How can you ensure data warehousing systems comply with GDPR and CCPA?

Always meticulously document the types and flow of personal data within the systems, identifying points of collection, storage, and processing. This granular understanding empowered me to establish targeted controls and safeguards. It has facilitated the creation of a clear data inventory. Knowing where personal data resides allowed me and my team to implement measures like data minimization, ensuring we only processed what was necessary for specific purposes. Regular updates to our data maps ensured that we remained agile in adapting to changing data flows, technologies, and compliance landscapes. We proactively manage and protect personal data throughout its lifecycle.

In my experience, when you start a new business initiative that requires data, you perform data mapping for different sources at that time. Then, make it a required step in your ETL process to review the documentation if new fields are added or removed. Keep all regulations related to GDPR/CCPA in mind. The data mapping documentation should be periodically checked, reviewed, and versioned for audit purposes

We identified and classified the essential data needed for specific purposes, discarding redundant or unnecessary information. This not only streamlined our data warehousing system but also minimized the risk associated with processing excessive personal data. Only by collecting and retaining the data essential for legitimate purposes, we enhance our ability to comply with principles of purpose limitation and data relevance outlined in the regulations. Regular audits and assessments were the keys for me and my team. Periodic reviews you can call it. A focused and adaptive approach to data handling aligns with the principles of privacy and data relevance.

In my experience, this is the most used and easily-applied step for GDPR compliance. In the organizations we have worked with, the most effective technique was simply to anonymize, and redact the data containing GDPR-restricted/PII entities to make the data warehouse compliant.

To minimise data, collect only what's necessary for your specific business purpose. For example, if you're analysing shipping routes, you might only need postal codes and countries rather than full addresses. Consider anonymising or deleting sensitive data after it's no longer needed, like replacing customer names with random identifiers after 90 days

We encountered a situation where outdated and inconsistent data hindered compliance efforts. We introduced validation checks and automated processes to ensure the accuracy and consistency of personal data. This not only improved compliance but also enhanced decision-making and trust in our data. We learned that accurate and up-to-date data is foundational to meeting regulatory standards, emphasizing the importance of data accuracy and integrity. By routinely reviewing and rectifying discrepancies, We maintained a high standard of data quality, aligning with the regulations' requirements and adapting to evolving compliance landscapes.

Data security emerged as non-negotiable. I and my team lacked robust security measures posed a significant risk to compliance. We introduced encryption, multi-factor authentication, and regular security audits to fortify our data warehousing systems. This not only enhanced our compliance efforts but also bolstered our defense against potential breaches. We learned that secure data handling is foundational to meeting regulatory standards, emphasizing the importance of protecting personal information from unauthorized access or disclosure. It helps instill confidence in our ability to safeguard sensitive data.

Following strict data access control is crucial. A smart way to do this is by using a Git-based solution, similar to what's done in modern data warehouses like BigQuery. You can use a Git repo to manage and version control your access policies, which lets you track changes, review updates, and roll back if needed. It's like having a time machine for your security settings! For example, you could use Terraform with Git to define and manage your BigQuery access controls. This way, you're not just securing your data, but also making your security process itself more transparent and easier to audit. It's a win-win for keeping your data safe and your compliance team happy

Relying on consent as a legal basis may not be very practical for data warehousing. considering that a data warehouse requires a large source of data, it is difficult to have every data subject consent. Most likely, organisations will have to rely on legitimate interest as a legal basis.

There are several tools to manage data consent such as OneTrust, TrustArc, Cookiebot, Tealium, OneConnect, CookiePro, Quantcast Choice, and ConsentEye. When selecting a tool, it's crucial to consider factors such as the scope of the tool (website, mobile app, etc.), customization options, integration capabilities with existing systems, and the ability to generate and maintain comprehensive consent records for compliance reporting. Additionally, regularly updating and auditing consent records is essential to ensure ongoing compliance with evolving privacy regulations.

Documentation throughout the process is key. It may take time initially, but it will definitely be useful in the long run and contribute to the efficient functioning of the team

Even in CCPA, law allows the firm to keep the PII of users for somewhere around 7years for legal purpose, but that to be stored or moved to some restricted database which can be accessed by backend only. So other than the restricted db data, once a user will request their data to be removed, backend team should have to remove permanently or at least mask the PII of that user from every database or schema wherever that is stored. For doing so, first and foremost data mapping is essentially required to find the flow for that PII to be deleted.