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Dashboard Usage Notes And Definitions

Learn Dashboard Usage Notes And Definitions for free with explanations, exercises, and a quick test (for BI Analyst).

Published: December 22, 2025 | Updated: December 22, 2025

Table of Contents

Why this matters

Great dashboards answer questions fast. Usage notes and clear metric definitions prevent misinterpretation, duplicate work, and emergency Slack threads. As a BI Analyst, you will:

  • Clarify exactly what each metric means and how it is calculated.
  • Explain filters, date grains, and segment logic so stakeholders trust the numbers.
  • Add context, caveats, and data freshness so people know when not to use a chart.
  • Reduce support overhead by documenting owners and when to contact them.

Concept explained simply

Dashboard usage notes are short, practical instructions on how to use a dashboard safely and effectively. Metric definitions explain what a metric measures, the formula, and any business rules or filters used.

Mental model

Think of your dashboard as a map. Usage notes are the legend that explains the symbols; definitions are the scale and coordinates. Without them, people get lost.

What goes into a good metric definition?
  • Name: The metric’s exact label on the dashboard.
  • Business meaning: One sentence on what it represents.
  • Formula: The calculation, including inclusions/exclusions.
  • Filters/Segments: Default filters applied in the dashboard.
  • Date grain and time zone: Day/Week/Month and TZ used.
  • Data source and freshness: Where it comes from and update schedule.
  • Ownership: Who to ask if something looks off.
  • Caveats: Known limitations or edge cases.

What to include in dashboard usage notes

  • Purpose and audience: What decisions this dashboard supports.
  • Key actions: How to slice/filter without breaking meaning.
  • Data freshness: Last refresh and schedule.
  • Scope and exclusions: Data or regions not included.
  • Metric definitions: Links not required—place essential definitions right here in short form.
  • Contact and change log: Owner, contributors, and last update summary.
Mini checklist to validate your notes
  • Would a new stakeholder understand the metric in under 10 seconds?
  • Is the formula unambiguous and reproducible by another analyst?
  • Are defaults (filters, date grain, time zone) explicitly stated?
  • Do you list data freshness and known caveats?
  • Is there a clear owner/contact?

Worked examples

Example 1: Product Analytics — Active Users (7-day)
  • Name: Active Users (7-day)
  • Business meaning: Unique users who triggered any product event in the last 7 days.
  • Formula: Count(distinct user_id) where event_timestamp between today-6d and today 23:59:59, event_type in {login, page_view, feature_use}.
  • Filters: Default filter excludes internal users (email domain = company.com).
  • Date grain: Daily; Time zone: UTC.
  • Data source/freshness: event_log table; updated hourly at hh:15.
  • Owner: Analytics Platform Team.
  • Caveats: Users on devices without tracking may be undercounted by ~2–3%.
Example 2: Ecommerce — Gross Revenue
  • Name: Gross Revenue
  • Business meaning: Total money from orders before discounts, shipping, taxes, refunds.
  • Formula: Sum(order_items.quantity * order_items.list_price).
  • Filters: Includes completed orders only; excludes test orders (test_flag = false).
  • Date grain: Daily based on order_completed_at; Time zone: Store TZ (US/Eastern).
  • Data source/freshness: orders, order_items; daily full refresh at 02:00 Eastern.
  • Owner: Commerce BI.
  • Caveats: Promotions and refunds reflected in separate metrics (Net Revenue, Refunds). Do not compare directly to finance GL without period close adjustments.
Example 3: Operations — On-time Delivery %
  • Name: On-time Delivery %
  • Business meaning: Share of shipments delivered on or before promised date.
  • Formula: On-time Deliveries / Total Deliveries, where on-time if actual_delivery_date <= promised_date.
  • Filters: Excludes shipments with address errors (error_code != ADDRESS).
  • Date grain: Weekly (ISO week); Time zone: UTC.
  • Data source/freshness: shipments, promised_dates; updated every 3 hours.
  • Owner: Ops Analytics.
  • Caveats: Promised date is set by carrier SLA; manual overrides not captured prior to 2023-01-01.

Template you can reuse

Copy-paste metric definition template 
Name:
Business meaning:
Formula:
Filters/Segments:
Date grain & Time zone:
Data source & Freshness:
Owner:
Caveats:
Copy-paste dashboard usage notes template 
Purpose & Audience:
Key Actions (how to filter/slice safely):
Data Freshness (schedule & last refresh):
Scope & Exclusions:
Key Metric Definitions (short):
Owner & Contact:
Change Log (date & summary):

Step-by-step: create usage notes for an existing dashboard

  1. Scan the dashboard: List all metrics, filters, date fields, and segments used.
  2. Interview the owner: Confirm purpose, audience, and critical decisions supported.
  3. Verify calculations: Rebuild one metric in SQL or your BI tool to match the chart.
  4. Write short definitions using the template (aim for 2–5 lines each).
  5. Document defaults: Filters, date grain, time zone, and data freshness.
  6. Add caveats: Data gaps, seasonality, or known edge cases.
  7. Peer review: Ask another analyst to reproduce the metric from your definition.
  8. Publish: Place notes in a visible area (top panel or info modals) and update the change log.

Exercises

Complete these exercises to practice. The quick test at the end is available to everyone. If you are logged in, your progress will be saved.

Exercise 1 — Draft usage notes for a Weekly Sales Overview

Mirror of Exercise ex1 below.

  • Define Revenue, Orders, and Average Order Value with business meaning and formulas.
  • State default filters, date grain, time zone, and data freshness.
  • Add owner and one caveat.

Exercise 2 — Tighten an ambiguous metric

Mirror of Exercise ex2 below.

  • Rewrite a vague definition so that another analyst can reproduce it exactly.
  • Include filters and time boundaries.
Self-check checklist
  • Every metric has a clear formula and business meaning.
  • Defaults (filters, date grain, time zone) are explicit.
  • Freshness schedule and last refresh are noted.
  • Known caveats are listed.
  • Owner/contact is included.

Common mistakes and how to self-check

  • Vague formulas: “Active users” without event list or time window. Fix: list events and exact time range.
  • Hidden filters: A chart excludes test data but definition does not mention it. Fix: document all default filters.
  • Date grain confusion: Weekly chart defined with daily logic. Fix: state date grain used and how aggregation works.
  • Freshness mismatch: Users assume real-time when it’s daily. Fix: write refresh schedule and last load timestamp.
  • No owner: Issues stall. Fix: add name/team and escalation path.
  • Copy-paste drift: Outdated notes after a model change. Fix: update the change log and dateModified when metrics change.

Practical projects

  • Take a top-3 dashboard and add usage notes plus definitions for all visible metrics. Ask a peer to reproduce one metric from your notes.
  • Create a team-wide “Metric Library” document using the template; include owner and freshness for each KPI.
  • Run a 15-minute read-through with stakeholders; collect 3 questions and convert them into clarifying notes/caveats.

Who this is for

  • BI Analysts and Analytics Engineers maintaining dashboards.
  • Product/Marketing analysts who share data with non-analysts.
  • Team leads who need consistent KPI definitions.

Prerequisites

  • Basic understanding of your BI tool (e.g., filters, date grains, calculated fields).
  • Access to underlying data model or SQL to verify calculations.
  • Familiarity with your organization’s core KPIs.

Learning path

  • Start: Identify your 5 most used metrics and write definitions using the template.
  • Next: Add dashboard-level usage notes (purpose, audience, freshness, caveats).
  • Then: Peer review with another analyst and a stakeholder.
  • Finally: Maintain a change log and schedule quarterly reviews.

Next steps

  • Apply the template to one critical dashboard today.
  • Schedule a 30-minute peer review session.
  • Take the quick test below to confirm you can spot ambiguities.

Mini challenge

Pick any metric you use daily. In exactly four lines, write a complete definition covering business meaning, formula, default filters, and freshness. Share it with a teammate and ask: “Can you rebuild this from my notes?”

Quick test and progress saving

Everyone can take the quick test. If you are logged in, your score and progress will be saved automatically.

Practice Exercises

2 exercises to complete

Instructions

You are documenting a Weekly Sales Overview dashboard with three tiles: Revenue, Orders, and Average Order Value (AOV). Defaults: last 12 weeks, week starting Monday, time zone US/Pacific, excluding test orders (test_flag=false). Data comes from orders and order_items; refreshed nightly at 01:00 Pacific.

  • Write usage notes (purpose, key actions, freshness, scope/exclusions).
  • Write definitions for Revenue, Orders, and AOV using the provided template.
  • Add owner and one realistic caveat.
Expected Output
Short usage notes covering purpose, key actions, freshness, scope; plus 3 concise metric definitions with formula, filters, grain, source/freshness, owner, caveat.

Dashboard Usage Notes And Definitions — Quick Test

Test your knowledge with 7 questions. Pass with 70% or higher.

7 questions70% to pass
Source To Dashboard LineageModeling And Transformation Notes

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