We are in the data era.
It’s marked by demand to collect, analyze, and capitalize on the trends, behaviors, and market shifts business data can reveal—“can” being the operative word.
It’s no surprise then that 89% of technology decision-makers view synthetic data as pivotal for maintaining a competitive edge. And it’s why recent estimates predict global demand for synthetic data generation will reach $2.1B in 2028.
Synthetic data helps organizations accomplish what original data cannot. It provides a novel approach to decision-making that uncovers trends and unlocks success, without compromising data security.
That may make it sound like a digital crystal ball. But it’s really more practical, and more magical, than that.
What is Synthetic Data?
Synthetic data is artificially generated data that can take many forms and structures. And while it “mimics” real-world data, it may or may not originate from actual data sources.
Synthetic data is particularly useful in cases where organizations need to analyze their data to drive business planning and innovation, yet are restricted from accessing the full spectrum of the information they’ve collected.
The greatest barrier to innovation today’s organizations face is lack of data access. Data siloed behind walls or in data lakes is unavailable to developers and testers for analysis. Any insights or conclusions drawn from incomplete datasets are not only unhelpful, they are potentially harmful to the future success of the business. Synthetic data lets organizations securely uncover previously hidden patterns and trends to finally reap the full value of data assets.
Why Embrace Synthetic Data Generation?
In the course of everyday business transactions, modern organizations collect a staggering amount of customer, industry, and competitive data. It’s a goldmine—if you know how to use it.
The insights hidden within this data provide critical inputs for everything from application development and testing to strategic forecasting to AI model training. Yet, access to much of it is restricted due to compliance and security regulations around the handling, processing, and storage of personally identifiable information (PII).
Organizations that deal with large volumes of highly sensitive information, like those in finance and healthcare, are most at risk and, conversely, more limited in data analysis capabilities.
Data breaches are expensive, literally and figuratively. No company can afford the severe reputational, legal, and financial repercussions they can bring. But to stay ahead of market trends and shifts, no company can afford to make operational decisions that aren’t 100% data-backed.
Synthetic data offers a game-changing solution. It allows organizations to remove PII* while preserving the structure and statistical properties of original data, so they can finally:
- Unlock data value without exposing it to risk
- Ensure compliance with privacy laws
- Safely share and analyze data across teams
* Please note that while synthetic data can effectively remove direct PII, it may still preserve the statistical distributions of an original dataset. As a result, it may retain sensitive business insights and should still be treated with appropriate security and privacy measures.
Synthetic Data Generation: Methods and Tools
Before moving forward with synthetic data, organizations need to realize that its accuracy and quality directly depend on two things: 1) a deep understanding of the original data being replaced; 2) the generation technique being used.
Here are just a few examples:
- Data Types: A date column in tabular data should remain in date format in the synthetic data.
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Format Preservation: Especially critical for application testing, where systems expect data in a specific format.
- Statistical Fidelity: To ensure synthetic data maintains the same distributions as original data, advanced generation techniques (e.g., those capturing and replicating statistical patterns) are essential.
5 Common Methods for Generating Synthetic Data
Once an organization is confident in its understanding of its original data, it’s ready to select a replication method.
The final choice ultimately depends on the type of data being synthesized and its intended purpose. Here are five of the most common methods used today:
- Statistical Distribution Modeling. Generates a synthetic dataset based on known statistical patterns, ensuring the generated data follows the same distributions as the original data.
- Direct Mathematical Modeling. Leverages mathematical and statistical models to create synthetic datasets with flexible and customizable statistical distributions, allowing for precise control over data characteristics.
- AI-Based Generation (VAEs & GANs). Generate new, synthetic data with similar statistical properties using AI models (Variational Autoencoders and Generative Adversarial Networks) trained on real-world data.
- Sequence Synthesis (Time-Series Data). Generates time-dependent synthetic data (e.g., transaction records, stock market trends, sensor data) that ensures the continuity and dependencies of sequential events.
- Large Language Models (LLMs). LLMs* excel at generating unstructured text data, making them useful for synthetic content creation related to document generation, chatbot training, and NLP-related tasks.
*LLMs, unless hosted on-premises, present one major drawback—potential data privacy risks. If an LLM requires real data samples in order to learn, that could inadvertently expose PII. In some cases, alternative methods (e.g., GANs) are a better option for producing high-fidelity synthetic data based on real datasets.
3 Off-the-Shelf Tools for Synthetic Data Generation
Build or buy: it’s the usual dilemma. Many organizations choose to build proprietary synthetic data generation systems. Others prefer a simpler approach out of the gate. A few of the ready-to-use commercial options gathering traction include:
- Tonic: Provides privacy detection, consistent data masking, and native integrations with common data sources. Also supports semi-structured and unstructured data generation.
- Hazy: Recently partnered with SAS to roll out SAS Data Maker, an upcoming solution for synthetic data generation.
- Gretel: Focuses on generating high-quality synthetic data for AI model training; emphasizes statistical accuracy while preserving privacy in generated datasets.
Unleash the Power of Synthetic Data in Your Organization
The power of synthetic data lies in more secure data analysis, leading to greater innovation, more accurate decision-making, and AI acceleration across the enterprise.
At S.i. Systems, we’ll help you harness this power to transform your business, ensuring regulatory compliance while generating high-quality output tailored to your needs. Let’s connect today to give you a competitive edge.
