Synthetic Data Solutions Power Data-Driven Innovation

Learn how to realize the full potential of your data – while safeguarding individual privacy and maximizing data security – with synthetic data solutions. 

Table of Contents

What are Synthetic Data Solutions?
Why Synthetic Data Solutions are Necessary
How Enterprises Use Synthetic Data Solutions
The Challenges of Synthetic Data Solutions
Synthetic Data Solutions via Business Entities

What are Synthetic Data Solutions?

Ideal for data science and privacy-sensitive applications, synthetic data solutions generate artificial datasets that closely resemble real-world data without exposing sensitive or Personally Identifiable Information (PII). Advanced statistical and generative AI synthetic data techniques are used to generate fake data that retains the same statistical patterns, properties, and relationships of real data.

Synthetic data solutions are used for numerous purposes, such as testing software without exposing actual user data, training Machine Learning (ML) models when genuine data is scarce or unavailable, complying with data protection regulations (like GDPR, CPRA, and HIPAA), and sharing data more securely.

In a data-centric world where privacy concerns remain top-of-mind, synthetic data generation offer organizations a practical way to harness the benefits of data-driven insights, while still protecting privacy and confidentiality.

Why Synthetic Data Solutions are Necessary

Synthetic data solutions are needed to:

1. Generate more data
   When testing software apps or training ML models, developers or data scientists need large, accurately labeled, realistically diverse datasets. Unfortunately, it’s often too time-consuming, expensive, and impractical to gather and label the massive volumes of data that are required – or the real data doesn’t exist at all. Synthetic data solutions resolve this issue in a cost-effective and timely manner, enabling functionality and negative testing.

2. Reduce costs
   Using synthetic data for training ML models and other purposes can dramatically reduce costs. For example, a training image that would cost $5 if legally sourced from a data labeling service could cost a fraction of this if generated artificially.

3. Ensure privacy and quality
   Synthetic data solutions eliminate the privacy issues generally associated with data sourced from the real world. A synthetic dataset also has lower bias than a real dataset. And, since actual data doesn’t necessarily reflect the full range of information in the real world, synthetic data offers greater diversity (including rare cases that are realistic yet difficult to source from actual data). Less bias + more diversity = better quality.

4. Rebalance data
   Enterprises that know how to create synthetic data can rebalance imbalanced data, in which certain classes may not be represented properly. By creating synthetic samples for minority classes, class distribution in a dataset can be made more equitable, reducing bias and improving model performance on underrepresented classes.

How Enterprises Use Synthetic Data Solutions

Companies leverage synthetic data solutions to address numerous challenges and achieve business goals. The versatility of synthetic data solutions across various industries – such as financial services, healthcare, and retail – clearly illustrates how firms use synthetic data solutions to balance data-driven innovation with privacy and security concerns.

Here are 8 ways that organizations leverage synthetic data solutions to unlock valuable insights while protecting sensitive information:

1. Testing software – in a more controlled environment with synthetic test data

2. Training ML models – in case actual data is unavailable, limited, or doesn’t exist

3. Preserving privacy – by replacing actual data with fake data to better comply with data protection regulations

4. Sharing data – with partners, external researchers, or vendors without revealing real-world customer information

5. Detecting anomalies – by simulating a wide range of attack scenarios for better cybersecurity and fraud detection

6. Improving healthcare analytics – by applying synthetic data in healthcare settings (e.g., medical research, predictive modeling, and clinical trials) without compromising privacy

7. Empowering financial services – by assessing risk, detecting fraud, and complying with privacy laws – without revealing any personal data

8. Enhancing marketing – by analyzing customer behavior, testing marketing strategies, and improving personalization – while protecting customer identities

The Challenges of Synthetic Data Solutions

Synthetic data solutions offer many benefits, yet challenges remain. Organizations should carefully consider the following 10 challenges, customizing their synthetic data strategies to better balance the benefits of data utility with privacy protection:

1. Privacy risks, where threat actors could reverse-engineer or de-identify synthetic data

2. Regulatory compliance, with the ever-changing landscape of data protection laws

3. Complex data types, like text, images, or time-series data

4. Insufficient realism, leading to unreliable modeling or analysis

5. Scalability, in the sense that generating large datasets can be time- and resource-intensive

6. Bias, which is sometimes inherited from original data used to train models

7. Data dependency, which happens when access to original data is needed for training

8. Feature engineering, which may be required to maintain meaningful relationships and patterns between variables

9. Validation and evaluation, which can be problematic since metrics and methods may lack ground truth for comparison

10. Cost and resources, where skilled data scientists and additional computational resources may be needed

Entity-based Synthetic Data Solutions

Data scientists, researchers and other data professionals are transitioning to advanced entity-based synthetic data generation tools to create more realistic synthetic data. The most advanced synthetic data solutions leverage business entities (such as customers, orders, or loans), which are automatically modeled based on metadata from the original datasets.

Entity-based synthetic data solutions use a variety of different data generation techniques alone or together, including:

1. Generative AI

2. Rules engine

3. Entity cloning

4. Data masking

K2view is one of the only synthetic data companies to support all 4 techniques.