Treasury Data¶

Overview¶

Treasury data refers to the strategic value of the data itself — its quality, enrichment, normalization, freshness, and trustworthiness. While Connectivity is about how data arrives, this domain is about what happens to it once it lands: parsing, validation, harmonization, and making it reliable enough to power forecasts, reports, and decisions.

This is Palm's moat. The belief is that valuable, well-structured data — not lock-in — is what wins in future treasury SaaS. Teams that trust their data make better decisions faster.

For detailed ICP context and terminology, see fundamentals.md

Top Jobs & Desired Outcomes¶

Full history: jobs.md

1. Get bank data into a usable format without massive integration projects ✓¶

Desired Outcomes: - Minimize the time from decision to live data (weeks, not months) - Reduce dependency on IT for new bank connections - Increase flexibility to add new data sources without rebuilding

Sources: Feature file (Confirmed — shipped capabilities)

2. Trust the data powering forecasts and reports ✓¶

Desired Outcomes: - Minimize data quality issues that pollute downstream analytics - Increase confidence that all accounts are being captured - Reduce time spent investigating data discrepancies

Sources: Feature file (Confirmed — shipped capabilities)

3. Maintain reliable forecast data despite upstream system limitations ⚡¶

Desired Outcomes: - Minimize data loss from Kyriba's daily delete behavior - Reduce the frequency of missing forecast data when training regional teams - Increase confidence in forecast data accuracy

Source: ON (2025-10-02) - Emerging, moved from cash-forecasting (data infrastructure concern)

Key Pain Points¶

Full history: pain-points.md

Bank connectivity projects take months — integration projects are heavyweight and slow (Feature file)
Different banks, different formats — MT940, CAMT, BAI2, CSV all need harmonization (Feature file)
Data quality issues discovered too late — downstream analytics polluted before anyone notices (Feature file)
No visibility into data freshness — teams don't know when data is stale (Feature file)
Looker/ERP not real-time — dashboard data depends on reconciliation, not live feeds (Sources: On x2)
External reconciliation creates delays — third-party teams create lag between transactions and visibility (Source: On)

Key Opportunities¶

Data as a moat — well-structured, trusted data is the foundation for everything Palm does
Freshness monitoring — proactive alerts when data is stale or missing
Multi-format harmonization — unified schema regardless of bank format
Schema validation at ingestion — catch issues before they pollute analytics
Audit trails — full lineage from source to decision

Open Questions¶

[ ] How do treasury teams currently assess data quality? (manual checks, reconciliation, gut feel?)
[ ] What's the acceptable latency for data freshness in different use cases? (cash positioning vs forecasting vs reporting)
[ ] How important is data lineage/audit trail for compliance vs operational use?
[ ] What role does data enrichment (entity resolution, categorization) play in trust?
[ ] How do teams handle the transition from "good enough" ERP data to trusted analytical data?

Last updated: 2026-02-17 | Sources: 3 transcripts + feature file (view all)