Como você protege os dados ao usar ferramentas de qualidade?

Two examples I've seen for data encryption in transit in the context of data quality: 1. When transferring data between two separate AWS accounts - for data quality checks purposes - Amazon S3 uses Secure Sockets Layer/Transport Layer Security (SSL/TLS) to encrypt data. Also, we can enforce SSL/TLS in S3 to deny any non-SSL requests, which ensures that data transmitted to and from that S3 bucket is always encrypted. 2. When working with Kafka event streaming to stream business events to our data platform, we had to secure by migrating to a Kafka TLS (Transport Layer Security) cluster. This prevents unauthorized access to data ingested in our data platform.

Before implementing security measures, it's essential to classify your data based on its sensitivity and importance. This classification could include categories such as confidential, sensitive, public, and more. Identify any data containing Personal Identifiable Information (PII) to ensure compliance with privacy regulations. Once data has been identified as having PII, create privacy policies and governance frameworks to effectively manage and protect it. These policies should outline guidelines for accessing, handling, and storing sensitive information, including PII. Implement access controls and encription mechanisms to safeguard data against unauthorised access or breaches.

When using data quality tools, encryption is a key step in securing your data. It ensures that even if data is accessed by unauthorized individuals, it remains unreadable and secure. Implementing strong encryption protocols for both data at rest and in transit is fundamental to protecting sensitive information while maintaining its quality.

In our projects, we prioritize encryption both at rest and in transit. By using advanced encryption techniques in professional Master Data Management (MDM) solutions, we ensure sensitive data is unreadable to unauthorized parties. This approach has proven effective in protecting customer data during large-scale migrations and integrations, reducing risks of theft or tampering.

In the realm of data quality, ensuring that only authorized personnel have access to data is vital. Implement robust authentication and authorization mechanisms to safeguard your data from unauthorized access or breaches. This includes using strong passwords, multi-factor authentication, and rigorous access controls. Properly managing who can access and modify data is a critical component of a secure data quality management system.

Implementing strong authentication and authorization is a key principle we follow. With role-based access control (RBAC) embedded in MDM platforms, we restrict access to sensitive data to only authorized users. This not only secures the data but also streamlines governance, ensuring that only relevant personnel have the permissions to modify critical data.

Regular auditing and monitoring of data activities are essential for maintaining data security when using quality tools. This process helps in detecting any unauthorized access, data manipulation, or breaches in real-time. Keeping a close eye on how data is used and by whom allows for quick action if any irregularities are detected, ensuring the ongoing integrity and security of your data.

Maintain detailed audit trails of user activities within the data quality tools. Log user actions, such as data access, modifications, and exports, to track and monitor data usage and identify any suspicious activities.

For our clients, continuous auditing and monitoring are essential for securing master data. We configure MDM tools to log and track every data activity, ensuring full traceability. This allows us to detect any anomalies or breaches quickly and respond before they escalate, giving clients peace of mind over their data security.

In data quality initiatives, masking and anonymizing sensitive data is crucial for maintaining privacy and security. This process involves obscuring personal identifiers and sensitive information, ensuring that data remains useful for analysis without compromising confidentiality. Implementing these techniques is especially important in environments where data is shared or used for development and testing.

To keep sensitive information safe in data quality tools, we use something called data masking. It's like putting a disguise on the sensitive data so that only the right people can see it. This way, even if someone gets access to the data, they won't be able to understand the sensitive details because they're hidden behind the mask. It's a way to make sure only authorised people can see the full information while keeping it safe from others who shouldn't have access to it.

When sharing data externally or using it for testing, we leverage data masking and anonymization techniques within MDM software. By obfuscating sensitive fields, we maintain the functionality of the data while protecting personal information. This approach is particularly valuable in projects involving third-party data sharing or cloud migrations.

In every project, we ensure our MDM solutions align with industry security standards like GDPR and ISO 27001. These standards act as a framework to maintain high data quality and security, allowing us to implement best practices that prevent breaches and ensure compliance, especially when handling customer-sensitive data.

This is especially relevant for open-source data quality tools. Ensure you understand the open issues in the open-source tool and that these issues don't affect your current data quality KPIs.

We make it a point to keep our MDM tools updated and patched to safeguard data quality. Regular updates ensure that any vulnerabilities are addressed and that our clients benefit from the latest features. This proactive approach mitigates security risks while maintaining high performance and data integrity.

An aspect not covered in the document: business-related data quality metrics. These are data quality criteria that must be met from a business perspective and not from a technical one (encryption, monitoring, data security standards, practices, etc..) Communication is required with relevant stakeholders and a process must be set to translate business requirements into implementable data quality checks. Data quality tools and infrastructure must be set in place to allow self-service capability for the business to add or modify data quality checks with minimal effort from the data teams.