DQC for Finance Data
Eliminate inefficiencies in finance processes caused by bad master and transactional data
Stop cleaning up finance master and transactional data in reactive, one-off efforts. Continuously fix and maintain data quality with DQC's AI-powered checks and human control.
DQC IMPACT
Don’t let bad data hurt your finance operations. Fix and maintain high-quality finance data sustainably.
Daily challenge
Finance master and transactional data is entered under time pressure by decentralised teams focused on operations, not data quality—yet the data is critical for many business functions
Risks and losses
Unreliable customer master data hurts the business.
Leads to flawed financial statements and misinformed decisions.
Misclassified or missing data skews forecasts and planning accuracy.
Inconsistent or missing transactional data slows month-end and year-end closing.
Inaccurate data can result in regulatory violations, audits, and penalties.
Errors in invoicing or payments disrupt receivables and payables.
Incomplete customer data weakens risk assessments and collections.
Duplicate vendors or unclear records can result in overpayments.
Finance teams waste time fixing data issues instead of adding value.
Executives rely on flawed data, leading to suboptimal strategies.
Internal and external stakeholders lose confidence in finance outputs.
Success with DQC Platform
- 100% data quality fit for purpose
- 15x+ faster issue remediation
- 1-5M€ cost saving in year 1
Calculate the cost of bad Finance Data
Works where you work
including FMS, TMS, CPM, and ERP
Built on 3 pillars
DQC Platform for 100% fit-for-purpose finance 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 invoices, vendors or GL accounts
- Inconsistent invoice & purchase order information
- Typos or incorrect units generating e.g., outliers
- Outdated cost centers and accounts
- Misaligned chart of accounts
- Incorrect or missing tax and legal identifiers
- Unclear or incorrect posting logic in transactions
- Invalid company relationships
- Inconsistent master data across systems, esp. ERP
- Incorrect currency and exchange rate 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 Finance data as an asset, today. Learn how AI agents can help you improve your Finance data.