Mastering Synthetic Data Generation: Applications and Best Practices

Enterprises should guard the data as their deepest secret, as it fuels their lasting impact in the digital spectrum. In pursuing the same, synthetic data is a weapon that emulates actual data and enables many data functions without revealing the PII. Even though its utility is below real-time data, it is still equally valuable in many use cases. 

For example, Deloitte generated 80% of training data from an ML model using synthetic data feeds. 

For quality synthetic data, we need equally good data generation platforms that sync well with the dynamic needs of an enterprise. 

What Are the Critical Synthetic Data Use Cases? 

Synthetic data generation helps in building accurate ML models. Especially in scenarios when enterprises have to train their ML algorithms and the available data sets are highly imbalanced, synthetic data generation is of greater use. Before choosing a data platform, here’s a quick run through the possible use cases. 

• Synthetic data equips software QA processes with better test environments and, thus, better product performance. 
• Synthetic data supplements ML model training when production data is non-existent or scarce. 
• Authorize third parties and partners by distributing synthetic data without disclosing PII sets. Prime examples here would be financial and patient data. 
• Designers can use synthetic data to set benchmarks for evaluating product performance in a controlled environment. 
• Synthetic data enables behavioral simulations to test and validate hypotheses. 

What Are the Best Practices for Synthetic Data Generation? 

• Ensure Clean Data: This is thumb rule number one for any data practice. To avoid garbage-in and garbage-out-like situations, make sure you follow data harmonization. This means the same data attributes from different sources are mapped to the same column.
• Ensure Use case relevance: Different synthetic data generation techniques fit well for different use cases. Assess whether the chosen generation technique applies well.
• Maintain Statistical Similarity: The statistical properties should match and maintain the characteristics of the original dataset. It also includes keeping the attributes intact. 
• Preserve Data Privacy: Implement appropriate privacy-preserving measures to protect sensitive information in the generated data. This may involve anonymization, generalization, or differential privacy techniques.
• Validate Data Quality: Thoroughly validate the quality of the synthetic data against the original data. Assess the similarity regarding statistical properties, distribution patterns, and correlations.

Synthetic Data Generation by Business Entities

Now, entity-based data management is a totally different approach from what we have discussed so far. Simply put, storing or generating data for a particular business entity only ensures coherence and optimal utilization. Entity-based approach creates fake yet contextually relevant data sets that bring referential integrity. 

For example, in healthcare, this method could fabricate patient records with realistic medical histories, ensuring privacy while maintaining accuracy for research and analysis purposes. Likewise, it could create artificial yet nearly accurate data sets for business entities such as customers, devices, orders, etc. 

Entity-centric synthetic data generation is crucial for maintaining referential integrity and context-specific accuracy in simulated datasets, serving as a foundational strategy for diverse business applications such as testing, analytics, and machine learning model training. Here’s a quick run-through of the key benefits:

• Focused Entity Generation: Ensures all pertinent data for each business entity is contextually accurate and consistent across systems.
• Referential Integrity with Entity Model: Acts as a comprehensive guide, organizing and categorizing fields to maintain reference integrity during generation.
• Technique Varieties: Utilizes Generative AI for valid and consistent data, rule-based engines for specific field rules, entity cloning for replication with new identifiers, and data masking for secure provisioning.
• Consistency Across Applications: Whether training AI models or securing data for testing, the entity-based approach guarantees coherence and accuracy in synthetic data, preserving referential integrity at every stage.

While many products in the past have attempted entity-based models, only a few have succeeded. However, K2View emerged as the first product to introduce and patent entity-based models for its data fabric and mesh products. The fabric stores data for every business entity in an exclusive micro-database while storing millions of records. Their synthetic data generation tool covers the end-to-end lifecycle from sourcing, subsetting, pipelining, and other operations. The solution crafts precise, compliant, and lifelike synthetic data tailored for training ML models, trusted by several Fortune 500 enterprises.

In contrast, synthetic data generators like Gretel and MOSTLY AI, albeit without entity-based models, offer distinct advantages:

Gretel extends APIs to ML engineers, fostering the creation of anonymized, secure synthetic data while upholding privacy and integrity.

Meanwhile, MOSTLY AI, a newer platform, specializes in simulating real-world data and preserving detailed information granularity while safeguarding sensitive data.

Conclusion 

Given the rise in strictness for compliance, such as the GDPR, enterprises must take every step wisely. Otherwise, any breach, no matter how unintentional, could attract hefty penalties. Partnering with the right synthetic data platform will enable them to operate seamlessly across borders.