Data privacy, management, usability, risk, and security make up a vast landscape that can be complex and difficult to navigate. But when you understand the interconnection between sensitive data elements, data quality, and data governance, you’re well on your way to creating an effective data privacy program that supports business growth as well as regulatory compliance.
Let’s define our terms and explore how sensitive (or private) data elements, quality, and governance are linked.
1. Data quality and sensitive data
- Accuracy and integrity: Achieving data quality requires ensuring your sensitive data is accurate, consistent, and reliable. Inaccurate or inconsistent sensitive data can lead to privacy breaches, compliance violations, and other negative consequences that no organization can afford.
- Completeness: Data quality practices help you maintain complete sensitive data. Missing or incomplete sensitive data can hinder your organization’s ability to make informed decisions or comply with regulations.
2. Data governance and sensitive data
- Data classification: Data governance involves classifying data based on its sensitivity. Sensitive data requires strict governance policies and controls, including who can access it and how it should be handled.
- Data access controls: Data governance defines who can access sensitive data and under what circumstances. It enforces policies that restrict unauthorized access to reduce the risk of data breaches.
- Data retention and disposal: Data governance policies determine how long sensitive data should be retained and when it should be securely disposed of. These policies help manage the lifecycle of sensitive data effectively.
3. Data quality and data governance
- Data policies: Data governance frameworks often include data quality policies and standards. These policies define the required quality levels for different data categories, including sensitive data.
- Data stewardship: Data governance involves assigning stewards to oversee data quality, including sensitive data. These staff ensure that data quality standards are met and maintained.
- Data auditing and monitoring: Data governance involves monitoring and auditing data usage, which includes assessing data quality. Regular audits can identify and rectify data quality issues, ensuring that data remains accurate and reliable.
In summary, to maintain integrity and protect against security risks, sensitive data elements must be of the highest quality. Data governance plays a pivotal role in defining and enforcing policies and controls for sensitive data while incorporating data quality disciplines and processes. By integrating these three aspects, your organization can effectively manage, secure, and derive more value from its sensitive data while adhering to legal and regulatory requirements.
Ensuring well-governed, compliant, and accurate data can be time- and resource-intensive. Data Sentinel combines AI and ML technology, managed services, and advisory capabilities to deliver data privacy, governance, and remediation solutions designed for simplicity and ease of use. Our innovative approach to data privacy compliance helps you:
- Manage risk to your sensitive data.
- Comply with privacy regulations.
- Manage data governance and quality.
- Remediate data challenges and events.
Discover how Data Sentinel’s flexible services can help your organization ensure accurate, trustworthy data that’s not only compliant but also supports better business decisions. You can also connect with us or request a demonstration to see our solution in action.