Why data quality compliance is required
Many industries, like healthcare and banking, have to follow strict rules. These rules often focus on how they collect and handle customer data, which can include sensitive or critical information. Think about it – a hospital needs accurate patient records to provide the right treatment, and a bank needs correct transaction data to prevent fraud.
Since data is so important for these businesses, they need to make sure they collect and manage it properly. And if a regulatory body comes knocking, they have to be able to prove it. Imagine a patient receiving the wrong medication because of a data error, or a bank misreporting financial transactions – the consequences could be serious. That’s why data quality assurance is crucial. It’s about having systems in place to constantly check the quality of your data, and keeping a history of those checks, especially if something goes wrong and an audit is needed.
You can monitor data quality for free
Before you continue reading, DQOps Data Quality Operations Center is a data quality platform that monitors data quality. It validates compliance by testing data with data quality checks and measures compliance by calculating data quality KPI scores.
Please refer to the DQOps documentation to learn how to start ensuring the quality of your data.
Areas of data quality compliance
When it comes to complying with data quality regulations, there are key areas where organizations need to focus. First and foremost, you need a reliable system for tracking the results of your regular data quality checks. This ensures that you have a clear record of the health of your data over time, which can be crucial in demonstrating compliance during an audit.
Additionally, it’s important to maintain a versioned history of the configuration of your data quality checks. This allows you to track any changes made to the rules and thresholds used to assess your data, providing transparency and accountability. Furthermore, any data quality incidents should be meticulously tracked and linked to corresponding records in other systems, demonstrating that corrective action was taken. Lastly, a robust data quality system should also keep a log of timestamps and user identities associated with any actions taken within the system, enhancing security and providing an audit trail.
Benefits of a compliant data quality process
Maintaining a robust and compliant data quality process isn’t just about ticking boxes for regulatory bodies. It actually translates to tangible benefits that can significantly enhance your organization’s operations and reputation.
First and foremost, in the event of a regulatory audit, you’ll be well-prepared. Instead of scrambling to gather evidence and demonstrate compliance, you’ll have a clear and comprehensive record of your data quality efforts. You can confidently showcase how you’ve been proactively monitoring data quality, identifying issues, and taking corrective action. This level of preparedness not only saves time and resources but also fosters trust and credibility with regulators.
Moreover, a well-defined data quality process ensures that all your data quality requirements are explicitly stated in the form of data quality checks. This empowers your data teams to thoroughly validate any upcoming changes or modifications to your data environment. By proactively identifying and addressing potential data quality issues before they impact your operations, you can prevent costly errors, maintain data integrity, and ensure the reliability of your business processes.
Finally, a compliant data quality process significantly reduces the risk of intentional data leaks. By restricting access to sensitive data and utilizing only approved data quality checks that cannot perform lookups of such information, you establish a robust safeguard against unauthorized access and potential breaches. This not only protects your organization’s valuable data assets but also helps maintain the trust of your customers and stakeholders. If the business is not convinced to invest in data quality, you can engage various data stakeholders, such as data platform owners, and build a strong use case for implementing data quality practices.
Audited data quality process
Maintaining a comprehensive audit trail is fundamental to ensuring transparency, accountability, and traceability within a data quality management system. Every action, whether initiated manually by users or triggered automatically by the data observability platform, is meticulously recorded, creating a robust and verifiable history of the system’s operations.
Auditing data quality scores: The data quality system functions as a continuous monitoring mechanism, capturing and storing data quality metrics at predefined intervals. This ongoing assessment provides a historical record of data quality, allowing for trend analysis, identification of anomalies, and proactive remediation of potential issues. When these metrics are checkpointed, they serve as a data quality audit trail that is a required track record in regulated industries that handle sensitive data.
Auditing configuration changes: Configuration parameters of data quality checks are subject to version control and change tracking. This ensures that any modifications to the rules and thresholds governing data quality assessments are documented and traceable. Such meticulous record-keeping facilitates troubleshooting, auditing, and regulatory compliance.
Auditing check execution: The system maintains a comprehensive log of all data quality-related activities, including manual check executions and automated incident detections. These audit records encompass details such as user identity, timestamps, and specific actions performed, fostering accountability and enabling a thorough examination of system interactions.
Data quality processes and practices
A robust data quality framework necessitates the establishment of well-defined, repeatable processes, adherence to reusable standards, and comprehensive traceability across the various tools and systems involved in data management. This integrated approach ensures that data quality issues can be effectively identified, tracked, and resolved, thereby minimizing their impact on business operations and decision-making.
Data Traceability: In a compliant data quality environment, the ability to trace the origin and lineage of data is paramount. A data quality issue can be traced back to its root cause, often residing in the implementation tasks performed by developers. To facilitate this traceability, all data quality incidents should be systematically linked to corresponding tickets and implementation tasks in project management and issue tracking systems. This enables a seamless flow of information and facilitates efficient collaboration between data quality teams and development teams in resolving issues and preventing their recurrence.
Impact Analysis: Understanding the potential impact of a data quality issue is crucial for prioritizing remediation efforts and mitigating risks. Maintaining a data lineage map that delineates the relationships between upstream and downstream datasets empowers organizations to identify the full extent of an issue’s impact radius. Additionally, assigning criticality levels to tables or data elements allows data teams to focus on addressing the most critical problems first, ensuring that resources are allocated efficiently and effectively.
Process Compliance: Adherence to standardized processes is essential for maintaining consistency and efficiency in data quality management. A well-defined data quality process encompasses various stages, including issue assessment, prioritization, remediation, and closure. A crucial aspect of this process is the ability to differentiate between genuine data quality issues and random data anomalies. Implementing clear notification paths and escalation procedures ensures that data quality incidents are promptly routed to the appropriate teams, thereby facilitating timely resolution and adherence to service-level agreements.
By establishing a cohesive framework that incorporates process compliance, impact analysis, and end-to-end traceability, organizations can proactively manage data quality, minimize risks, and optimize their data-driven initiatives.
Data quality operational processes
Running a data quality platform that meets all the rules doesn’t have to be a headache. By setting things up smartly, you can make life easier for your data quality team. This means having ready-to-use checks that can be applied to any data, and keeping track of important data quality numbers using dashboards that can be tailored to different needs. For example, managers might want a big-picture view of data quality scores, while the operations team needs more detailed information.
Data Quality Standards: Think of these as your data’s rulebook. Your organization should have a set of standard checks that apply to all data sources. This ensures consistency and helps catch common problems early on. If you have specific types of data that need to follow a certain format (like invoice numbers or product codes), the same check should be used everywhere to keep things tidy.
Data Quality Reporting: All your data quality information, both current and historical, should be stored in a central database. This database acts like a library, allowing anyone with the right tools to access and analyze the data. This is crucial for creating custom reports and dashboards, especially if you need to prove your data quality track record to auditors or other stakeholders.
Data Platform Upgrades: Upgrading your data platforms and systems is like giving your car a tune-up – it’s necessary, but you need to do it carefully. Your organization should have a plan to ensure data quality checks keep working smoothly after any upgrades. This often involves testing the checks in a development environment first, and then carefully moving them to the live production environment.
Requirements for a compliant data quality platform
When striving for data quality compliance, data governance teams must carefully evaluate the capabilities of potential data quality platforms. While open-source libraries like Great Expectations or Deequ offer a convenient way to embed data quality checks within data pipelines, they often rely on log files for audit records. Constructing a compliant audit trail database from these logs can be a complex and time-consuming endeavor, potentially introducing risks and delays.
Therefore, data governance teams should prioritize platforms that inherently support the calculation and storage of data quality health scores, such as data quality KPIs, along with their historical values. Direct access to the underlying data quality metrics database is crucial for building customized dashboards tailored to specific data sources or domains, ensuring compliance reporting requirements are met. However, Software-as-a-Service (SaaS) vendors may restrict direct database access in multi-tenant environments, hindering this flexibility.
Furthermore, data privacy and security concerns often necessitate on-premise deployment of data quality platforms, particularly when organizations handle sensitive or regulated data. By maintaining an on-premise instance, all data quality results are stored within the organization’s secure environment, adhering to strict data protection protocols and ensuring full control over audit trails and historical data.
In summary, selecting a data quality platform that aligns with compliance requirements involves considering its ability to calculate and store data quality metrics, provide direct database access for custom reporting, and offer on-premise deployment options when necessary. This strategic approach empowers organizations to proactively manage data quality, demonstrate regulatory compliance, and safeguard sensitive information.
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What is the DQOps Data Quality Operations Center
DQOps is a data quality platform designed to monitor data and assess the data quality trust score with data quality KPIs. DQOps provides extensive support for configuring data quality checks, applying configuration by data quality policies, detecting anomalies, and managing the data quality incident workflow.
DQOps was designed to meet regulatory compliance. It stores the configuration of all data quality checks in flat files that can be stored in a source code repository to allow versioning, change management, or even to enable pull requests and approval for applying configuration changes. DQOps can be started in many configurations, ranging from a local installation on the data steward‘s laptop to performing an initial data quality assessment, an on-premise local installation, or a SaaS-hosted environment. Each deployment option maintains its data quality data warehouse, keeping the metrics private for the customer. The platform comes with 50+ data quality dashboards that can be adapted to generate data quality compliance reports in various formats.
You can set up DQOps locally or in your on-premises environment to learn how DQOps can monitor data sources and ensure data quality within a data platform. Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
You may also be interested in our free eBook, “A step-by-step guide to improve data quality.” The eBook documents our proven process for managing data quality issues and ensuring a high level of data quality over time.
