bi-report

name	bi-report
description	Design BI reports and analytics views — with metric definitions, data source mapping, filter logic, drill-down paths, and refresh schedules that enable self-service decision-making.
metadata	{"displayName":"BI Report","categories":["data","business"],"tags":["business-intelligence","reports","analytics","SQL","self-service"],"worksWellWithAgents":["bi-analyst","data-analyst","data-visualization-specialist","product-analyst"],"worksWellWithSkills":["dashboard-design","metrics-framework"]}

BI Report

Before you start

Gather the following from the user before designing any report:

1. What decision does this report support? Name the specific business question. "How is revenue trending?" is a question. "Revenue dashboard" is not.
2. Who is the audience? Executives need summaries and trends. Analysts need drill-downs and raw data access. Operators need real-time alerts.
3. What metrics matter? List the 3-7 KPIs this report must answer. Every metric needs a precise definition before you build anything.
4. What data sources exist? Tables, schemas, APIs, or third-party systems. Confirm access and freshness.
5. How often is the data needed? Real-time, hourly, daily, weekly — this drives refresh strategy and cost.
6. What filters and breakdowns are required? Date range, region, product line, customer segment — define the dimensions.

If the user says "build me a sales report," push back: "What specific questions should this report answer? Who will use it and how often?"

BI report design template

1. Report Purpose

Write 2-3 sentences:

• The business question this report answers
• The primary audience and how they will use it
• The cadence of use (daily standup, weekly review, monthly board meeting)

2. Metric Definitions

Define every metric precisely. Ambiguous metrics produce misleading reports.

Metric	Definition	Formula	Unit	Grain
Monthly Recurring Revenue	Sum of active subscription values at month end	SUM(subscription_amount) WHERE status = 'active' AND date = last_day_of_month	USD	Monthly
Churn Rate	Percentage of customers who cancelled in the period	cancelled_customers / start_of_period_customers * 100	%	Monthly
Average Order Value	Mean revenue per completed order	SUM(revenue) / COUNT(DISTINCT order_id) WHERE status = 'completed'	USD	Daily

For each metric, specify:

• Includes/excludes: Does revenue include refunds? Does churn count downgrades?
• Null handling: What happens when a dimension value is missing?
• Historical comparability: Has the definition changed? Document when and how.

3. Data Source Mapping

Metric	Source Table(s)	Key Columns	Join Logic	Known Issues
MRR	billing.subscriptions	customer_id, amount, status, period_end	None	Trial subscriptions have amount=0, exclude
Churn Rate	billing.subscriptions, crm.customers	customer_id, cancelled_at	Join on customer_id	Reactivated customers counted as new, not returning

Document data freshness for each source — when was the last ETL run, what is the typical lag?

4. Filters and Parameters

Filter	Type	Default	Options	Behavior
Date Range	Date picker	Last 30 days	Any range, max 1 year	Applies to all metrics
Region	Multi-select	All	NA, EMEA, APAC, LATAM	Filters underlying data, not just display
Product Line	Single-select	All Products	Product A, B, C	Changes all metrics to selected product

Specify filter interactions: Does selecting a region also filter the product dropdown to products available in that region?

5. Layout and Visualizations

Describe each section of the report top-to-bottom:

1. Summary bar — KPI cards showing current value, period-over-period change (%), and trend arrow. Metrics: MRR, Churn Rate, New Customers, AOV.
2. Trend chart — Line chart of MRR over time with comparison period overlay. X-axis: date grain matching the filter. Y-axis: USD.
3. Breakdown table — Tabular view of all metrics broken down by the selected dimension (region, product, segment). Sortable on every column. Include sparklines for trend.
4. Detail drill-down — Clicking any row in the breakdown table opens a filtered view showing the individual records that compose that aggregate.

For each visualization, specify: chart type, axes, legend, color encoding, and what interaction (click, hover, filter) is supported.

6. Drill-Down Paths

Define how users navigate from summary to detail:

MRR Summary Card → MRR by Region (bar chart) → Region Detail Table → Individual Subscription Record

At each level, specify what filters carry forward and what new dimensions become available.

7. Refresh Schedule

Data Source	Refresh Frequency	Method	SLA	Failure Handling
billing.subscriptions	Every 6 hours	Incremental ETL	Data available by :30 past	Retry 3x, alert #data-ops after failure
crm.customers	Daily at 02:00 UTC	Full sync	Data available by 03:00 UTC	Stale data badge shown on report

Specify what the user sees when data is stale — a timestamp, a warning banner, or a fallback to the last successful refresh.

Quality checklist

Before delivering the report design, verify:

• Every metric has a precise written definition with includes/excludes
• Formulas are unambiguous — another analyst could reproduce the number from the formula alone
• Data sources are identified with table names, join logic, and known data quality issues
• Filters specify defaults, options, and cross-filter behavior
• Drill-down paths are defined from summary to the most granular level
• Refresh schedule includes SLA, failure handling, and staleness indicators
• The report answers the stated business question — not adjacent interesting questions
• Null and edge case handling is documented (zero-division, missing dimensions, partial periods)

Common mistakes to avoid

• Undefined metrics. "Revenue" means different things to finance, sales, and product. Write the SQL-level definition. If two stakeholders disagree on the definition, that is a conversation to have before building the report, not after.
• Too many metrics on one report. A report with 20 KPIs answers no question well. Limit to 3-7 primary metrics that directly answer the stated business question. Link to secondary reports for everything else.
• No drill-down path. A summary number without the ability to investigate why it changed is useless. Every aggregate needs a path to the underlying records.
• Ignoring data freshness. Users will make decisions based on stale data if you do not make freshness visible. Always show the last-refreshed timestamp on the report.
• Filters that mislead. A date filter that only filters one chart but not another on the same report causes users to draw wrong conclusions. Document exactly what each filter affects.