective methods to begin this journey is to create a data glossary, a cornerstone tool that bridges the gap between data and business users by fostering a common language.

In this blog post, we’ll walk you through the step-by-step process of creating a data glossary and explore how it can significantly enhance data literacy within your organisation.

Why Traditional Data Literacy Efforts Often Fail

Many organisations attempt to improve data literacy by rolling out training programs, statistics classes, software tutorials, or sessions on self-service tools like Power BI or Tableau. While these efforts aim to help employees understand data, they often fail due to a lack of relevance and practicality for non-technical users.

Expecting everyone in the organisation to become proficient in analytics tools is unrealistic. Instead, organisations need a simple, accessible, and tailored approach to demystify data. This is where a data glossary comes in.

What Is a Data Glossary?

A data glossary is a document that lists all key metrics and KPIs used in your organisation, providing their:

1. Definitions (in plain business language).
2. Examples (to illustrate what the data looks like).
3. Alternative Names (if applicable).

Think of it as a single source of truth for understanding data, tailored specifically to your organisation’s unique terminology.

How to Build a Data Glossary

1. Start with the Right Template

Your data glossary should have a simple structure with four columns:

• Column 1: Metric/KPI Name
  Ensure the name is unique within the organisation. Avoid duplicate names for metrics, even if different teams use the same name but define them differently, agree on a unique name per metric/KPI across the entire organisation.
• Column 2: Explanation
  Use plain, business-friendly language to describe what the metric means. This column is designed for non-technical users, so avoid jargon.
• Column 3: Example
  Provide a real-world example of what the metric looks like in reports or dashboards. Indicate the format (e.g., two decimal places, a specific date range, or expected text values).
• Column 4: Alternative Names
  List any other names the metric might be known by, internally or externally. This helps align terminology across teams and ensures clarity.

2. Use Existing Reports as a Starting Point

The best way to populate your glossary is to start with the metrics and KPIs already in use within existing reports and dashboards. Review these reports and extract all the relevant metrics, adding their definitions, examples, and alternative names.

3. Resolve Conflicts and Ensure Uniqueness

As you build the glossary, you may discover conflicting definitions for the same metric. For example, the marketing team might define “active customer” as someone who transacted in the last 30 days, while the digital team might define it as someone who logged into the website in the last 30 days.

To resolve these conflicts:

• Discuss with both teams to reach a consensus on unique names for their metrics.
• If the term is system-defined and cannot be changed, agree on an organisational alias for clarity (e.g., “Active Customer (Marketing)” vs. “Active User (Digital)”).

When conflicts persist, form a data governance committee comprising representatives from key departments. This committee can help finalize definitions and ensure buy-in from all stakeholders.

Making Your Glossary a Living Document

Creating the glossary is only the first step; maintaining it is equally important. Follow these practices to ensure it stays relevant:

• Version Control: Use cloud-based tools like Google Sheets, Office 365, or Confluence to manage the glossary. Implement version control to track changes over time. This is critical for auditing past reports that may have used older definitions.
• Dedicated Ownership: Assign a small team of representatives from various departments to oversee updates. They can meet monthly to review new metrics, resolve discrepancies, and update the glossary as needed.

Integrating the Glossary into Daily Operations

For the glossary to improve data literacy, it must be accessible and integrated into everyday business processes:

• Link to Reports and Dashboards: Add hyperlinks to the glossary in online dashboards or attach it to emailed reports. This ensures that users can easily reference definitions while analyzing data.
• Promote Consistent Usage: Encourage the data team and senior leaders to use glossary terms consistently. When leadership models this behavior, it sets a standard for the entire organisation.

Key Benefits of a Data Glossary

• Clarity and Alignment: A common language reduces confusion and enables teams to interpret data consistently.
• Improved Decision-Making: Teams can confidently use data to make informed decisions without misinterpreting metrics.
• Stronger Data Culture: As employees engage more effectively with data, the organisation develops a healthier data culture.

Take the First Step Toward Better Data Literacy

Building a data glossary is a simple yet powerful way to kickstart your journey toward better data literacy. By creating a clear, centralized resource for understanding metrics, you empower employees to make data-driven decisions with confidence.

At Be Data Solutions, we specialize in helping organisations improve their data culture and literacy. Whether you need assistance creating your data glossary or implementing broader data literacy initiatives, we’re here to help.

Contact us today at hello@bedatasolutions.com to learn more about how we can support your organisation.