Messy data in Workday is not just an inconvenience—it breaks reports, creates compliance risks, generates duplicate records and erodes trust in the system. Whether it is worker data with missing managers, inconsistent job profiles and duplicate contingent workers, or financial data with orphaned journals, mistagged Worktags and unreconciled balances, data quality problems compound over time if not addressed systematically.
This roadmap walks through how to diagnose, fix and prevent data quality issues so your Workday tenant stays clean and trustworthy.
Phase 1: Diagnose—know what is broken
You cannot fix what you do not measure. The first step is identifying where data quality has degraded.
Worker data diagnostics:
Run reports or use custom queries to find:
- Workers missing critical data: no manager, no cost center, no location, no hire date.
- Organizational inconsistencies: workers reporting to terminated managers, or supervisory orgs with zero members.
- Duplicate or near-duplicate records: same name, SSN or employee ID appearing multiple times.
- Stale data: terminated workers still active, positions filled but marked vacant, contingent workers never ended.
- Job and compensation anomalies: job profiles inconsistent with actual role, base pay = $0 or absurdly high.
Financial data diagnostics:
Run validation checks to find:
- Journals with missing or invalid Worktags: no Cost Center, invalid Project, or Worktag combinations that violate rules.
- Unbalanced or orphaned journals: journals that did not post, or posted but left balances hanging.
- Supplier and customer data gaps: missing tax IDs, invalid bank accounts, duplicate vendors.
- Asset and project mismatches: capitalized assets with no supporting project, or projects with costs but no asset.
- Unreconciled accounts: GL accounts with unexplained variances, or subledger balances that do not tie to GL.
Use data quality dashboards to track metrics over time: percentage of workers with complete profiles, percentage of journals with valid Worktags, open exceptions by type.
Phase 2: Fix—clean up the mess
Once you know what is broken, prioritize fixes based on impact: compliance risks first, then reporting accuracy, then operational annoyances.
Worker data fixes:
- Fill critical missing fields
- Use bulk EIBs or mass updates to populate missing managers, cost centers, locations and hire dates from authoritative sources (HRIS, payroll).
- Resolve duplicates
- Merge duplicate worker records where possible; if not, terminate or inactivate incorrect ones and update downstream references.
- Correct organizational misalignments
- Reassign workers to active managers, fix supervisory org memberships, and retire obsolete org structures.
- Terminate stale records
- End contingent worker contracts that should have expired, terminate workers who left but were never properly exited.
Financial data fixes:
- Correct Worktag errors
- Use journal adjustments or edit unposted transactions to assign valid Worktags (Cost Center, Spend Category, Project).
- Reconcile and close orphaned items
- Identify unbalanced journals, reverse and repost them correctly; clear construction-in-progress for completed projects.
- Clean supplier and customer master
- Deduplicate vendors, fill missing tax IDs, update banking details and retire inactive accounts.
- Validate asset and project linkages
- Ensure all capitalized assets trace to source transactions; close completed capital projects and move costs to fixed assets.
Document every cleanup step: what was changed, why, and who approved it. This creates an audit trail and prevents repeating the same cleanup next quarter.
Phase 3: Prevent—build quality into processes
Fixing data is expensive and disruptive; preventing bad data from entering in the first place is far better.
Prevention strategies for worker data:
- Validation rules at entry
- Make critical fields required in business processes (Manager, Cost Center, Location on hires; End Date on terminations).
- Use conditional validation to enforce logical rules (for example, Supervisory Org must have an active manager).
- Automated alerts and reports
- Schedule weekly or monthly reports that flag new data quality issues (workers missing data, org mismatches).
- Assign owners to review and resolve flagged items before they accumulate.
- Training and process discipline
- Train HR Partners, Recruiters and Managers on the importance of complete and accurate data entry.
- Use checklists and job aids to guide users through complex processes (hires, reorgs, conversions).
Prevention strategies for financial data:
- Enforce Worktag completeness
- Configure business processes to require all mandatory Worktags before submitting journals, invoices or expenses.
- Use Allowed Worktag rules to prevent invalid combinations at entry, not after posting.
- Posting rule reviews
- Regularly review and test Account Posting Rules to ensure they generate correct GL entries for new transaction types.
- Reconciliation disciplines
- Embed subledger-to-GL reconciliations into monthly close checklists; do not let discrepancies roll forward.
- Master data governance
- Restrict who can create suppliers, customers, bank accounts and Worktags; require approvals for additions or changes.
Prevention turns data quality from a cleanup project into an operational discipline.
Phase 4: Monitor—continuous data quality management
Even with strong prevention, data quality requires ongoing monitoring and governance.
Establish data quality KPIs:
Track and report metrics like:
- Percentage of workers with complete core data (manager, cost center, job profile).
- Percentage of financial transactions with valid Worktags.
- Number of open data quality exceptions by category.
- Time to resolve flagged issues (for example, average days from flag to fix).
Build a data governance framework:
- Assign data stewards for each domain: HR data, org structures, financial master data, Worktags.
- Hold quarterly data quality reviews to assess trends, prioritize cleanup and refine prevention controls.
- Document data standards and ownership so everyone knows what “good data” looks like and who maintains it.
Use automation and tools:
- Leverage Workday’s built-in data validation and audit reports.
- Consider third-party data quality tools or Workday marketplace apps for advanced profiling, deduplication and monitoring.
- Use scheduled reports and dashboards to surface issues proactively instead of reactively.
Continuous monitoring means data quality does not decay between major cleanup projects.
Data cleanup as a capability, not a one-time event
The most successful Workday tenants treat data quality as an ongoing capability, not a periodic crisis.
This means:
- Clear ownership and accountability for data domains.
- Validation and governance embedded in business processes, not bolted on afterward.
- Metrics and dashboards that make data quality visible to leadership.
- Culture of data discipline where users understand that clean data is everyone’s responsibility.
When you move from firefighting messy data to systematically diagnosing, fixing, preventing and monitoring it, Workday transforms from “the system with bad data” to a trusted platform for decision-making.
