What is Synthetic Data Generation?

A test data generator is a form of synthetic data generation tool, specific for testing.

It’s typically used when production data is not accessible, or when testing new functionality for which production data is not available.

Test data generators must be configurable, enabling data teams to request the amount, and type, of data they want to generate, as well as the characteristics it should have.  

Synthetic data, which is commonly used to test applications in a closed test environment before deployment to production, provides several benefits to testing teams. Not only does it give them total control over the datasets in use, but it also protects data privacy by delivering fake, but lifelike, information. Synthetic data can also be more efficient to use than production data because it allows testers to generate large amounts of test data quickly and easily. 

Synthetic test data generators are used for: 

• Simulation, which mimics the production conditions an application is expected to encounter 

• Boundary testing, which tests the limits an app can handle, like the largest input, or the maximum number of users 

• Load testing, which stress-tests a system to ensure that it can accommodate massive amounts of data or simultaneous users 

• Progression testing, which tests new functionality that has been developed

Synthetic Test Data Advantages 
Synthetic test data is critical to testing and DevOps teams, in cases where there’s not enough complete or relevant real-life data at hand.

Not only does synthetic test data reduce the noncompliance and security risks (associated with using actual data in a test environment), but it’s also ideal for validating new applications, for which no data exists. In such cases, testing teams can match their requirements to the closest profiles available. 

For test data management teams, whether the data is real or fake may not be an important criterion. More critical are the balance, bias, and quality of the datasets – and that the data maximizes test coverage. The benefits of synthetic test data include:

1. Enhanced data quality 

   Production data can be rife with biases, errors, and duplications that can negatively affect the reliability of testing processes. Synthetic data can enhance data quality, variety, and balance. Synthetic data generation tools automate a variety of tasks aimed at improving the quality and uniformity of the data, including: 

   – Unifying data from multiple sources (often in multiple formats) 

   – Identifying and erasing duplicate data 

   – Eliminating erroneous records 

2. Greater scalability 

   The quantity of complete, high-quality, and reliable data in real-life subsets is often insufficient for meaningful software testing. At times, it’s easier to define the parameters for synthetic data generation than it is to derive rules-based test data. Therefore, integrating synthetic test data with real data provides greater flexibility and scalability for testers.  

3. Better data protection

   Data masking is the gold standard for protecting sensitive data in testing environments. Although data masking tools keep private information secure, to eliminate risk, they must be executed perfectly. Synthetic data generation tools eliminate data privacy risk completely. Since synthetic data is artificial, DevOps and testing teams may prefer a risk-free way to test new software.

In the synthetic test data vs test data masking comparison, IT and testing teams must decide which model suits their specific needs best.

An AI data generator employs artificial intelligence (AI) to generate synthetic data. It can be used for many different use cases, such as data privacy management, business process optimization, application testing, and machine learning model training. A number of different techniques are used to generate synthetic data via AI, including the: 

• Crafting of synthetic data from existing datasets 

• Creation of new data points using generative models 

• Simulation of real-life scenarios 

AI data generators are designed to be flexible and customizable, enabling users to specify the format and quantity of the data they’d like to generate, as well as the characteristics they should have.  

Since AI system training and evaluation often depend on a large amount of data for learning and decision-making, synthetic data can be used to replace, or augment, production data under certain circumstances. For instance, synthetic data generation could benefit: 

• Autonomous driving 

• Image classification 

• Language processing 

Synthetic data is ideal for such situations because it can be generated in massive quantities, and manipulated to reflect a complex chain of events that rarely occurs in the real world. 

In addition to training and evaluation, synthetic data can also be used to test the reliability and speed of AI systems in a simulated environment – before they are deployed in the field. This helps ensure that the systems behave as expected when they are used in practice. 

To summarize, synthetic data can be a valuable tool in the development and testing of AI systems – especially when production data is unavailable, or hard to get.

In the fields of machine learning and computer vision, synthetic training data is often preferred over production data because it can be created in large quantities, and applied to many different uses cases. 
 
Synthetic training data is in demand due to the:  

• Difficulty involved in collecting a large enough real-life dataset, such as in the case of a rare event, such as an accident 

• Limited availability of labeled data, such as in the case of medical imaging that requires expert knowledge and time 

• Demand for augmented production datasets, to create bigger datasets aimed at improving performance on unseen data 

• Need for controlled test environments 

Machine learning data generation
Machine learning data generation is the process of creating synthetic data for the purpose of training machine learning models to accurately perform a specific task.  

Synthetic data can be used to train machine learning models when there isn’t enough (or any) production data. For example, synthetic data could be used to: 

• Perform object recognition 

• Improve performance, by debugging and fine-tuning 

• Generate training examples similar enough to, yet slightly different from, real life 

• Evaluate the models, in comparison to the golden record 

Computer vision training data
Computer vision is an interdisciplinary field that deals with how computers interpret and understand real-world visual information in the form of images and videos. It requires its own training data because machine learning models consume massive quantities of labeled data to learn, and make accurate predictions. The quality and diversity of the computer vision training data have a direct impact on the accuracy and reliability of the model. 

The more complex the task, the greater the need for computer vision training data. The training data also needs to be diverse as possible, because models trained on narrow datasets may not adapt well to new data. Complexity and diversity expose the model to a wider range of possible scenarios and conditions.

Synthetic data refers to artificially generated information that’s similar to real life, but not associated with actual events or people. The 3 most common types of synthetic data are: 

• Synthesized audio, which imitates real-life sounds 

• Synthesized images, which depict realistic-looking visuals  

• Synthesized text, which mimics the writing of human beings  

All of these are used in simulations or virtual reality environments, to test audio/image/text recognition algorithms, and train machine learning models. Additional types of synthetic data that can be generated, include synthesized financial data, sensor data, and video.  

Computer vision, for instance, supplies many synthetic data examples. In this field, artificial information is used to: 

• Generate images of objects in different positions, and orientations, and scales – by rendering 3D models of objects in a virtual environment and applying various effects such as rotation, scaling, and lighting. Object detection trains object detection models that can detect objects in different scenarios and environments. 

• Create new images using generative models that are similar to the training data. Image synthesis is useful in generating a plausible image from incomplete inputs. 

• Simulate different lighting, traffic, and weather conditions in a virtual environment, in order to train perception models in self-driving cars. 

• Form realistic images of anatomical structures, or diseases, for testing and training machine learning healthcare models. Medical imaging is used to overcome the limitations of insufficient labeled data from real patients. 

In its 2022 report, When and How to Use Synthetic Data, Gartner lists 4 primary ways to generate synthetic data, in order of complexity (from easiest, to must difficult to accomplish):

1. Rule-based, and intelligent rule-based

   Rule-based techniques create data via business rules. For instance, if you had 10 products and 1,000 customers, a rule might be might to generate a record for each customer, to buy each product. Intelligence can be added to the fake data by referencing the relationships between the elements, such as using primary and secondary keys between tables (in more complicated relational models), to ensure the relational integrity of the generated data.

2. Statistical model-based

   This method creates artificial data similar to the real data, via statistical distributions of the original information. It determines which statistical distributions to keep, and which to augment, in order to minimize bias.

3. Generative Adversarial Network (GAN)

   A GAN is a machine learning technique that uses parts of the actual data, and is comprised of 2 neural network models – a generator, and an opposing discriminator. A generative network creates candidates, and the opposing discriminative network attempts to identify which candidates aren’t real. GANs support both structured and unstructured data.

4. Agent-based modeling

   To examine the impact of actions on large populations, agent-based modeling simulates the actions and reactions of autonomous agents that represent individuals or groups of individuals. This method is especially useful for situations with complex interdependencies, such as equity trading.

Here’s a synopsis of the pros and cons for each method used to generate synthetic data:

The first 2 approaches represent rule-based methods, which are widely used by enterprises today due to their speed, scalability, and cost-effectiveness.

The more complex methods, such as GAN and agent-based modeling, use AI technology which is early in its maturity level. While likely to deliver more accurate synthetic data, these techniques usually require more time and human resources to implement.

Privacy challenges

Synthetic data is not inherently private, because unique values may remain embedded within it, thus providing a possible key to the real world. For instance, in a dataset indicating net worth, if only one billionaire is included in that group, then that individual can easily be mapped to the single billionaire appearing in the synthetically generated dataset. To avoid such situations, data teams can either change distributions, or add differential noise to the data.

The most appropriate method for your organization depends on the specific requirements and constraints of the application at hand.

There are various synthetic data generation tools on the market today. Before selecting one, make sure it includes the following key features: 

• Ability to create datasets where no source data for AI/ML model training exists
  If there’s no source data available to train your AI/ML model, you will still want the ability to create realistic test data. Sophisticated synthetic data generation tools can automatically generate life-like data based on the required fields.  

• AI- and ML-based synthetic data generation
  Unlike rule-based data generation, which relies on rules defined by people, Artificial Intelligence and Machine Learning models are trained on real production data to replicate its structure and the information it contains. AI/ML-based data generation produces synthetic entities on demand to enable speed and scalability.  

• Assurance of relational integrity
  Synthetic data generation tools that maintain relational integrity use metadata, schemas, and rules to learn the relationship between data objects and preserve relational consistency of the data.  

• Connection with all databases and automation pipelines
  Your synthetic data generation tool must easily integrate with the databases you have in use (or can see your organization using in the future), as well as test automation and CI/CD tools. 

• Emulation of distributed values in the real source data
  Emulations allow you to filter data to accurately segment it. For example, a synthetic data generation tool that allows you to emulate the distribution of values in source data can tell you what percentage of your customers live in one city versus another.  

• GUI built into TDM data product for synthetic data generation
  Often, synthetic data generation tools are sold separately from general test data management solutions, meaning companies that want this capability need to pay an additional cost and use two sets of user interfaces. Instead, look for a solution whose test data management suite includes synthetic data generation. This helps you avoid added costs and implementation requirements that compound over time.  

• Rules-based synthetic data generation
  This method of synthetic test data generation fulfills a primary synthetic test data use case: augmenting existing test datasets where data is sparse. For example, if you need more customer data, you can configure the synthetic data generation tool to fill out and add combinations and permutations to the data you have.  

• Self-service synthetic data generation 
  A self-service synthetic data generation tool lets testing teams provision data by themselves, independent of a centralized system that few can operate. In the spirit of an agile environment, this is a decisive factor. 

There are many potential use cases for synthetic data. In many of them, synthetic data is used to train machine learning models to: 

• Detect unusual activities/transactions 

• Pilot new financial products (such as algorithmic trading systems, or customer-facing financial apps) in a simulated environment, before making them available to the public 

• Predict patient outcomes, or identify potential health hazards 

• Validate new medical devices or treatments, before they are deployed in the real world 

• Optimize network performance, or predict outages 

• Test new telecom services (like virtual assistants, or customer service chatbots) in a closed test environment, prior to general release 

Synthetic data generation has been used successfully in a number of real-life applications across a wide range of industries. Some examples of successful real-life applications of synthetic data include: 

• Healthcare – patient privacy protection 

• Finance – fraud detection, credit scoring 

• Retail – customer experience improvement 

• Manufacturing – process planning 

• Telecommunications – churn prediction, network usage analysis 

• Transportation – network/logistics optimization  

Here are the top 6 companies that specialize in synthetic data generation: 

1. K2view
   K2view Synthetic Data Generation employs various synthetic data manufacturing techniques to create synthetic data for software testing and ML model training. It implements a patented, business entity approach, ensuring that the generated data is consistent and complete, regardless of the number and type of systems the data needs to be provisioned to. K2view has been successfully implemented in numerous Fortune 500 companies around the world. 

2. Gretel
   

   Gretel provides a comprehensive synthetic data platform for developers and ML/AI engineers that use the platform's APIs to generate anonymized and safe synthetic data while preserving data privacy.

3. MOSTLY AI
   The MOSTLY AI synthetic data platform enables enterprises to unlock, share, fix, and simulate data. Although similar to actual data, its synthetic data retains valuable, granular-level information, while assuring private information is protected. 

4. Syntho
   The Syntho AI-based engine generates a completely new, artificial dataset that reproduces the statistical characteristics of the original data. It allows users to minimize privacy risk, maximize data utility, and boost innovation through data sharing

5. YData
   The YData data-centric platform accelerates the development and RoI of AI solutions by improving the quality of training datasets. Data teams can use automated data quality profiling and improve datasets, leveraging state-of-the-art synthetic data generation. 

6. Hazy
   Hazy models are capable of generating high quality synthetic data with a differential privacy mechanism. Data can be tabular, sequential (containing time-dependent events, like bank transactions), or dispersed through several tables in a relational database. 

This is just the top of the list. There are many other providers of synthetic data generation solutions. The market is continually evolving, in accordance with the changing demands of the marketplace. 

Technology companies are researching 5 potential future directions for synthetic data generation, including: 

1. Enhanced data quality and reliability 

   Since data professionals are constantly concerned with ways to improve the quality and reliability of synthetic data, they’re developing better ways to evaluate it – for instance,  identifying any biases or inaccuracies that might present themselves. 

2. Ethical and legal perspectives 

   With the spread of synthetic data, legislators and regulators are paying more attention to its ethical and legal implications. IT and business teams need to be aware of these issues, and take them into account, as they develop. 

3. Improved data generation algorithms 

   The consistent objective of synthetic data is to make it as true to life, and as representative of production data, as possible. Towards this end, researchers are constantly developing more sophisticated machine learning models, including deep learning and Generative Adversarial Networks (GANs), to achieve this goal. 

4. Integration with production data 

   By integrating synthetic data with real-life data, data teams hope to generate more realistic and comprehensive datasets. For example, synthetic data could be used to close any gaps in actual datasets, augment real-life information to cover a broader scope of edge cases and create test data to cover new application functionality being developed. 

5. Use in emerging applications 

   Synthetic data is increasingly being used in new applications, such as autonomous automobiles and virtual reality. Researchers are exploring how synthetic data can be used to improve the performance of AI systems in these, and other, emerging technologies.

Best-of-breed enterprise synthetic data generation tools create synthetic data based on a business entity data model – where a business entity can be a customer, account, product, or work order. They ensure that the generated data is consistent and complete, regardless of the number of disparate systems that the data needs to be provisioned to.

The combination of business entity data modeling, with intelligent business rules, results in highly pragmatic approach synthetic data generation. The artificial datasets produced by this method are as realistic and balanced as you need them to be, in order to support your test cases, or train your ML models.

Rapidly evolving data privacy and security laws are putting synthetic data generation on the fast track for enterprise IT.  

This paper examined the definitions of, and requirements for, synthetic data generation, highlighting examples, methods, features, use cases, vendors, and predictions for the future. 

It concludes by presenting a business entity platform approach to synthetic data generation, which outperforms the current standalone or homegrown solutions – and is proving to be significantly more cost-effective as well.