A grants management database is only as good as the data in it. Foundations that invest in grants management software but don't invest in data quality end up with systems that are technically sophisticated but practically unreliable — where staff can't trust what they find, reports are inaccurate, and the investment in software doesn't deliver its promised value.
Maintaining high-quality grant data is an ongoing discipline, not a one-time activity. This guide covers the practices that keep grant databases clean, accurate, and useful.
Decision support: Grants staff rely on their database to make decisions — which organisations have we funded before? What was the last grant amount? Are there outstanding compliance issues? Inaccurate data produces bad decisions.
Compliance and audit: Funders' boards, auditors, and sometimes regulatory bodies expect accurate records of grants made. An auditor who finds discrepancies between the database and financial records raises concerns about control environments.
Relationship management: Contact details, relationship notes, and communication history in the database shape how staff engage with grantees. Outdated contacts, duplicate records, and missing relationship notes cause relationship problems.
Portfolio reporting: Accurate reports to boards and donors depend on accurate data. A portfolio report that double-counts grantees or misattributes grants produces misleading analysis.
Learning and evaluation: Analysis of grant portfolio patterns — what types of organisations are funded, at what amounts, with what outcomes — depends on clean, consistent data. Inconsistent categorisation makes analysis unreliable.
Duplicate records: The same organisation appearing twice under slightly different names ("Auckland City Mission" vs "The Auckland City Mission" vs "ACM"). Duplicates distort portfolio analysis and confuse relationship management.
Inconsistent naming conventions: Organisation names entered inconsistently across grants — some with "The", some without; some abbreviated, some spelled out. Consistent naming is essential for deduplication.
Stale contact information: Contact email addresses and phone numbers that are outdated. Staff turnover at grantees means contact information goes stale faster than it's updated.
Missing mandatory fields: Grants entered without key information — sector classification, geographic scope, organisation type — that are needed for reporting. This typically happens because entry is done under time pressure.
Inaccurate dates: Grant start and end dates, report due dates, and payment dates that are incorrect or approximated. Date accuracy is critical for compliance monitoring.
Narrative quality varies: Grant purpose descriptions, assessment notes, and relationship records that vary enormously in detail and quality between grants or staff members.
Grant amounts that don't reconcile: Grant amounts in the database that don't match the grant agreement or financial records. Reconciliation failures undermine financial reporting.
The foundation of data quality is consistent data entry standards:
Organisation naming conventions: Define how organisation names are entered — with or without "The", abbreviated or full, handling of charitable trust names. Document the convention and train staff to follow it.
Required fields: Define which fields must be completed for every grant record. Enforce completion of required fields (either through system validation or review processes) before records are finalised.
Classification taxonomy: Define categories for sector, region, organisation type, and grant type. Use controlled vocabularies — not free text — for fields used in reporting and analysis.
Date accuracy: Enter dates when they're known, not approximated. Document when dates are estimated vs. confirmed.
Documentation standards: Define what documents should be attached to grant records — grant agreement, assessment report, correspondence, reports. Train staff to attach documents consistently.
Preventing duplicates is easier than fixing them:
Search before creating: Build a workflow that requires staff to search for an existing organisation record before creating a new one.
Matching logic: Understand how your system matches organisation records. Some systems match on name; others on registration number. Use the most reliable matching field (registration number, if available).
Regular deduplication reviews: Periodically review records for potential duplicates — especially after data imports, staff turnover, or system migrations.
Merge protocols: When duplicates are found, have a protocol for merging — which record is the master, how linked records (grants, contacts, documents) are handled.
Contacts within organisations move, leave, and are replaced:
Relationship to organisations: Contact records should be linked to organisation records, not stored independently. This maintains the relationship between a person and their organisational context.
Update triggers: Create workflows that prompt contact updates — when a grant application comes in from a known organisation, check whether contacts are current; when a report comes in, note any changes to the signatory.
Separation of departed contacts: Mark departed contacts as such rather than deleting them — their historical involvement in grants is a permanent record. Don't delete; archive.
Primary contact designation: For each grantee organisation, designate a primary contact. This prevents ambiguity about who receives what communications.
Grants management creates permanent records:
Retention policy: How long should grant records be retained? Most legal and audit requirements suggest 7 years minimum; perpetual foundations may want to retain records indefinitely.
Archive vs. delete: Closed grants should be archived (remaining accessible but not appearing in active grant views) rather than deleted.
Historical accuracy: Historical grant records should not be modified to match current policies or classifications. If a historical grant was categorised differently, note the reclassification as separate data rather than overwriting historical data.
Document archiving: Attached documents should be stored in a way that's accessible long-term — not dependent on cloud services that may change or disappear.
Monitor data quality continuously:
Completeness reports: Regular reports showing records with missing mandatory fields, allowing staff to identify and fix gaps.
Consistency checks: Regular checks for inconsistent data — organisation names that appear in multiple forms, grants with amounts that don't reconcile to payments.
Aging reports: Contacts not updated in over a year; organisations with no recent activity; reports overdue for follow-up. These point to records that need attention.
User training: Regular training for grants staff on data entry standards, especially for new staff who inherit databases without having been trained on the conventions used.
Tahua's grants management platform includes data quality tools built in — duplicate detection, required field validation, controlled vocabulary taxonomies, and the reporting that helps grants teams monitor and maintain data quality over time.