DQC for HR Data
Streamline HR operations by ensuring clean and reliable HR data
Move beyond one-time manual fixes for bad HR data. Instead, ensure automatically and continuously clean, reliable HR data.
DQC IMPACT
Don’t let bad master data hurt your HR operations. Fix and maintain high-quality HR data sustainably.
Daily challenge
In fast-paced HR environments, employee data is often entered quickly by teams focused on day-to-day tasks—not data accuracy. But this information underpins critical processes like payroll, reporting, and compliance.
Risks and losses
Unreliable HR master data leads to serious business risks: payroll errors, compliance violations, inaccurate headcount reporting, onboarding delays, audit findings, and loss of employee trust.
Misleading workforce numbers distort planning, budgeting, and KPIs.
Incorrect or outdated data results in underpayments, overpayments, or missed payments.
Incomplete or inconsistent data slows down access setup, training, and productivity.
Missing or incorrect employee records increase the risk of regulatory breaches and fines.
Inaccurate role, skill, or location data leads to staffing gaps or inefficiencies.
Poor documentation and inconsistent records trigger findings during internal or external audits.
HR teams spend excessive time fixing data issues instead of focusing on people.
Incomplete or inconsistent data undermines insights into retention, performance, and DEI metrics.
Errors in records, benefits, or payroll damage trust and reduce engagement.
Inaccurate HR reporting erodes trust in people data and HR-driven strategies.
Success with DQC Platform
- 100% data quality fit-for-purpose
- 23x+ faster issue remediation
- ~1M€ cost saving in year 1
Calculate the cost of bad HR Data
Works where you work
including HRIS, HCM, and ATS
Built on 3 pillars
DQC Platform for 100% fit-for-purpose HR data
1) Find data issues with AI.
- Set up data quality rules with the help of DQC AI agent
- Import any rules, requirements, or issue descriptions in natural language or as code in seconds
- Find issues in the data and let the AI agent document everything for you
- Duplicate employee entries
- Circular reporting lines
- Missing or outdated job titles
- Inconsistent employee identifiers
- Incorrect or outdated contract types
- Typos in key fields
- Unassigned or invalid manager roles
- Outdated or inactive org. units
- Mismatched work location & country
- Incomplete offboarding data
2) Fix data at source with AI + human experts.
- Generate AI suggestions for data corrections and enhancements
- Fix issues at source with subject matter experts in full control
- Track the change history for complete visibility
- Observe data quality improve over time
3) Prevent issues at source.
- Use all DQC data quality rules via API or SDKs
- Embed the DQC data quality rules directly in your product management system
- Prevent data issues in real-time in source systems Stop bad data from flowing through your data pipelines / ETL processes
Companies Can Generate Value by Improving Their Data
Dealing with data quality issues at the source lets businesses start with a strong foundation and make the most of GenAI, DQC’s Dr. Michael Spira explains.
Data quality made in Germany
Secure & responsible - DQC is headquartered in Munich, Germany. We prioritize data security and user control. Fully GDPR compliant. The DQC Platform is available as SaaS, or private cloud deployments and your data always remains in your systems. No copies are made. Also, the DQC Platform only needs reading rights. Finally, enterprises can bring their own LLMs.
Act now!
Start treating HR data as an asset, today. Learn how AI agents can help you improve your HR data.