Keeping Metadata Updated

Why this matters

As a BI Developer, people rely on your dashboards and curated datasets. If metadata (names, descriptions, owners, lineage, freshness, tags) is outdated, users misinterpret metrics, pick the wrong fields, or duplicate logic. Clean, current metadata reduces support pings, speeds up analysis, and prevents expensive mistakes.

• Real task: A product manager asks what “Active Users” means. With updated metadata, they find a clear definition and time window.
• Real task: A column is deprecated. Up-to-date metadata warns users and suggests a replacement field before things break.
• Real task: An incident investigation needs lineage. Updated lineage shows which dashboards are impacted in minutes.

Concept explained simply

Metadata is data about your data: what a field means, when it updates, where it comes from, who owns it, and how to use it. Keeping it updated means every change in your data ecosystem is reflected in the catalog, semantic model, and dashboards.

Mental model

Think of metadata like signposts on a busy highway. If signposts are clear and current, drivers reach destinations safely. Outdated signposts cause wrong turns, delays, and crashes. Your job: keep the signposts correct and visible.

When to update metadata

• Schema changes: new/renamed/removed columns or tables.
• Logic changes: definition of a metric or transformation updated.
• Ownership changes: team or data steward updated.
• Freshness changes: schedule or SLA changes.
• Deprecations: fields or datasets marked for removal.
• New usage context: dashboard released, KPI added, or policy updated.

Worked examples

1) New column added to a fact table

Change: Added discount_amount to fact_orders.

• Update column description: purpose, currency, pre/post-tax, null behavior.
• Lineage: note derivation source (e.g., source system field or formula).
• Semantic model: add to dataset with proper data type and formatting.
• Dashboard: add tooltip description and example usage, if exposed.
• Tags: add tags like finance, revenue, pricing.

{
  "column": "discount_amount",
  "desc": "Amount subtracted from list price before tax, in USD.",
  "type": "numeric",
  "lineage": "calculated as list_price - sale_price",
  "owner": "Data Finance",
  "tags": ["finance", "revenue", "pricing"],
  "freshness": "daily 03:00 UTC"
}

2) Metric definition changed

Change: Active Users now defined as users with at least one session in the last 30 days (was 14 days).

• Metric definition: update description and time window.
• Impact note: dashboards where the metric is used.
• Version note: add change date, reason, and validation steps.
• Communication: add a deprecation note to old metric alias.

2025-03-10: Active Users window changed 14 -> 30 days. Reason: align with company KPI. Validated by comparing historical daily counts; expected +8-12% uplift.

3) Deprecating a column

Change: customer_tier_legacy being replaced by customer_tier.

• Mark deprecated with a clear sunset date.
• Add migration guidance: mapping from legacy to new values.
• Update impacted objects: dashboards, queries, scheduled reports.
• Post-sunset: remove from semantic layer and archive description.

Deprecated: Use customer_tier. Sunset on 2025-06-30. Mapping: BASIC -> BRONZE, STD -> SILVER, PRM -> GOLD.

Update checklist

• Name and description reflect current business meaning.
• Owner and contact are correct.
• Data type, format, and allowed values are set.
• Lineage: source tables, transformations, and downstream assets listed.
• Freshness/SLA and schedule accurate.
• Tags and sensitivity classification added.
• Deprecation state and replacement guidance (if applicable).
• Release note/version note created for non-trivial changes.

Step-by-step: plan and run an update

1. Identify change: collect from PRs, change logs, or requests.
2. Assess impact: search lineage and usage analytics.
3. Draft metadata: description, examples, tags, freshness, owner.
4. Coordinate: confirm with domain owner and QA checks.
5. Apply updates: catalog, semantic layer, dashboards.
6. Communicate: release note and, if needed, deprecation notice.
7. Verify: spot-check dashboards; ensure users see changes.

Title: [Asset/Metric] updated
Date: YYYY-MM-DD
Summary: What changed in one sentence.
Details: Definition, scope, fields impacted.
Impact: Expected change in numbers (%), dashboards affected.
Action: What users should do (e.g., switch to new field).
Owner: Team contact.

Exercises

Everyone can take the exercises and quick test for free. Note: Only logged-in users will have their progress saved.

Exercise 1 — Write an update plan

Scenario: A new field refund_reason (text) is added to fact_refunds. A dashboard “Refunds Overview” shows refund counts by reason.

• Define a good description with examples.
• Set tags, owner, and sensitivity.
• Document lineage and freshness.
• Draft a short release note.
• List the systems where you will apply updates (catalog, semantic layer, dashboard tooltips).

Exercise 2 — Deprecation notice

Scenario: gross_revenue_v1 will be replaced by gross_revenue on 2025-08-01 due to tax logic fixes. Three dashboards use the v1 metric.

• Write a deprecation block with a sunset date and migration guidance.
• Propose a communication message to analysts.
• Add a quick validation note describing how you will compare v1 vs new.

Common mistakes and how to self-check

• Vague descriptions: Fix by adding examples, units, and time windows.
• Forgetting lineage: Add upstream tables and downstream dashboards.
• No owner: Assign a team and backup contact.
• Hidden breaking changes: Write release notes and mark deprecations early.
• Stale freshness: Update the schedule after pipeline changes.
• Inconsistent naming: Use a naming pattern and stick to it.

Practical projects

• Build a “Metadata Update Playbook” page for your team using the checklist and templates above.
• Create a weekly metadata review ritual: a shared checklist with 10-minute slots for top datasets.
• Assemble a “Deprecation Calendar” that tracks upcoming sunsets and communication status.

Mini challenge

Timebox: 15 minutes. Pick a high-traffic dashboard you know. Identify one metric or field likely misunderstood. Update its description, add examples, and write a 3-line release note. If deprecation is relevant, add a sunset date and replacement.

Field: conversion_rate
Description: Orders / Sessions over the selected period. Sessions counted by web analytics tracker; excludes bot traffic.
Example: On 2025-03-01, 1,200 orders / 60,000 sessions = 2.0%.
Release note: Clarified denominator and bot filter. Expect minor (-0.2pp) drop vs prior method due to stricter bot exclusion.

Who this is for

• BI Developers and Analytics Engineers maintaining semantic layers and dashboards.
• Data Stewards and Analysts responsible for data catalogs and definitions.

Prerequisites

• Basic SQL and understanding of your data model.
• Familiarity with your BI tool's semantic layer and field descriptions.
• Access to your team's catalog or documentation space.

Learning path

1. Learn catalog fields: descriptions, owners, tags, sensitivity.
2. Practice lineage: upstream sources and downstream dependencies.
3. Adopt change notes: release notes and deprecation blocks.
4. Establish cadence: weekly sweeps + change-driven updates.
5. Automate what you can: reuse templates and checklists.

Next steps

• Do the exercises below and take the quick test to solidify concepts.
• Apply one update to a real dataset this week and log a release note.
• Share the checklist with your team and propose a 10-minute weekly review.