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
Trusted by
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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Outdated cost centers and accounts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.