Companies rely heavily on accurate and reliable data for critical decision-making processes for their customers and businesses. According to the analyst firm Gartner, the cost of poor data quality is staggeringly high, costing organizations an average of $12.9 million every year.
A notable example of how minor data errors can quickly spiral out of control happened at Samsung Securities, which was known as the $105 billion "Fat-Finger” error. An employee's accidental keystroke led to the issuance of billions of shares to employees during a routine dividend payout. This mistake created 2.8 billion "ghost shares," which were approximately 30 times the total number of shares actually in existence.
For data to be truly valuable, it must be of high quality — accurate, consistent, and reliable. Maintaining high data quality is essential as it ensures the data's usability, leading to better business outcomes and informed decisions.
But data quality is only one piece of the puzzle. Organizations must also adhere to strict regulatory standards. These regulations protect sensitive information, ensure data accuracy, and guarantee its proper use. By promoting transparency and accountability, these standards help organizations manage data responsibly and securely, safeguarding both individual privacy and business interests. Poor data quality can lead to serious compliance risks, including hefty fines and reputational damage.
This article will delve into the critical role of data quality in regulatory compliance and offer practical steps and tools to achieve it.
Understanding the Regulatory Compliance Landscape
Businesses must follow established standards to ensure their operations meet legal and ethical requirements. When it comes to data and data quality, regulatory compliance mandates that companies maintain accurate, complete, and reliable data. This is not just a technical requirement but a legal obligation. Regulations such as the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States enforce strict data quality standards.
These regulations require organizations to implement frameworks for data governance for compliance, ensure data integrity, protect data privacy, and regularly audit their data management processes. Non-compliance with regulatory standards can lead to significant fines, legal actions, and reputational damage. Understanding and implementing regulatory compliance in data quality is crucial for businesses to avoid risks and maintain trust with stakeholders.
Key regulatory compliance standards
Below are the most common — and most sweeping — regulatory standards that companies must comply with:
GDPR: The General Data Protection Regulation (GDPR) mandates that organizations ensure the accuracy and security of personal data within the European Union. Non-compliance can result in severe fines, emphasizing the importance of maintaining high data quality standards.
SOX: The Sarbanes-Oxley Act (SOX) requires companies to implement internal controls and procedures for financial reporting to ensure data accuracy and reliability. It emphasizes the need for transparency and accountability in financial data management.
CCPA: The California Consumer Privacy Act (CCPA) enforces strict data quality standards to protect consumers' personal information in California. Companies must ensure data accuracy and allow consumers to access, correct, or delete their data upon request.
HIPAA: The Health Insurance Portability and Accountability Act (HIPAA) sets stringent standards for protecting and confidentially handling health information. To comply with HIPAA regulations, healthcare organizations must ensure the accuracy and completeness of patient data.
OSHA: The Occupational Safety and Health Administration (OSHA) requires accurate data collection and reporting on workplace safety incidents. Businesses must maintain precise records to ensure compliance and promote a safe working environment.
FDA: The Food and Drug Administration (FDA) mandates strict data quality standards for the pharmaceutical and food industries to ensure public safety. Companies must ensure the accuracy, completeness, and traceability of their data related to products and clinical trials.
EPA: The Environmental Protection Agency (EPA) enforces regulations that require accurate environmental data reporting to protect human health and the environment. Companies must ensure data integrity and reliability in their environmental assessments and reporting processes
The Role of Data Quality in Compliance
The role of data quality in compliance is pivotal, as high-quality data ensures that organizations meet regulatory data standards effectively. Accurate, complete, and reliable data enables businesses to produce precise reports, make informed decisions, and maintain transparency, all of which are crucial for compliance. Poor data quality can have serious negative reputational, as well as monetary impact on businesses. For instance, an IBM study found that poor data quality costs the U.S. economy around $3.1 trillion annually, underscoring the financial implications of data inaccuracies.
Poor data quality can garner severe regulatory consequences. For example, in 2018, Marriott International faced a $124 million fine under GDPR for data breaches resulting from inadequate data management.
Ensuring data quality is not just a best practice but a legal data requirement necessary for regulatory compliance. High-quality data helps prevent costly fines, legal repercussions, and damage to an organization's reputation, thereby supporting sustainable business operations.
Key Components of Data Quality
Completeness
In regulatory compliance, completeness refers to ensuring that all required data elements are captured and available for reporting and audits.
For example, under GDPR, organizations must provide comprehensive records of personal data processing activities. Missing data can lead to incomplete submissions and potential violations, resulting in hefty fines. Completeness is vital for presenting a full picture of business operations and adhering to regulatory data standards.
Consistency
Consistency in regulatory compliance means maintaining uniform and reliable data across all systems and platforms to ensure accurate reporting and auditing.
For instance, under SOX compliance, financial data reported to regulators must be consistent across internal and external reports. Inconsistent data can lead to significant compliance errors and undermine the credibility of the reports. Consistency ensures that data presented to regulators is reliable and uniform, reducing the risk of compliance issues.
Accuracy
Accuracy in the context of regulatory compliance involves ensuring that data accurately reflects the true state of affairs and meets legal data requirements.
For example, in the context of HIPAA, maintaining the accuracy of patient records is crucial to avoid legal ramifications and ensure patient safety. Implementing stringent data validation processes helps meet legal data requirements and prevents costly regulatory fines.
Timeliness
Timeliness refers to having data available and up-to-date in accordance with regulatory deadlines and requirements.
For example, under the CCPA, organizations must respond to data access requests within a specific timeframe. Failing to provide timely data can result in penalties and non-compliance. Ensuring data is up-to-date and available when needed supports compliance monitoring and enables organizations to submit accurate reports within the stipulated time frames.
Steps to Ensure Data Quality for Regulatory Compliance
Assess current data quality
Conduct comprehensive audits to identify data inconsistencies and inaccuracies. This initial assessment helps establish a baseline for improvements and compliance.
Implement data quality rules and policies
Define clear standards for data entry, processing, and storage to ensure consistency. Establish procedures for ongoing data quality management to maintain these standards.
Monitor and maintain data quality
Perform regular checks to detect and address data issues promptly. Continuously review and update practices to adapt to changing requirements and maintain high data quality.
Issue identification
Detect and document data quality problems using both automated tools and manual reviews. This process ensures that issues are captured accurately for further analysis.
Root-cause analysis
Investigate the underlying causes of data issues to understand their origin. Analyze processes and systems to identify factors contributing to the problems.
Recommendations and remediation
Develop solutions based on the root-cause analysis to address identified issues. Implement corrective actions and verify their effectiveness in resolving the problems.
Issue resolution
Resolve data quality problems through targeted remediation efforts. Confirm that the solutions effectively address and rectify the issues identified.
Audit trail of issues and remediation actions
Maintain detailed records of all issues and remediation steps for transparency and accountability. This documentation provides a reference for compliance and future audits.
Challenges and solutions in maintaining data quality for regulatory compliance
Implementing data compliance effectively offers numerous benefits, but it also presents several significant challenges:
Evolving regulations
Staying current with continuously changing laws and regulations across various jurisdictions can be challenging. As new protections for consumers are introduced, companies must adapt to meet these updated requirements. As the technological space changes, there are new additions and modifications done to the existing regulatory compliances which the organization need to keep up with and implement as and when the compliances are changed.
Managing large data volumes
The explosion of Big Data means companies are now handling more information than ever before. This vast amount of data complicates the task of maintaining compliance. Global data is projected to exceed 180 zettabytes by 2025, according to Statista. This massive volume of data must be effectively stored, managed, and secured to meet regulatory compliance standards, enabling organizations to extract valuable information, insights, and business reports. As data consumption grows, the importance of maintaining robust data management practices will only intensify.
Technological advances
Emerging technologies, such as cloud computing, AI, and IoT, introduce new privacy and security challenges, complicating compliance efforts. These technologies introduce new privacy and security challenges, complicating compliance efforts. These technologies rely on vast amounts of data, often including sensitive information, and they continuously evolve using data, algorithms, and analytics. Thus, maintaining regulatory compliance is crucial in this ever-changing technological landscape to ensure data security and privacy.
Shortage of expertise
There is a pressing need for professionals adept in technology and data compliance. The demand for such expertise often exceeds the supply. Securing the data and ensuring that data meets regulatory requirements requires expert skilled professionals, the world is facing a shortage of such professionals, Cybersecurity workforce shortage has reached 4 million, according to a report by ISACA, 54% of technical data roles are unfilled and 44% of legal compliance roles remain unfilled due to demand and supply gap. Training the existing workforce and finding/creating new talents is the need of the hour to meet this gap.
High costs
Compliance can be costly, requiring substantial investments in technology upgrades, training, and potentially hiring additional staff or consultants. Ongoing audits and assessments add to these expenses. Data compliance comes with a cost as it involves maintaining infrastructure, hiring expert workforce, licensing costs etc. According to a report released by PwC, eighty-eight percent of global companies report that GDPR compliance costs their organization over $1 million annually, with 40% spending more than $10 million each year.
Cross-border data transfers
In today's interconnected world, data operates as a single digital entity, making it challenging for multinational companies to navigate the complex and often conflicting data protection laws across various countries. For instance, Facebook, used globally, must comply with each country's unique data protection and compliance regulations. This requires Facebook to adhere to diverse legal standards for data management and collection, ensuring compliance across all regions where it operates.
Data mapping difficulties
Companies must accurately track and classify their data across multiple systems and databases to ensure compliance, which can be complex. Large product based companies often has a number of systems and databases from which they source thier data, each of these systems/databases might have different regulatory data compliance incorporated depending upon the nature of data, mapping these different kinds of data and bringing them under an umbrella of a single or set regulatory compliance standards in a difficult and complex task.
Third-party compliance
Organizations often work with third party vendors to leverage their expertise and manpower, Ensuring that third-party vendors also adhere to relevant regulations adds another layer of difficulty. The vendors need to be monitored, audited and compliance agreement has to be set and agreed by both the parties to ensure that compliance standards are adhered.
Risk of data breaches
Increasing cyber threats heighten the challenge of securing data and maintaining compliance. As per report by Cybercrime Magazine, Cybercrime will cost the world $10.5 trillion by 2025. Such data breaches not only result in monetory losses but it also exposes sensitive data to attackers which can later be used for fraudulent activities.
Outdated systems
Legacy systems often lack the features needed to meet modern compliance standards, making updates and adaptations difficult. As technology evolves and compliance standards are updated to address new threats, some systems may fail to meet these revised standards. The cost of redesigning or upgrading these legacy systems can be prohibitively high. However, if these systems are not updated, they become vulnerable to data breaches and can attract hefty fines from regulatory bodies.
Data observability solutions like the Acceldata Data Observability Cloud (ADOC) platform ensure that customers adhere to the latest infrastructure standards, best practices, and data quality techniques. They also provide ongoing compliance and operational efficiency across various vendors, pipelines, and cloud platforms, continuously optimizing spending and performance.
Achieving Regulatory Compliance through Robust Data Quality Practices
Achieving regulatory compliance is a continuous effort that demands an unwavering focus on data quality. Organizations can meet regulatory data standards and mitigate risks by utilizing advanced tools such as the Acceldata Observability Platform and adhering to industry best practices.
Acceldata provides real-time monitoring and alerts for data anomalies, schema drift, and quality threshold breaches. It helps enterprises optimize pipelines, warehouses, and clusters by identifying data issues, infrastructure failures, timeouts, and dependency errors. With real-time insights into data health, businesses can fine-tune performance, establish best practices from usage patterns, and quickly pinpoint the root cause of performance issues in their cloud platforms.
High-quality data is crucial for effective compliance monitoring, ensuring that all legal data requirements are met and reducing the likelihood of regulatory penalties. Furthermore, maintaining high data quality supports operational efficiency by streamlining processes and enhancing decision-making. Consistently investing in data quality not only helps organizations stay compliant but also fosters a strong reputation with regulatory authorities, contributing to long-term success and reliability.
Summary
Data quality is pivotal in regulatory compliance. Ensuring completeness, consistency, accuracy, and timeliness of data helps organizations meet regulatory requirements. Utilizing tools like Acceldata can streamline this process, providing comprehensive data observability and improving overall data management.
By following these strategies and utilizing advanced tools, your organization can navigate the complex regulatory landscape with confidence, ensuring compliance and building trust with stakeholders.
Schedule a demo of the Acceldata platform and explore how its observability can enhance your data quality and help you effortlessly achieve compliance.
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