Verifying Accuracy, Completeness, And Consistency of Data to Support Reliable Decisions
Ensuring Accurate and Reliable Data with Integrity Audits
Automated Integrity Checks
Automated methods for finding inconsistencies, inaccuracies, and anomalies within a dataset to maintain a high level of quality
Verified Data Comparison (Cross-Referencing)
Comparison of verified data with authoritative (internal and external) sources for veracity.
Error Checking and Fixing Workflows
Methods supported by rules and AI-based processes/information to discover, fix, and/or clean inaccurate or incomplete data.

Alignment with Governance and Compliance Standards
Tools that provide an integrated (governance/oversight) approach to compliance and auditing for data sets
Sectors That We Support
- We have a knowledgeable experienced team in the areas of finance, health care, retail, technology, and research.
- Statswork uses automated and AI driven audit tools to quickly and accurately identify errors, inconsistencies, and anomalies within your data.
- By cross-referencing your data with trusted internal and external sources, Statswork ensures that your data is verified for authenticity.
- The complete audit process can only be done with the end-to-end quality process from the initial assessment to your company’s final compliance review.
- We will implement scalable solutions to provide our clients with an efficient method of handling very large and/or complex data.
- We provide our clients with reliable insights using validated data to improve the accuracy of all analytics and reports, and to improve the decision-making process.
1. Data Review and Analysis
Identify any errors or missing fields.
2. Automated Checks and Validation Against Rules
Detect anomalies in the data and standardize and ensure the uniformity of how the information is presented.
3. Cross-Referencing Data
Validate the data through a review of trusted sources; using both internal and external sources to cross-check the data's validity.
4. Final Review of Quality and Compliance
Ensure that the data is complete, accurate, consistent, and free from errors, before it can be analyzed.
- A process that reviews datasets for accuracy, completeness, and reliability
- Ensures data is free from errors, inconsistencies, and duplicates
- Helps maintain trustworthy information for analytics and decision-making
- Prevents incorrect insights caused by flawed data
- Reduces operational and compliance risks
- Enables accurate reporting, automation, and AI implementation
- Automated checks to detect inconsistencies, duplication & missing values
- Cross-referencing data with trusted internal/external sources
- Compliance-based corrections and standardization workflows
- Finance, Healthcare, Retail, Technology, Research & more
- Any business dealing with large datasets
- Especially useful for regulated sectors requiring data accuracy
- Duplicate or outdated records
- Manual entry errors and formatting inconsistencies
- Unverified or non-compliant data
- Depends on dataset size and complexity
- Automated processes accelerate correction and verification
- Scalable for small datasets to enterprise-level data volumes
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