Data security is an important priority for every organization that handles customer or user data, especially those that fall under data privacy and protection laws such as the EU’s General Data Protection Regulation (GDPR). Large volumes of personal data flow in and out of companies and their systems every day, and the threat of data breaches looms over these operations. How can organizations use their data to power critical operations without running afoul of regulations or privacy concerns? 

Table of Contents

  1. What is PII?
  2. What is Pseudonymization?
  3. Benefits of PII Pseudonymization
  4. PII Pseudonymization Use Cases
  5. Implementing PII Pseudonymization
  6. Using’s Extract, Transform, Load Platform to Handle Pseudonymization at Scale

What is PII?

Personally identifiable information, or PII, is sensitive data capable of identifying a unique individual. This personal data comes in many forms, from a person’s name to their medical information. 

Depending on an organization’s location, industry, and the type of PII they work with, they may need to handle this data in specific ways to keep it protected. PII is a tempting target for hackers, and organizations of all sizes can fall victim to a data breach. For example, the data from 500 million LinkedIn accounts recently surfaced on the dark web.  

Many companies handle large volumes of PII, which they use for many purposes such as sales, marketing, customer support, human resources, healthcare, business intelligence, and more. If they don’t protect this data properly, then organizations face the potential of compromised data, breaches, non-compliance with data regulations, and other consequences. 

What is Pseudonymization?

So many business operations depend on having access to PII in various forms, so eliminating it entirely is not an option. Organizations can balance the security needs of PII with their operational needs through a practice called pseudonymization. 

PII pseudonymization is a recommended approach in the EU’s GDPR. With this data security method, the original PII data goes through a substitution process. Sensitive data gets changed to information that retains the usefulness of the data without the security concerns of completely unmasked PII. 

Pseudonymization allows organizations to reverse the process to get back to the original PII, if needed, through a reference pseudonym table. This feature differentiates it from data anonymization, which makes reversal impossible. 

Benefits of PII Pseudonymization 

Leveraging PII pseudonymization as part of an organization’s data security strategy delivers many benefits: 

  • Improving data privacy: Since the original data remains on a reference table under the organization’s control, it’s less likely that an intentional or unintentional data breach will occur. Even if the attackers get the pseudonymized PII, they can’t trace it back to an individual without having the reference data. 
  • Adhering to GDPR requirements: Since pseudonymization is specifically mentioned in GDPR as an allowable method for protecting personal data, organizations can use it on their journey to achieve and maintain compliance. 
  • Decreasing PII risks: Malicious actors continually develop new attack methods and malware to gain access to data and systems. By using pseudonymization as one part of a comprehensive data security plan, organizations can gather the data they need without it putting individuals at undue risk of unauthorized access. 
  • Expanding PII usability: Organizations have an easier time using pseudonymized PII in research studies, reporting tools, and other solutions that may involve a third party. Since the original data is protected and inaccessible by these external partners, organizations can get more value out of this information. 
  • Maintaining consumer trust: Individuals have increased their data privacy awareness. They want to know that organizations are doing everything possible to keep their PII safe. Without these reassurances, a company could lose customer trust and suffer from reputation damage. 

PII Pseudonymization Use Cases

A few examples of how PII pseudonymization works in real-world applications include: 

  • Service provider access: The organization takes the PII through a pseudonymization process before moving it to a third-party provider for other data operations. This provider can work on these data sets without seeing the original PII. Once they complete the processing, the data goes back to the organization’s systems and is reverted to the original form. 
  • Going beyond the original data collection purpose: Data protection regulations such as the GDPR require companies to have valid and specific purposes for collecting personal data. The PII in its original form wouldn’t be eligible for other types of uses, but pseudonymized data would be. 
  • Implementing data minimization: Another important part of GDPR is the concept of data minimization, which means collecting and using only the personal data needed to meet the intended purpose. 
  • Using realistic test data: Organizations may have difficulties testing new applications or conducting research if they have to use placeholder data. PII pseudonymization provides data sets that are within the typical ranges for the company’s actual data. The test environment is realistic. 

Problems with PII Pseudonymization

Organizations may run into several roadblocks while implementing PII pseudonymization. One of the biggest challenges is working with PII at scale. Manually processing this data would be an inefficient method for most use cases, as the data sets may be massive. If an organization runs all of its PII through a pseudonymization process, it can greatly impact the performance of the data pipeline. Categorizing the PII and determining which data is suitable for pseudonymization is an important part of properly managing the implementation process. 

Implementing PII Pseudonymization

Following best practices for PII pseudonymization helps organizations get the most out of this data security method. The most important aspect of choosing a sufficient approach is to avoid any tools that base the pseudonymized values on the original data. Incremental counters, random number generators, and hashing functions are three options that protect personal data. 

Strict security controls and policies should be in place for the lookup table. If this reference table is exposed or shared with unauthorized parties, then the PII becomes exposed, and the pseudonymization can’t protect this information. 

Using’s Extract, Transform, Load Platform to Handle Pseudonymization at Scale

Having to wait hours, days, or weeks for PII to go through the pseudonymization process makes it difficult to act on its insights. This lack of agility can cost an organization many opportunities or lead team members to try to use workarounds that could put data privacy at risk. 

Extract, Transform, Load (ETL) tools such as offer an ideal solution to quickly and conveniently pseudonymize PII. This data pipeline platform extracts data from source applications and databases and then moves to the transformation process. During this step, the ETL tool can turn personal data into pseudonymized data before it ever reaches the destination database or data warehouse. Other types of transformations are also available, such as data cleansing. 

By using to automate PII pseudonymization, this data arrives at the data store prepared and ready for analytics operations, research, and other use cases. This ETL solution provides a user-friendly, visual data pipeline builder to streamline the initial configuration process, with over 100 built-in integrations. As new data regulations are developed, updating the data flow is a straightforward process. is compliant with GDPR, HIPAA, CCPA, SOC 2, and many other data privacy regulations, so organizations can use this platform for PII pseudonymization and remain in compliance. 

Ready to learn more about’s PII pseudonymization capabilities? Start your 7-day trial and improve your data security today.