Tables and pivot reports provide flexible analysis capabilities, allowing you to reorganize and analyze your financial data across multiple dimensions. Unlike pre-formatted statements (balance sheet, ...
Last updated Feb 18, 2026 · 5 min read
A pivot report is a dynamic data analysis tool that reorganizes transactional data into a structured format:
This structure provides insights that standard financial statements don't show.
To create a custom pivot report:
Light displays your pivot report with drill-down capabilities.
Available row dimensions depend on your data:
Accounts: Organize by GL account. Shows revenue, expense, asset, liability by account.
Account type: Group by asset, liability, equity, revenue, expense. Useful for high-level analysis.
Account code: Organize by account code prefix for hierarchical analysis.
Cost centers: Show data by department or operational unit.
Business partners: Segment by customer or vendor. Useful for customer profitability or vendor spend analysis.
Custom properties: Organize by any custom attribute you've tagged transactions with (e.g., project, product line, geographic region).
Document type: Analyze by transaction type (AP, AR, JE, etc.).
Ledger: Segment by ledger if you operate multiple ledgers.
Tip: Use business partner dimension to analyze your top 10 customers or vendors and their contribution to profit or spend.
Available column dimensions:
Period: Group by day, week, month, quarter, or year. Most common for time-series analysis.
Entity: Segment by legal entity. Useful for multi-entity companies analyzing which subsidiary contributes to profit.
Currency: Show data in transaction currency, local currency, or group currency.
Tax code: Organize by tax treatment. Useful for tax compliance analysis.
Custom properties: Organize by attributes you've assigned to transactions.
Multiple column dimensions create a cross-tabulated structure.
Revenue by customer and period: Revenue (rows) × Month (columns) = monthly revenue by customer
Shows which customers drive revenue and seasonal patterns.
Expense by cost center and month: Expense accounts (rows) × Month (columns) = monthly spending by department
Identifies high-spending departments and cost control opportunities.
Receivables aging by customer: Customer balance (rows) × Age bucket (columns) = AR aging by customer
Shows collection efficiency and credit risk.
Payables aging by vendor: Vendor balance (rows) × Age bucket (columns) = AP aging by vendor
Identifies payment obligation timing.
Profit by product and entity: Product line (rows) × Entity (columns) = profit contribution by product in each subsidiary
Identifies which products and entities are profitable.
Light provides two similar but distinct capabilities:
Table reports: Simplified structure with a single row dimension, columns for periods/entities, and simple calculations. Easier to create for basic analysis.
Pivot reports: More powerful with multi-dimensional analysis, custom aggregations, drill-down, and filtering. Better for complex questions.
Start with table reports for straightforward questions. Use pivot reports when you need cross-tabulation or complex analysis.
Constrain pivot data to specific dimensions:
Filters reduce the data set to focus on relevant dimensions.
Pivot reports support drilling down to transaction detail:
This enables rapid investigation of unusual values or exceptions.
Add calculations to pivot reports:
Calculated columns: Create new columns that perform calculations:
Calculated rows: Create new rows:
Light supports common calculations and custom formulas.
Analyze year-over-year or period-over-period trends:
This is useful for assessing business momentum and identifying problem areas.
Format your pivot report for presentation:
This improves readability and presentation quality.
Save your pivot report template for reuse:
Retrieve the report by name anytime:
Schedule recurring reports:
Export pivot data for external use:
Excel export preserves your formatting and pivot structure for further analysis.
Performance: Large pivot reports (>100k rows × many columns) may load slowly. Filter data or reduce dimensions.
Data quality: Pivot analysis is only as good as your source data. Ensure consistent account mappings, cost center assignment, and business partner coding.
Custom properties: If you rely on custom properties for analysis, ensure all transactions are tagged correctly.
Documentation: Document your pivot report definitions (what rows, columns, filters mean) so others can understand and reproduce them.
Tip: Create a library of standard pivot reports your team uses regularly (revenue by customer, expense by department, profit by product).
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