Data sharing is critical for organizational growth. Yet it takes weeks or even months to access or share data internally or beyond organizational borders due to strict data privacy laws. Putting enterprises that heavily rely on data for development, testing, prototyping, and analysis, etc., at a strategic disadvantage on multiple fronts.
What are the Challenges with Data Access and Sharing?
Data Breaches and Legal Liability:
78% of organizations reported a breach in the past year, with an average global cost of $4.88 million.
Ransomware and Cybersecurity Threats:
59% of companies were targeted by ransomware in 2024, highlighting major gaps in secure data sharing.
Operational Costs and Turnaround Time:
41% of IT staff spend up to 60% of their week on data requests due to poor access systems.
Compliance Burdens and Regulatory Overhead:
Strict data protection laws (e.g., GDPR, CCPA, PDPC) severely restrict sharing and access.
What is the impact of Data Breaches?
Business Disruption:
$2.8M of breach costs stem from downtime and customer churn.
Dark Data and Lost Value:
55% of enterprise data remains unused and unquantified.
Poor Data Retention:
98% of new data is discarded within a year, with only 2% retained.
Data Silos Affecting Transformation:
89% of IT leaders report silos slowing digital initiatives.
Limited Cross-Border Innovation:
82% of global companies cite regulatory complexity as a blocker to market expansion.
Protecting customer data privacy is both a moral and legal requirement for any enterprise, but innovation waits for no one. This is why synthetic data is the next best or even better alternative than real data.
What is Synthetic Data?
Synthetic data is real-like data that does not contain any PII. Meaning that it can be accessed and shared 10x faster compared to real collected data.
- Synthetic data is generated through generative AI; hence, it does not contain any PII.
- Synthetic data can further be protected via differential privacy and advanced anonymization techniques.
- Synthetic data does not mask, encrypt, or destroy data, preserving data utility.
- Synthetic data can be generated, augmented, scaled, and enhanced on demand.
How does Synthetic Data Solve Data Sharing?
Protect data privacy:
Synthetic datasets contain no real personal information. This means even if synthetic data is leaked or breached, real individuals’ identities or any sensitive information will not be revealed. Making synthetic data an easier and faster alternative for enterprises to share data quickly and securely, protecting data privacy while accelerating innovation and growth.
On a side note, Betterdata provides quantifiable privacy guarantees through differential privacy to control and balance of synthetic data utility and privacy. This means enterprises can customize synthetic data based on their internal, local, and national data privacy protection laws. To learn more, contact us.
Reduced Compliance Burden:
Privacy protection laws were established to regulate personal data. However, since synthetic data is not collected from real-world events but is a mirror image of real-world events, many data privacy laws and regulations can be circumvented. Enterprises can use and share synthetic data without triggering the same strict oversight, reporting, or consent processes that real data would require.
It is also worth noting that this applies to generating high-quality synthetic data with a low cosine similarity score (or other metrics that denote the statistical difference between real and synthetic data). Privacy laws still apply to the early stages of the synthetic data pipeline, where real data is being used for training generative synthetic data models.
Greater Data Availability and Collaboration:
Synthetic data removes data silos. Enterprises can share previously off-limits data with partners, vendors, or researchers. For example, a bank can create a synthetic version of its transaction database and share it with a fintech partner or an analytics vendor without exposing any customer information. This enables collaboration on analytics, machine learning models, AI enablement, or product development that would have been impossible with real data due to privacy and security challenges.
Operational Efficiency and Cost Savings:
Preparing real data for sharing (through heavy anonymization, legal reviews, setting up secure environments, etc.) can be time-consuming and costly. In contrast, once a robust synthetic data generation process is in place, Enterprises can generate fresh synthetic data on demand, eliminating the need for lengthy approvals or data provisioning delays each time data is needed for a project.
How do you generate synthetic data for data sharing?
Synthetic data is a subset of Generative AI generated via advanced machine learning models such as GANs, LLMs, VAEs, or DGMs. The process for generating synthetic data varies depending on the model being used; however, in principle, all models are first trained on real training data where they learn it’s statistical properties and then generate synthetic data using these same properties.
At Betterdata, we have built SOTA models for synthetic data generation, such as,
- ARF (Auto Regressive Flows) that generates and augments high utility tabular synthetic data.
- TAEGAN can scale and augment small and scarce synthetic datasets.
- IRG (Incremental Relational Generator) uses deep learning to generate synthetic relational databases without compromising structural integrity.
Furthermore, we implement differential privacy in the entire synthetic data generation pipeline, allowing us to customize the output (data utility/data privacy) depending on your specific needs, corporate regulations, and the overarching governmental laws.
