High-quality data supports:

• Reliable decision-making
• Improved analytics
• Stronger customer trust
• Enhanced regulatory compliance
• Reduced operational costs

Without data quality controls, organizations face risks like reporting inaccuracies, compliance failures, and wasted resources.

A data quality checklist is a structured framework for evaluating data integrity, completeness, consistency, and accuracy. It helps ensure your data is fit for reporting, decision-making, and compliance. By following a checklist, organizations can:

• Establish systematic quality control procedures
• Identify potential data issues early
• Apply corrective measures before errors impact outcomes

Using a data quality checklist delivers numerous beneficial advantages for organizations committed to data excellence:

• Improved decision-making with accurate, trustworthy data
• Reduced costs by preventing expensive data errors
• Stronger compliance with regulatory standards (IFRS, GAAP, SOX)
• Increased efficiency by reducing time spent fixing data issues
• Better customer experiences through accurate insights
• Enhanced analytics with clean data inputs
• Greater stakeholder confidence
• Stronger data governance and accountability

Use a checklist whenever you’re:

• Closing financial periods (month-end, year-end)
• Submitting reports to stakeholders or regulators
• Migrating or integrating systems (ERP, BI tools)
• Automating workflows or reporting
• Identifying anomalies or reconciliation issues
• Driving ongoing data governance initiatives

The dimensions of data quality are standardized criteria used to evaluate the various aspects of reliability, accuracy, and usability of data within an organization. These dimensions help ensure that data is fit for its intended purpose and supports effective decision-making. The most commonly recognized data quality dimensions include:

1. Accuracy: Measures how closely data reflects the real-world values it represents. Inaccurate data can lead to faulty insights and poor decisions.
2. Completeness: Indicates whether all required data is present. Missing values or records can compromise analysis and reporting.
3. Consistency: Ensures uniformity of data across different systems or datasets. Inconsistent data can result in duplicate records or conflicting reports.
4. Timeliness: Refers to whether data is current and available when needed. Outdated information can reduce the value of insights and hinder timely decisions.
5. Validity: Checks if data conforms to defined formats, rules, or standards (e.g., date fields contain valid dates). Invalid data can cause system errors and compliance issues.
6. Uniqueness: Ensures that each record is distinct and not duplicated. Duplicate entries can inflate metrics and obscure true values.

These dimensions form the foundation of any effective data quality checklist. By evaluating data against each dimension, organizations can systematically identify issues, maintain high-quality datasets, and build trust in their analytics and reporting functions. The relevance of these dimensions is crucial in ensuring that data quality assessments are comprehensive and meet organizational needs.

Creating a data quality assessment is the process of reviewing your data to ensure it’s accurate, complete, and reliable for reporting and decision-making. For finance teams, this is especially important before closing periods, audits, or implementing automation. Specific tasks involved in performing a data quality assessment include recurring checks during basic and advanced data reviews to ensure data accuracy and overall quality management.

1. Define your goal and scope: Decide what you’re assessing (e.g., GL, AP, reconciliations) and why (e.g., audit, reporting, migration).
2. Identify key data fields: Focus on critical fields like account codes, dates, amounts, and tax rates.
3. Check data quality dimensions: Evaluate completeness, accuracy, consistency, timeliness, validity, and uniqueness using a checklist or tool.
4. Log and fix issues: Document errors, prioritize fixes, and correct them at the source or downstream systems.
5. Monitor quality over time: Incorporate checks into recurring workflows to maintain clean, trusted data.

Regular assessments help ensure your data is fit for purpose, supports compliance, and builds trust in financial reporting.

A data quality audit is a systematic review of your datasets to evaluate their accuracy, completeness, consistency, validity, and timeliness. Unlike a simple data check, a data quality audit involves in-depth analysis of data sources, governance processes, and compliance requirements to ensure your data meets internal standards and external regulations (such as SOX, IFRS, or GDPR).

Conducting regular data quality audits helps organizations uncover hidden issues, improve data governance, reduce compliance risks, and ensure reliable analytics and reporting. Using a structured data quality checklist makes the audit process more efficient and consistent.

According to industry research, poor-quality data costs organizations an average of $12.9 million per year. A lack of data quality controls can lead to:

• Financial reporting errors
• Compliance failures (SOX, IFRS, GAAP)
• Increased operational costs
• Misguided business strategies

A data quality audit or data quality assessment ensures your data meets critical standards before it feeds into analytics, reports, or decision-making. With a structured quality checklist, you can identify data gaps, correct inaccuracies, and build trust across your teams.