Privacy teams face mounting pressure to comply with an increasingly complex patchwork of regulations. To fulfill new requirements for data governance, data management, and privacy compliance across multiple jurisdictions as well as respond to a growing amount of data and awareness of data subject rights, it is paramount that these teams increase their capacity.
To do this, privacy teams require tools that allow them to scale data visualization while also providing relevant context around purpose and consent. Initially guided by Article 30 requirements of the General Data Protection Regulation (GDPR), many organizations achieve this visibility through data mapping exercises. Considering the following challenges privacy teams face, keeping data maps up to date and accurate is easier said than done:
In order to comply with regulatory requirements, meet the needs of the business, and respond to data subject requests in a timely (and compliant) manner, organizations mature and scale their data mapping programs with technology that unifies and automates data discovery. Tools such as OneTrust Data Discovery connect to all systems and scan for personal data. This provides the context to create a foundation for a more efficient and accurate privacy program. Here’s how they do it:
Regulations like the GDPR require organizations to understand the processing of personal data through a record of processing activities. Establishing a basic list of processes and the required attributes can be done relatively quickly. However, many elements of a data map, such as the legal basis for processing personal data, cannot be populated with data discovery. Therefore, most organizations will start with utilizing semi-automated methods to populate their data map, with a view to using fully-automated data discovery to enhance it and keep it up to date as new data is added.
Once the foundation of the data map is in place, it’s now time to look at enhancing and enriching it through data discovery software that can help develop advanced analytics into complex data. From a high level, the data discovery process identifies two key attributes:
It’s likely that an organization stores personal data in more than one place. And several instances of the same customer data may be inconsistently classified or formatted across different sources. To create a holistic view, privacy teams need to facilitate an accurate and scalable translation across these differences.
Ultimately, it’s in a privacy team’s best interest to create unified data inventories as a product of data discovery and mapping projects. Manual data mapping and data discovery processes can be lengthy, time-consuming, and often yield inaccurate results.
Using more automated data mapping tools is the best solution for this under today’s conditions. It allows teams to understand the complete scope of their data — while bypassing traditional bottlenecks, generating business intelligence, and scaling project effectiveness.
To accurately classify data across diverse data sources and formats, analyzing the metadata may not be enough. Privacy teams need to go many levels deeper with their data analysis to develop data classifications that will inform decision-making around compliance and risk management.
In some instances, topline metadata may raise a flag for privacy review. But metadata doesn’t cover all the possible iterations or combinations of sensitive data that a privacy team ought to know.
For example, it’s possible that an individual data element — and the way it’s stored, shared, or grouped with other data elements — could be at odds with regulatory requirements under specific conditions. But accessing these highly-dispersed insights in a scalable way is impossible without help.
Privacy teams are best equipped with a data discovery tool that intelligently scans various sources, including metadata and unstructured data sets, and offers real-time samples to develop accurate classifications.
As privacy legislation continues evolving, definitions of personal data also continue to change. Further, regulatory bodies present varying definitions that create challenges for the organizations that are accountable to them.
A trained data classification engine can help privacy teams keep pace with each major regulatory body — even as requirements change — by providing intelligence based on the latest guidance and regulatory updates.
For example, as privacy teams get up to speed with the new requirements under the California Privacy Rights Act (CPRA), an intelligent classification tool can flag data violations that didn’t previously exist under the California Consumer Privacy Act (CCPA).
And as teams evaluate their data retention policies, deeper scanning within company-held data can reveal important classifications such as “Created” or “Last Updated” that can support the application of retention policies.
Before different stakeholders can address vulnerabilities in their privacy programs, it’s necessary to understand the complete scope of their organization’s stored data.
The outcomes of data discovery and data mapping establish this. Once an organization has a readily accessible, well-classified data landscape, it’s much easier to develop detailed data insights and improve data quality.
Privacy teams that partner with an intelligent data mapping solution and automated data discovery can build confidence in their ability to source up-to-date regulations and remain flexible as circumstances evolve. They know that automation plays a pivotal role in addressing and solving the challenges presented by the data mapping process — by saving time, increasing accuracy, and laying the groundwork for an effective privacy strategy.
The OneTrust Privacy & Data Governance Cloud is designed to automate privacy compliance throughout the data lifecycle, including:
With these modules in place, privacy teams can make more informed business decisions. See how OneTrust can help. Request a demo today!