Data verification services refer to the process of checking and confirming the accuracy and quality of data in a company’s databases or systems. As data grows in volume and importance for businesses, having trusted and validated information is crucial. Faulty data can lead to incorrect analysis, operational issues, compliance problems, and poor decision making. That’s why organizations utilize data verification to ensure completeness, validity, accuracy, and consistency of their data.
Some key questions around data verification include:
Why is data verification important?
Data verification is critical because it directly impacts data quality. Low quality data leads to a range of issues:
– Inaccurate reporting and analytics – Faulty data produces incorrect reports and statistics used for vital business insights and strategy. This can lead to poor decision making.
– Lower productivity – Employees waste time working with incorrect or duplicate data. It also takes longer to find the right information.
– Poor customer experiences – Incorrect customer or product data leads to frustrations and errors in customer service.
– Non-compliance – Many regulations require companies to have accurate financial, customer, and other data. Non-compliant data leads to legal and reputational risks.
– Higher costs – Bad data requires extra work to cleanse and manage. It can also result in penalties, lost revenue, and angry customers if problems occur.
What methods are used for data verification?
There are various techniques and approaches used to verify the quality of business data:
– Manual verification – Humans review samples of data for accuracy and completeness. This provides a high level of inspection but doesn’t scale well.
– Rules-based checks – Validation rules are applied to data sets to identify issues like duplicates, incorrect formats, or missing values. Parameters and requirements can be tailored to specific data types.
– Statistical analysis – Aggregate statistics like means, distributions, and patterns are generated on data fields. Unexpected deviations or anomalies may indicate inaccurate information.
– Matching algorithms – Data fields are compared to external sources to verify accuracy. This could include checking addresses against postal service APIs.
– AI and machine learning – Advanced models can be trained to evaluate data sets for inconsistencies and errors. This allows automation for large volumes of data.
– Audits – Formal data audits involve systematic sampling and checking of records against source documents. Audits provide verified results but require significant manual effort.
What types of data validation checks are commonly used?
Typical validation checks on data sets include:
– Format checks – Verifying data is in the correct format like dates, IDs, phone numbers, zip codes.
– Completeness checks – Confirming required fields have values and have not been left blank.
– Consistency checks – Validating uniformity of related data across records, like customer names and addresses.
– Referential integrity checks – Ensuring foreign keys and identifiers link properly across associated tables and databases.
– Limit and validation checks – Confirming data falls within expected ranges, like ages over 0 and under 150.
– Duplicate checks – Identifying and removing duplicate records in a database or data set.
– Business logic checks – Verifying data aligns with business rules, such as dates falling within valid time periods.
How can automation and AI improve data verification?
Automation and artificial intelligence technologies are playing an increasing role in data verification processes:
– Large volume capabilities – AI models can process huge amounts of data for validation at high speeds and massive scale.
– Increased accuracy – Machine learning algorithms can be highly accurate at pattern recognition and identifying data anomalies.
– Reduced manual effort – Automation handles many validation tasks previously requiring tedious and repetitive human verification.
– Ongoing monitoring – Automated systems enable continuous data monitoring rather than periodic auditing. Issues can be identified in real-time.
– Customizable rules – Machine learning models allow validation rules to be tailored and evolved based on specific data needs.
– Improved efficiency – AI reduces the time and labor required for organizations to keep their data verified and up-to-date.
What are the limitations of data verification?
While data verification is crucial, there are some limitations to consider:
– Not real-time – Verification is typically done periodically in batches. There can be lag between data issues arising and detection.
– Sampling – It’s often not feasible to verify 100% of records. Sampling limits full coverage.
– Resource intensive – Significant human and technology resources are required for robust verification capabilities.
– Diminishing returns – There is a point of diminishing returns where more verification provides little incremental value.
– Can’t prevent bad data – Verification uncovers problems but doesn’t directly prevent bad data from entering systems.
– Subjectivity – Some checks require human judgement which may be subjective or prone to error.
– Can’t assess context – Checks often evaluate data in isolation and may miss contextual factors that affect accuracy.
Common uses of data verification
Data verification is applicable across many core business functions and use cases:
Finance
Financial data must be meticulously accurate for reporting. Verification ensures key master data like customer and material records are correct. Transactional data is checked to match amounts and ledgers. Data validation prevents accounting errors and non-compliance.
Sales
Customer, product, and pricing data directly impacts sales processes. Data verification aligns catalog and CRM data with inventory systems. Valid address data improves deliveries and logistics. This helps gain customer trust and satisfaction.
Procurement
Supplier and material master data used for purchasing must be verified. Purchase order information is checked pre and post-transaction. Accurate procurement data prevents over-ordering, receipt issues, and incorrect payments.
HR
Employee master data like contact info, salaries, and job roles are verified. Payroll and timekeeping data is validated to ensure proper pay and adherence to regulations. Complete employee data enhances the hiring process.
Legal
In litigation scenarios like fraud, disputes, and regulatory actions, verification of involved transactional records is crucial. Checking data validity supports legal claims and defenses with reliable facts.
Healthcare
Patient medical records and treatment information can literally be a matter of life and death. Extensive validation ensures completeness and accuracy of sensitive personal health data.
Manufacturing
Correct specifications for products, equipment, materials, and bill of materials are essential in manufacturing. Data verification aligns engineering, inventory, production, and quality data to minimize defects.
Challenges with data verification
While clearly beneficial, reliable data verification poses a variety of practical challenges:
Volume of data
Modern data sets can contain millions or billions of records. Verifying entire data lakes can be unfeasible. Intelligent sampling and modelling must be used.
Variety of data
Organizations have many types of structured and unstructured data in different formats. Tailored verification methods are needed for different data.
Velocity of data
High velocity streaming data introduces verification challenges. Techniques like probabilistic verification on samples are required.
Legacy systems
Monolithic legacy systems often lack native data quality features. New verification layers must wrap around aging infrastructure.
Distributed systems
Disconnected, siloed systems make holistic verification difficult. Data mappings must reconcile disparate sources.
Cost
Heavy manual verification procedures are expensive. Automation requires investment in skills and technologies. Funding data quality initiatives is hard.
Governance
Data verification projects need stakeholder support, dedicated resources, and oversight to sustain them. Change management is key.
Best practices for data verification
Some proven ways organizations can optimize their data verification processes and quality programs include:
Upstream verification
It’s easier to prevent bad data than fix it later. Checks during data entry stop errors at the source.
Embedded verification
Build in validation codes and rules directly into transactional systems and databases to automate checks.
Monitoring + alerting
Use dashboards and notifications to monitor verification statuses and alert on issues found.
Master data foundation
Maintain “golden records” for critical master data like customers and products.
Reference data governance
Standardize reference data like location lists across applications.
Data quality team
Establish a dedicated cross-functional data quality team to improve verification.
Issue library
Log common data issues found through verification for root cause analysis.
Ongoing verification
Make verification an ongoing process, not a one-off project.
Verification automation
Automate where possible for efficiency, consistency and scale.
Conclusion
Data verification is a crucial process that confirms the quality of organizational data. It provides the foundation for trust in business data. Verification checks validate the accuracy, completeness, and integrity of data used across functions like sales, finance, production, HR and more. Techniques range from manual reviews to statistical analysis to AI-powered automation. There are some inherent challenges to large-scale verification including data volumes and system legacies. However, a focus on the right people, processes and technologies can enable solid data verification capabilities and data quality. With reliable information, companies can have confidence in reporting, analytics, and most importantly, decision making.