Our treasury team is expanding use of SuiteAnalytics for cash positioning, investment tracking, and FX exposure reporting. As we build more sophisticated workbooks accessing sensitive financial data, we need to establish proper data governance practices to meet regulatory compliance requirements.
The challenge is balancing accessibility for treasury analysts who need flexible reporting with strict controls around who can see bank account details, investment positions, and cash flow forecasts. We’ve had auditors ask about data lineage - how we ensure reported figures trace back to source transactions - and access controls proving only authorized users view certain datasets.
What data governance frameworks have others implemented for treasury management analytics? I’m looking for best practices around access control configuration, maintaining data lineage documentation, and ensuring audit compliance while still enabling the analytical flexibility that makes SuiteAnalytics valuable.
The metadata repository concept is interesting. How granular do you get with documenting transformation logic? Some of our workbooks have complex calculated fields building on other calculated fields. Do you document every intermediate calculation step?
Yes, document every calculation layer, especially for treasury where precision matters. We use a calculation dependency map showing how fields relate: “Net Cash Position = Cash Balance + Short-term Investments - Committed Outflows” where each component is itself a calculated field. This map becomes invaluable when auditors question a variance or when you need to modify formulas without breaking dependent calculations. For really complex workbooks, we maintain a separate documentation workbook that mirrors the production workbook structure but replaces data with formula explanations.
Access control in SuiteAnalytics requires multi-layer thinking. Start with role-based permissions at the dataset level - treasury datasets should only be accessible to treasury roles. Then layer on record-level security using dataset filters that restrict data by subsidiary or account type based on the user’s role. Finally, implement field-level restrictions using custom segments to hide sensitive fields like account numbers from broader finance users who might need aggregate cash data but not account details.
Data lineage documentation is critical for audit compliance. We maintain a metadata repository documenting each workbook: source datasets, transformation logic, calculation formulas, and refresh schedules. For treasury reports, we also document which GL accounts feed each cash position metric and how multi-currency conversion is applied. When auditors request validation, we can show the complete chain from bank reconciliation to treasury dashboard. This documentation lives in a shared drive with version control, updated whenever workbooks change.
Don’t forget about data retention and archival for compliance. Treasury reports often need to be reproducible for regulatory reviews years later. We export monthly snapshots of key treasury workbooks to PDF with embedded data sources, storing them in our document management system. This ensures we can recreate historical reports even if underlying datasets or exchange rates change. Also implement change tracking on your datasets - knowing when fields were added or formulas modified helps explain variances during audits.