Restricted access to data is becoming a concern among data-driven companies. Bound by strict regulations around data collection, use, and sharing organizations are now moving towards synthetic data for machine learning, research, and development. Gartner estimates that 60% of the data used in training AI models will be synthetically generated by 2024. Since synthetic data isn’t real data key users of data i.e. data teams, engineering teams, and privacy teams find it extremely useful to use synthesized data for building and testing AI\ML models and software applications.
Read Betterdata’s complete guide to synthetic data here.
1. Data Teams:
a. Challenges for Data Teams:
Data Quality and Privacy Compliance:
- Data teams often struggle with the availability of high-quality, diverse datasets that are crucial for developing robust analytics and machine learning models. This challenge is compounded by stringent privacy regulations like the General Data Protection Regulation (GDPR) in Europe, the Health Insurance Portability and Accountability Act (HIPAA) in the United States, and the Personal Data Protection Commission (PDPC) in Singapore which impose strict rules on how data can be collected, stored, and used.
- Ensuring data quality involves continuous efforts to cleanse, integrate, and manage data from varied sources, which can be resource-intensive and complex. Achieved through a series of continuous efforts aimed at maintaining the accuracy, completeness, and reliability of data throughout its lifecycle.
Balancing Act:
- Providing broad access to data for analytics and research purposes while ensuring the privacy and security of this information is a significant challenge. Data teams must implement robust access controls and monitoring to prevent unauthorized access and data breaches, which can lead to legal repercussions and damage to reputation.
- Implementing anonymization techniques to protect individual privacy reduces the utility of the data for analytical purposes, leading to a trade-off between privacy and the utility of data.
b. How Synthetic Data Helps:
Diverse and Compliant Testing Data:
- Synthetic data generation techniques can create realistic, non-sensitive data that mimics the characteristics of real datasets. This enables data teams to perform testing, development, and training of models without the risk of exposing sensitive information.
- Since synthetic data is not derived from actual individual records but is generated to follow the same statistical distributions, it can be used freely without violating privacy laws, making it an excellent tool for compliance with regulations.
Privacy Compliant Data Insights:
- Synthetic data allows for the analysis and sharing of datasets that contain no real personal data, thus eliminating the risk of re-identification. This is particularly useful in fields such as healthcare, where data privacy is paramount but researchers need access to detailed data to advance medical research and treatment.
- By using synthetic data, organizations can explore and extract valuable insights from their data while ensuring that individual privacy is maintained. This approach supports innovation and data-driven decision-making within the boundaries of ethical considerations and legal requirements.
2. Engineering Teams:
a. Challenges For Engineering Teams:
Realistic Data for Development:
- The development and testing of software solutions require data that is representative of real-world scenarios to ensure that applications perform as intended in a variety of conditions. Accessing such data, however, can be fraught with privacy concerns and practical difficulties.
Speed vs. Privacy:
- The push to bring innovations to market faster increases the pressure on development cycles. Balancing this need for speed with the imperative to maintain data privacy, especially when dealing with sensitive information, presents a significant challenge.
Security in Development Environments:
- As software becomes more complex, the risk of vulnerabilities and data breaches grows. Ensuring that development environments remain secure against both internal and external threats is paramount to protecting intellectual property and user data.
b. How Synthetic Data Helps:
Enhanced Testing and Development:
- Synthetic data provides a solution by generating data that closely mimics real operational data in its complexity and variability, without containing sensitive information. This enables thorough testing and development of software under a wide range of scenarios, leading to more robust and reliable applications.
Streamlined Data Provisioning:
- By using synthetic data, engineering teams can bypass many of the hurdles associated with data access and privacy concerns. This accelerates the development process by making it easier and faster to provision data for testing, allowing teams to focus on innovation and refinement rather than data management.
Data Security Assurance:
- Since synthetic data does not include real user data, its use significantly mitigates the risk of data breaches within development environments. This ensures that even if security is compromised, the exposure of sensitive information is limited, protecting both users and companies from the repercussions of data leaks.
3. Privacy Teams:
a. Challenges For Privacy Teams:
Data Protection Compliance:
- Keeping up with the evolving landscape of global data protection laws, such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Personal Data Protection Act (PDPA), is a constant challenge. Each regulation has its own set of requirements, making compliance a complex, ongoing task.
Sensitive Data Handling
- The need to anonymize or pseudonymize sensitive data to prevent the identification of individuals while still allowing the data to be useful for analysis and business processes. This requires sophisticated techniques and constant vigilance to ensure that the data remains anonymous over time.
Access Control
- Implementing and managing strict access controls and permissions for sensitive data to ensure that only authorized personnel can access the information they need for their role, and no more. This is key to protecting against both internal and external threats.
b. How Synthetic Data Helps:
Regulatory Compliance:
- Synthetic data, by its nature, does not contain real personal data but rather is artificially generated to mimic the properties of real data without including identifiable information. This makes it a powerful tool for achieving compliance with data protection laws, as it removes the risk of personal data exposure.
Privacy-Preserving Practices
- The use of synthetic data allows privacy teams to support the organization’s data needs for analysis, development, and testing without compromising individual privacy. Since synthetic data can be generated to reflect the complexity and variability of real data, it enables meaningful insights and innovations while adhering to privacy principles.
Strict Access Controls
- With synthetic data, the need for strict access controls becomes less burdensome because the data does not contain sensitive personal information. While access controls remain important for protecting intellectual property and maintaining data integrity, the risk associated with potential unauthorized access is significantly reduced.
Data privacy and utility challenges vary across different professional domains. Synthetic data provides a versatile solution to these challenges catering to the unique needs of Data Teams, Engineering Teams, and Privacy Teams. By offering diverse, realistic, and privacy-compliant data, synthetic data not only streamlines processes but also enables responsible data usage. As organizations move towards a data-driven ecosystem, synthetic data becomes an interesting opportunity to balance data usage and data privacy due to its many benefits.
