How to Keep a Business Glossary Up to Date

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 20th, 2026 | 22 min read

Quick answer: How to keep a business glossary up to date?

Keeping a business glossary up to date means ensuring that terms, definitions, owners, and relationships accurately reflect how your organization operates today. It is a continuous process of reviewing definitions, capturing changes from projects and systems, and retiring obsolete concepts so people can rely on the glossary as the single source of truth.

  • Accurate, current definitions: Terms match how metrics and concepts are implemented in data models and dashboards.
  • Clear ownership and workflows: Every important term has an accountable owner and a repeatable path for proposing and approving changes.

Below: why freshness matters, governance and roles, a 30/60/90-day rollout plan.

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A business glossary is only valuable if people trust it. That trust erodes quickly when definitions lag behind how the business actually operates, metrics shift without explanation, or new concepts never make it into the glossary. At that point, teams quietly build their own spreadsheets and slide decks, and the shared language you worked so hard to define starts to fragment.

Keeping a glossary current is an ongoing operational practice, not a one-off project. It needs clear ownership, lightweight workflows, and tight integration with your existing data and BI tools. Whether you are evolving an established business glossary or scaling one you just created using a guide like how to create a business glossary, this article walks through the governance model, review cadence and change management, technical integration, user engagement, quality standards, automation with platforms like Atlan, and a 30/60/90-day rollout plan.

Why keeping a business glossary up to date matters

An up-to-date glossary reduces confusion, accelerates work, and lowers risk. When definitions drift, teams make different assumptions about the same words, leading to misaligned KPIs and slow decision-making. A maintained glossary provides a shared contract between business and data teams, anchored in how your systems actually behave.

Definition drift is a leading cause of metric confusion


Most metric debates are not about SQL; they are about definitions. “Active customer,” “churn,” or “revenue” often mean subtly different things to sales, finance, and product. Over time, those differences accumulate into definition drift, where the glossary, data models, and dashboards all say something slightly different.

Common patterns:

  • The glossary definition was agreed in a workshop but never updated after a new data warehouse model shipped.
  • BI teams created quickfix metrics for a launch and never reconciled them with the official glossary entry.
  • Mergers and acquisitions introduced new concepts that overlapped with existing terms but were never harmonized.

By regularly reconciling glossary entries with actual implementations and lineage, you avoid “dashboard wars” and keep executive conversations focused on decisions instead of definitions. Analyses of metric drift show how small definitional changes and component shifts can cause KPIs to move in ways that confuse stakeholders.

Operational efficiency: less rework, fewer questions, faster onboarding


When definitions live in people’s heads or stale slides, every new project starts with a round of “what do we mean by…?” Clarifying these ad hoc is slow and error-prone. A current glossary gives teams a self-service way to answer basic questions and align on semantics upfront.

Benefits you can measure:

  • Less rework: Fewer last‑minute spec changes when engineering or analytics discovers ambiguity in terms.
  • Fewer repetitive questions: Stakeholders can search the glossary or catalog instead of pinging the same SMEs.
  • Faster onboarding: New joiners use the glossary as a structured orientation to your business language.

Modern catalogs and glossaries, such as those described in what is a glossary, make this context available directly in the tools people already use.

Risk and compliance: clearer accountability for sensitive/regulated terms


Regulated concepts (like “customer”, “PII”, “consent status”, or “capital adequacy ratio”) often appear across hundreds of tables and reports. If their definitions or allowed values change, you need to know who is accountable and where that change must propagate.

Up-to-date glossaries help you:

  • Link regulated terms to specific owners, policies, and tagged assets.
  • Show auditors a clear chain from policy definition to implemented metric or control.
  • Quickly identify which reports or pipelines are impacted when regulations or policies change.

By tying glossary terms to assets and tags in your data catalog, for example via features like asset-term link workflows, you reduce manual impact analysis and lower the risk of inconsistent regulatory reporting.

Governance model and roles for your business glossary

A living glossary needs a governance model that is clear but lightweight. You want enough structure to avoid chaos, without turning every term into a three‑month committee decision. This starts with defined roles and a simple workflow for changes.

Core roles and responsibilities (RACI you can copy)


Use a RACI to clarify who does what for glossary changes. Adapt this example to your context:

Role / Activity Propose new term Draft / edit definition Approve definition Maintain / review Resolve conflicts Bulk imports / migration
Business SME R C C C C I
Data steward C R C R R A
Domain owner A C A A A C
Analytics lead C R C C C I
Governance team C C C C R R
Glossary admin I I I I I R

R = Responsible, A = Accountable, C = Consulted, I = Informed.

Map these roles to concrete personas in your catalog or glossary tooling. In platforms like Atlan, you can use glossary ownership and permissions (as described in set up glossaries) to reinforce this RACI.

A practical glossary workflow (intake → triage → draft → review → approve → publish)


Avoid bespoke processes per team. Instead, standardize a simple end‑to‑end workflow:

Intake → Triage → Draft → Review → Approve → Publish → Monitor

Intake: Anyone can propose a new term or change via a form, Slack command, or directly in the catalog.

Triage: A steward or governance team member assigns priority, domain, and owner within one business day.

Draft: The assigned owner or SME writes or updates the definition, examples, and metadata.

Review: Stakeholders (analysts, neighboring domain owners) review and comment. Use async collaboration tools or comments in your catalog.

Approve: The domain owner approves. For high‑impact terms, a governance council may sign off.

Publish: The term goes live with a change note. Downstream consumers are notified if the term is already linked to assets.

Monitor: Track usage, feedback, and staleness. Queue the term for periodic review.

This workflow can be partially automated in modern platforms; for example, Atlan’s asset-term link workflow lets you trigger approvals when someone links a critical term to a new table or dashboard.

Escalation paths and dispute resolution


Disagreements about definitions are common and healthy. Have clear escalation rules:

  1. Peer resolution: Stewards and SMEs try to resolve in comments or a quick meeting.
  2. Domain owner decision: If no consensus, the domain owner makes the call and documents the rationale.
  3. Cross‑domain council: For conflicts spanning multiple domains (e.g., “customer” for sales vs. support), a council of domain owners and a governance lead decides.

Document decisions in the glossary entry’s change history or description. This prevents the same debate from recurring and shows new team members why a particular definition was chosen.

Review cadence and change management

Even well‑written definitions become stale as business logic evolves. Proactive review cycles and event‑driven triggers help you catch drift before it causes confusion.

Scheduled reviews (tiered by criticality)


Not all terms need the same review frequency. Segment your glossary:

  • Critical / regulated terms (e.g., revenue, PII, capital ratios): Review quarterly. These often have compliance or financial reporting implications.
  • Core business metrics (e.g., churn, conversion, active user): Review semi‑annually. These drive decisions and dashboards.
  • Standard terms (e.g., product category, region): Review annually.
  • Long‑tail / legacy terms: Review opportunistically when related systems or processes change, or deprecate if unused.

Assign review due dates in your catalog metadata. Platforms like Atlan let you set custom fields and trigger reminders, as shown in custom metadata best practices.

Trigger-based updates (project kickoffs, schema migrations, M&A)


Scheduled reviews are not enough. Hook glossary updates into existing workflows:

  • New data model or schema change: Before deploying, check if any glossary terms are affected and update definitions or lineage.
  • BI project kickoff: Part of your requirements checklist should be “confirm or propose glossary entries for new metrics.”
  • Mergers and acquisitions: Reconcile glossaries from both organizations. Identify duplicates, conflicts, and gaps early.
  • Regulatory change: When a new policy or law affects how you define a concept, update the glossary immediately and notify downstream consumers.

Embed these triggers in your project templates, JIRA workflows, or data platform CI/CD pipelines so updates happen automatically, not as an afterthought.

Communicating changes and managing impact


When a definition changes, people using that term need to know. A communication plan might include:

  • Change notes: Capture what changed, why, and when in the glossary entry itself.
  • Notifications: Alert owners of linked dashboards, reports, or datasets. Use your catalog’s lineage and notification features (e.g., Atlan’s asset watchers or Slack integrations).
  • Transition period: For major changes, run both old and new definitions in parallel with deprecation warnings, or provide migration guides.
  • Training snippets: Update onboarding docs, add a note to your internal wiki, or post a Slack announcement.

Document your change management process in a central location (a wiki page or the glossary itself) so everyone knows what to expect when a term evolves. Change management frameworks emphasize that clear communication and stakeholder engagement are critical to successful adoption of new processes and definitions.

Technical integration: linking glossary to catalog, lineage, and BI tools

A glossary is most powerful when it is not a separate document but deeply integrated into your data stack. Users should see definitions in context, wherever they work.

Bi-directional sync with your data catalog


Modern catalogs treat the glossary as a first‑class citizen. Business terms are linked to technical assets (tables, columns, dashboards) so you can:

  • Navigate from a glossary term to all the tables and reports that implement it.
  • See which business concepts a given table or column supports.

Bi‑directional sync means:

  • When you update a term in the glossary, those changes reflect immediately on linked assets.
  • When lineage or usage patterns change, the catalog can flag glossary entries that may need review.

Atlan’s glossary overview describes how terms, categories, and assets form a connected graph, not isolated lists.

Embedding definitions in BI tools (Tableau, Looker, Power BI)


Data consumers should not have to leave their BI tool to find a definition. Integrate glossary content directly:

  • Tableau: Use the Atlan connector or custom extensions to surface glossary definitions in field descriptions or info tooltips.
  • Looker: Map LookML dimension labels or descriptions to glossary entries. Some organizations use Looker’s metadata API to pull in definitions at build time.
  • Power BI: Embed glossary links in dataset or measure descriptions, or use Power BI’s endorsement and certification features to point to your catalog.

When analysts see a metric like “Monthly Recurring Revenue” in a dashboard, a tooltip or info icon should show the official definition, owner, and a link to the full glossary entry. This in‑context help reduces ambiguity and builds trust. Research on data literacy and self‑service analytics shows that embedded context and definitions significantly improve user confidence and reduce errors.

Automated lineage to trace definition → implementation


Lineage connects glossary terms to the SQL, ETL jobs, and dashboards that bring them to life. This gives you:

  • Impact analysis: “If I change the definition of ‘active customer,’ which dashboards break?”
  • Consistency checks: “Does this table’s logic match the glossary definition, or has it drifted?”
  • Audit trails: “Show me the full path from policy to report for this regulated metric.”

Platforms like Atlan automatically parse SQL, dbt models, and BI metadata to build lineage graphs. You can then tag specific transformations or columns with glossary terms, as described in column-level lineage. When definitions change, walk the lineage graph to notify or update affected assets.

APIs and webhooks for custom integrations


Not every tool has a pre‑built glossary connector. Use APIs and webhooks to:

  • Push glossary updates to your internal wiki, Confluence, or SharePoint.
  • Trigger a review workflow in JIRA when a term’s “last reviewed” date expires.
  • Pull glossary data into your data quality or observability platform to enrich context for alerts.

Atlan’s REST API lets you read and write glossary entities programmatically. Combine this with webhooks to build event‑driven integrations that keep your glossary in sync with the rest of your data ecosystem.

User engagement: making the glossary a daily habit

A glossary that lives only in a governance team’s backlog is not a glossary anyone uses. Build habits that pull people in naturally.

In-context access (search, browser extensions, Slack bots)


Meet users where they work:

  • Universal search: Let people search the glossary from your catalog’s homepage, Slack, or a browser extension. Atlan’s global search indexes glossary terms alongside assets.
  • Slack bot: A bot that responds to /define <term> with the glossary entry and a link to the full page.
  • Browser extension: Highlight glossary terms on internal dashboards or wikis, with hover tooltips showing definitions.

The less friction, the more likely people will consult the glossary instead of guessing or asking someone.

Gamification and recognition (leaderboard for stewards, “term of the month”)


Encourage contributions with light gamification:

  • Steward leaderboard: Show who has reviewed or updated the most terms this quarter. Recognize top contributors in team meetings or newsletters.
  • Term of the month: Feature a newly added or newly refreshed term in your data team’s Slack or wiki. Explain why it matters and thank the owner.
  • Badges or certifications: Offer a “glossary champion” badge for team members who complete a short course on using and contributing to the glossary.

These small nudges build a culture of shared ownership and continuous improvement. Behavioral science research shows that public recognition and small incentives can significantly increase engagement with organizational knowledge resources.

Training and onboarding materials


Integrate glossary training into:

  • New hire onboarding: Include a 15‑minute session on how to search the glossary and whom to contact for new terms.
  • Lunch and learns: Host quarterly sessions where domain owners walk through their glossary domains and answer questions.
  • Role‑specific guides: Create short guides for analysts (“How to link a glossary term to your dashboard”) and engineers (“How to tag a dbt model with a glossary term”).

Make these materials searchable and link them from the glossary homepage so people can self‑serve.

Feedback loops (comment threads, suggested edits, upvotes)


Invite users to improve the glossary:

  • Comments: Let anyone comment on a glossary entry to ask questions, suggest clarifications, or report errors.
  • Suggested edits: Use a workflow where users can propose changes that stewards review and approve.
  • Upvotes or “helpful” flags: Let users vote on whether a definition is clear. Low scores trigger a review.

Closing the loop shows contributors that their input matters, and it surfaces issues faster than waiting for a scheduled review. Platforms like Atlan support commenting and discussion threads directly on glossary entries.

Quality standards and metrics

To keep the glossary trustworthy, define what “good” looks like and measure it.

Completeness (every critical term has owner, definition, examples)


Set minimum standards for a glossary entry:

  • Required fields: Term name, definition (plain language), owner, domain/category, last reviewed date.
  • Recommended fields: Examples, related terms, linked assets, usage notes.
  • For regulated terms: Add policy links, approval history, compliance tags.

Run periodic audits to flag incomplete entries. Many catalogs let you create custom metadata templates or required fields.

Consistency (naming conventions, style guide)


Avoid chaos with a style guide:

  • Naming: Use singular nouns for concepts (e.g., “Customer,” not “Customers”). Use consistent capitalization (e.g., title case for term names).
  • Tone: Write definitions in plain language for a business audience. Avoid jargon unless it is industry standard.
  • Length: Aim for definitions under 100 words. Use examples or extended notes for details.
  • Acronyms: Spell out on first use and cross‑reference. E.g., “Annual Recurring Revenue (ARR)” with a cross‑link from “ARR.”

Publish the style guide in your glossary’s documentation section so contributors know what is expected.

Freshness (% of terms reviewed in last N months)


Track staleness metrics:

  • % of terms reviewed in the last 6 or 12 months: This is your primary health metric.
  • Average age of definitions: Flag any term not touched in over 18 months for review or retirement.
  • Orphaned terms: Terms with no owner or no linked assets may be candidates for deprecation.

Set targets (e.g., “90% of critical terms reviewed in the last quarter”) and dashboard them. Share progress in governance meetings to keep stakeholders accountable.


Measure whether the glossary is actually used:

  • Search volume: How many glossary searches per week? Is it growing?
  • Link clicks: How often do users click from a glossary term to a linked dashboard or table?
  • Asset coverage: What % of your core tables and dashboards are linked to at least one glossary term?
  • User feedback: Track comment volume, upvotes, and suggested edits as proxies for engagement.

Low usage signals that the glossary is hard to find, incomplete, or not integrated into daily workflows. Use these metrics to guide improvements.

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Automation and tooling (focus on Atlan)

Manual glossary maintenance does not scale. Modern platforms like Atlan automate much of the heavy lifting, letting your team focus on governance decisions rather than administrative work.

Auto-discovered assets and suggested term mappings


Atlan crawls your data sources (warehouses, BI tools, dbt, etc.) and automatically catalogs tables, columns, and dashboards. It then uses heuristics and machine learning to:

  • Suggest which glossary terms might apply to a given column or dataset based on name, lineage, and usage.
  • Flag columns that look like PII or other sensitive data, prompting you to link them to the relevant glossary term and policy.

This reduces the manual toil of tagging thousands of assets. Analysts and stewards review and approve suggestions rather than hunting for connections themselves. See auto-classification and propagation for how tags and terms can be inherited automatically.

Workflow automation (approval chains, notifications, SLA tracking)


Atlan’s workflow engine lets you:

  • Define multi‑step approval processes for high‑impact terms (e.g., require sign‑off from finance and legal for “revenue”).
  • Send Slack or email notifications when a term is updated, a review is due, or a linked asset changes.
  • Track SLAs for triage and approval so nothing falls through the cracks.

For example, when a new dbt model references a glossary term, Atlan can trigger a review workflow to confirm the mapping is correct before promoting the model to production. This is described in playbooks and workflows.

AI-assisted drafting and enrichment (e.g., Atlan’s AI features)


Writing definitions from scratch is time‑consuming. Atlan’s AI features can:

  • Generate a first draft of a definition based on column names, sample data, and lineage.
  • Suggest related terms or synonyms to cross‑reference.
  • Summarize usage patterns or common queries for a term to inform the description.

Stewards review and refine these drafts, turning a 30‑minute writing task into a 5‑minute editing task. This lowers the barrier for busy SMEs and speeds up glossary growth. See Atlan AI for current capabilities.

Integration with dbt, Snowflake, and other modern data stack tools


Atlan natively integrates with the modern data stack:

  • dbt: Pull model and column descriptions from dbt YAML into the glossary, or push glossary terms back to dbt as tags. Lineage from dbt models to BI dashboards is automatically mapped.
  • Snowflake: Sync Snowflake tags and row access policies with glossary terms to enforce consistent governance.
  • Fivetran, Airflow, Looker, Tableau, etc.: Atlan’s connectors keep metadata in sync so glossary links remain accurate as pipelines and dashboards evolve.

This deep integration means your glossary is never a separate system of record; it is part of the fabric of your data platform. Documentation on integrations covers the full list of supported tools and how to configure them.

Key takeaways and a 30/60/90-day rollout plan

Keeping a business glossary up to date is continuous, but you can build momentum with a focused rollout.

Summary of best practices


  1. Clear ownership and lightweight workflows: Every term has an owner; every change follows a simple intake → triage → draft → review → approve → publish flow.
  2. Tiered review cadence: Review critical terms quarterly, core terms semi‑annually, and long‑tail terms opportunistically.
  3. Trigger‑based updates: Hook glossary changes into project kickoffs, schema migrations, and M&A processes.
  4. Deep technical integration: Link glossary terms to data catalog assets, embed definitions in BI tools, and use lineage for impact analysis.
  5. User engagement: Make the glossary easy to search, recognize contributors, and invite feedback through comments and suggestions.
  6. Quality metrics: Track completeness, consistency, freshness, and usage; set targets and dashboard progress.
  7. Automation: Use platforms like Atlan to auto‑discover assets, suggest mappings, trigger workflows, and draft definitions with AI.

30/60/90-day plan to operationalize glossary maintenance


Days 1–30: Foundation

  • Week 1: Audit your current glossary. Identify incomplete, stale, or orphaned entries. Tag the top 20 critical terms for immediate review.
  • Week 2: Define roles (domain owners, stewards, admins) and assign owners to all critical terms. Document your RACI and escalation paths.
  • Week 3: Set up a simple intake form (Google Form, Slack workflow, or your catalog’s built‑in request feature) for new term proposals. Publish it to your team.
  • Week 4: Draft a one‑page style guide for definitions and run a pilot review cycle on 5–10 critical terms to validate your workflow.

Days 31–60: Integration and automation

  • Week 5: Configure your catalog (Atlan or similar) to auto‑discover assets and suggest term mappings. Review and approve a batch of suggestions.
  • Week 6: Integrate glossary definitions into your primary BI tool (Tableau, Looker, Power BI). Test with a handful of popular dashboards.
  • Week 7: Set up automated notifications for term updates and review due dates. Create a Slack channel or email digest for glossary changes.
  • Week 8: Run a short training session (“Glossary 101”) for analysts and business users. Record it for onboarding.

Days 61–90: Adoption and refinement

  • Week 9: Launch a “term of the month” feature and recognize top contributors. Share the first batch of updated definitions company‑wide.
  • Week 10: Measure initial metrics: % of critical terms reviewed, search volume, asset coverage. Dashboard them in your team’s regular review.
  • Week 11: Collect feedback from users via a survey or comment threads. Identify pain points (hard to find, unclear definitions, missing terms).
  • Week 12: Refine your workflow and tooling based on feedback. Plan the next quarter’s review schedule and any new integrations (e.g., dbt, additional BI tools).

Common pitfalls and how to avoid them


  1. Pitfall: Glossary becomes a bottleneck because every change requires a committee.

    • Fix: Empower domain owners to approve most changes. Reserve council review for cross‑domain conflicts only.

  2. Pitfall: Definitions are written in technical jargon that business users cannot understand.

    • Fix: Enforce a style guide that prioritizes plain language and examples. Have a non‑technical reviewer sign off on critical terms.

  3. Pitfall: The glossary is out of sync with actual data models and dashboards.

    • Fix: Use lineage and automated asset‑term linking. Make glossary updates part of your data modeling and BI deployment checklist.

  4. Pitfall: No one uses the glossary because it is hard to find.

    • Fix: Embed search in Slack, your BI tool, and your wiki. Make the glossary homepage prominent in your catalog.

  5. Pitfall: Stewards burn out because maintenance is all manual.

    • Fix: Invest in automation: AI‑assisted drafting, auto‑discovery, and workflow orchestration. Let humans focus on judgment calls, not data entry.

Frequently asked questions

How often should we review our business glossary?


You should review critical terms at least quarterly and standard terms annually. Long‑tail or legacy terms can be reviewed opportunistically when systems or processes change. The key is to align review frequency with business risk and usage, not to set a single schedule for all terms. Tiered cadences help focus limited expert time where it matters most.

Who should own the business glossary?


Ownership should be shared between business and data teams. A data governance or data enablement function typically runs the process, while domain owners and stewards are accountable for specific sets of terms. Each important term should have a clearly named owner who can make decisions and coordinate changes across teams when definitions or implementations evolve.

How many terms are “too many” for a glossary?


There is no universal limit, but problems start when users cannot find what they need or when many entries are stale. Instead of capping term counts, focus on organizing terms into clear domains and categories, retiring unused entries, and applying quality gates. A large but well‑structured, well‑maintained glossary can still be highly effective and trusted.

What is the difference between a data dictionary and a business glossary?


A data dictionary describes technical metadata such as table names, column types, and constraints. A business glossary describes business concepts and metrics in human language, often independent of specific implementations. Both are important. Ideally, glossary terms are linked to the relevant data dictionary objects so users can move seamlessly from concept to technical detail.

How do we encourage busy SMEs to contribute to the glossary?


Lower the effort and increase the perceived value for SMEs. Provide simple templates, pre‑filled drafts, and, where available, AI‑assisted suggestions so they edit instead of writing from scratch. Make glossary stewardship part of role expectations, and show how better definitions reduce interruptions, clarification meetings, and rework for their own teams over time.

How do we handle conflicting definitions across regions or business units?


Start by understanding whether differences are truly conflicts or reflect legitimate regional variations. If both are valid, create distinct terms with clearly defined scopes and cross‑references. Where a single global standard is required, designate a decision‑maker, document the chosen definition and rationale, and plan a transition period for dependent reports, systems, and training materials.

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