How Data Discovery Enhances &#...
How Data Discovery Enhances & Aut...

How Data Discovery Enhances & Automates Your Data Map 

Effective Privacy Programs begin with a Firm Understanding of Data 

clock6 Min Read

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Privacy teams face mounting pressure to comply with an increasingly complex patchwork of regulations.  To fulfil new requirements for data, governance, and privacy compliance across multiple jurisdictions as well as respond to a growing number of data subject requests, it is paramount these teams increase their capacity.   

To do this, privacy teams require tools that allow them to scale their visibility into the data they possess while also providing relevant context around purpose and consent. Initially guided by Article 30 requirements of the 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: 

  • Overlapping or conflicting regional regulations 
  • Evolving complexity of tech stacks 
  • Fragmented data sources and formats 
  • Cross-functional business processes  
  • Data mapping exercises reliant on manual processes e.g., assessments 

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 Data Discovery by OneTrust connects to all systems, scanning for personal data and providing context to create a foundation for a more efficient and accurate privacy program. Here’s how they do it:  

Laying the foundation of your Data Map

Regulations like the GDPR require organizations to understand the processing of personal data through a record of processing activities. Many elements of a Data Map, such as what is our legal process for using the data, cannot be populated with Data Discovery. Furthermore, establishing a basic list of processes and the required attributes can be done relatively quickly. Therefore, most organizations will start with utilizing semi-automated methods to populate their data map, with a view to use Data Discovery to enhance it and keep it up to date. 

Once the foundation of Data Map in place, it’s now time to look at enhancing and enriching it with the use of Data Discovery. From a high level, the process of data discovery identifies two key attributes:  

  • Data location: Where is the data stored? Is it located in the cloud, on-premises, or both?  
  • Data type: What format does the data take? If the same type of data lives in more than one location, does the format align across sources? 

It’s likely that an organization’s stored personal data exists in more than one place. And several instances of the same 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. Without the right tools, this can lead to manual, lengthy, and often inaccurate efforts.  

Using more Automation 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 time efficiencies, and scaling project effectiveness. 

Scanning Metadata and Data Sources for Accurate Classification  

To accurately classify data across diverse sources and formats, analyzing the metadata may not be enough. Privacy teams need to go many levels deeper with their analysis to develop data classifications that will inform decisions around compliance.  

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 information 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 solution that intelligently scans thousands of sources, beyond metadata, and deep into real-time samples to develop accurate classifications.  

Gaining Insights as Privacy Evolves with Intelligent Scanning  

As privacy legislation continues evolving, definitions for 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 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.  

For example, as privacy teams get up to speed with the new requirements under CPRA, an intelligent classification tool can flag data violations that didn’t previously exist in 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.  

 Conclusion: A more complete understanding of organizational data yields better insights

Before data teams 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 insights. 

Privacy teams that partner with an intelligent solution for data discovery and data mapping 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 their challenges — by saving time, increasing accuracy, and laying the groundwork for an effective privacy strategy. 

OneTrust’s software is designed to automate privacy from start to finish, including: 

  • Discovery & classification of personal data across the IT ecosystem, applying both business and regulatory context through DataGuidance research. 
  • Population of a central data inventory & catalog to serve as the foundation of privacy & data governance initiatives. 

With these tools in place, privacy teams can make more informed decisions. In addition, they’re easy to use, so everyone on the team can understand and use them. 

See how OneTrust can help. Request a demo today! 

 

Further Reading on Privacy Program Automation:

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Tags:

data discovery
data map
data mapping automation
GDPR Article 30

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