PR Newswire reports 79% of global executives and 75% of business users to face data quality issues based on a survey of 1000+ executives and managers. Further, 69% of executives believe that data regulations hinder decision-making and call for better data management tools.
Organizations can solve such issues through Synthetic Data. Indeed, Gartner predicts that by 2024, 60% of data used in Artificial Intelligence (AI) and analytics will be synthetic.
But what exactly is synthetic data? And what tools can organizations use to generate it? This article answers such questions and discusses some other aspects of synthetic data.
Synthetic data is artificially generated that follows real-world patterns. Analysts can train AI and Machine Learning (ML) algorithms on real-world data and then use them to create synthetic or fake data. Synthetic data helps users build robust applications where real data is lacking.
In today’s digital age, companies must shift to synthetic data to assure customers that their information is in safe hands.
Synthetic data has several benefits over actual data.
Actual data can come in with several inaccuracies and errors that bog down the performance of data-driven applications. For example, real data may have a systematic bias, such as a dataset having an underrepresentation of a certain ethnic group.
However, once analysts have a synthetic data model, they can generate a clean and new dataset without such issues.
Analysts can use a synthetic data tool to generate as many data points as they want.
Often, companies cannot scale up ML applications due to the low availability of quality data for training and testing purposes. Synthetic data can help as it is a viable substitute for real data, and there is no limit to its volume.
Companies cannot collect or store sensitive data due to strict privacy laws. But they can overcome the difficulty by generating fake data resembling the actual one.
For example, organizations can generate fake customer data with all the necessary details like income, preferences, credit card history, etc. Since all of it is fake, there's no danger of breaching any law or cybercrime threats, such as data theft.
Since all a company needs is some actual data and a data generation tool, synthetic data is cost-effective for collecting data. It requires no extra devices, focus groups, surveys, questionnaires, or purchase of expensive data from third-party sellers.
Analysts have more control over synthetic data. They can change their data-generating models to suit new circumstances and get more relevant data that reflect the latest conditions.
Analysts can generate synthetic data using some state-of-the-art methods involving neural networks. Data scientists can create and train models based on the neural network architecture that can effectively learn non-linear distributional patterns of real data.
GANs have become a famous unsupervised learning method for generating fake data and also for reinforcement and semi-supervised learning tasks. The algorithm architecture consists of two neural networks. One is called the generator, and the other is called the discriminator.
The generator generates fake data, and the discriminator tells if the simulated data differs from the real one. With several thousand iterations, the discriminator cannot tell the difference as the generator produces very realistic synthetic data.
GANs are useful when analysts want to generate fake image data. But they can use it to generate numerical data also.
VAEs are unsupervised algorithms that learn patterns from the original data's distribution and "encode" the information in a latent distribution. The algorithm then maps or "decodes" it onto the original space and computes a "reconstruction error."
The objective is to minimize reconstruction error. The method is suitable for continuous data that have well-defined distributions.
NeRF takes a partially known 3-dimensional (3D) scene and generates new views. The neural network uses static images from the scene and then adds new view angles of the same scene. However, the algorithm is slow and may produce low-quality images.
Despite its benefits, generating synthetic data takes longer than it seems. There are several challenges that analysts face when working with synthetic data models.
Synthetic data generation requires complex models and expensive hardware that are costly to build and maintain.
Such costs are a hindrance for smaller organizations that don’t have the budget to invest in the required technology.
Data scientists can test the accuracy of synthetic data by comparing it with real data. However, if the real data itself has flaws, then no matter how great the accuracy is, synthetic data will be low-quality.
Finally, Personally Identifiable Information (PII) can be relevant for several analyses. Synthetic data generators remove PII to maintain privacy, which can be a problem for users who want to work with such information.
Synthetic data tools can help solve some of the challenges mentioned earlier. But with so many tools available in the market, it often becomes overwhelming. Technical details make it even more challenging to understand how a particular tool processes synthetic data behind the scenes. So to make things easier, below is a list of some factors that you can consider before making a purchase.
Business Requirement: Businesses must clearly define the reason for which they require synthetic data. It largely depends on the industry in which the business operates. For example, a retailer may have a different requirement for synthetic data than a healthcare professional.
A retailer may want a generator that can replicate transactional data, while a healthcare provider may want a tool that is good at understanding clinical data for patients.
Types of Synthetic Data: Synthetic data can be categorical, like gender data, numerical, like age data, or an image. Users need specialized tools to generate each type of data. There isn't a single tool that can do it all.
Cost: A company has three options for generating synthetic data. It can either create a generation algorithm in-house, get an open-source solution, or buy from a vendor. Building in-house solutions can be not only time-consuming but resource-intensive as well if it requires a business to hire experts. Open-source tools are easy to customize but challenging to implement and can trigger privacy issues. An out-of-the-box tool is easy to implement and learn due to vendor support but offers less customization.
A business must therefore weigh the pros and cons of each option carefully. The following section lists the top 15 third-party synthetic data tools companies can consider if they want to buy from a vendor.
With innovative deep-learning models and the ability to easily connect with popular database servers like MySQL, Datomize is a modern generator that is best for creating fake customer data for global banks. The models learn the fundamental distributional parameters of the original data and generate high-quality replicas.
The tool has a rules-based engine that lets analysts generate data for new scenarios. They provide rules for a given situation to provide context, and the engine produces the appropriate dataset.
Also, Datomize Integrates easily with existing ML pipelines through the Python Software Development Kit (SDK) and provides validation tools that visualize the similarity between original and synthetic data.
The company offers custom quotes depending on the nature and complexity of the problem. Users can fill out this form to get started.
Mostly AI is a no-code synthetic data generation solution for the insurance, banking, and telecom industries. The platform complies with Global Data Privacy Regulations (GDPR) and has stringent anonymization standards.
It has a System and Organizations Control (SOC) 2 Type II certification, which means it has the highest security and privacy standards. As such, businesses can trust Mostly AI's algorithm to handle sensitive customer data.
In addition, the tool allows data and ML engineers to take risk mitigation strategies proactively as they can quickly visualize multiple attacks.
Mostly AI offers a rich UI that lets analysts customize data generation settings. It also allows users to leverage the power of Graphical Processing Units (GPUs) and compute clusters for faster generation of large datasets.
The platform has a lifetime free option for individuals that allows up to 100K rows per day. Users must contact the company for a custom quote for the pro and enterprise options.
The healthcare industry often faces issues analyzing actual patient data due to several privacy issues. With MDClone however, such a problem is a thing of the past as the tool is made specifically for healthcare professionals to generate as much synthetic data as needed from actual patient profiles.
MDClone is based on the Ask, Discover, Act, Measure, and Share (ADAMS) infrastructure to provide data and foster collaboration, research, and innovation. It can generate synthetic data using any type of structured or unstructured patient-oriented data without exposing a patient's identity.
Healthcare professionals can frequently apply medical terminologies without needing to code and compare analytical results through in-depth visualizations. The tool allows professionals to share their findings and work on research projects using the synthetic data it generates.
Users can schedule a 15-minute call to get a demo. There isn’t a price or fee for MDClone.
Hazy is a synthetic data generator for the fintech industry as it helps in producing fake financial data. The platform complies with GDPR and allows banks or financial service providers to share customer insights without revealing a customer's true identity.
Its algorithms use differential privacy, meaning it is mathematically impossible to discover someone's true identity from the generated data.
Hazy can also quickly generate replicas of complex time series and transactional data without compromising any causal relationships between variables.
Users must contact the sales team directly to discuss their specific requirements to get started. Hazy will then give a customized price based on users' needs.
CVEDIA is a sophisticated computer vision cross-industry platform that uses synthetic data for running its AI and ML algorithms. Using its proprietary simulation engine SynCity, CVEDIA generates high-quality synthetic data that analysts can use for testing and training models based on the neural network architecture.
With wide applications in the security, manufacturing, and aerospace industries, CVEDIA provides a holistic platform through NVIDIA's Metropolis program, that covers both hardware and software requirements.
It features an advanced human detector called TALOS that can accurately identify human faces and uses the ACESCO tool to create heat maps for identifying areas with large crowds.
HERMES, one of the tool’s algorithms, classifies vehicle types and has a face anonymizer that blurs out the faces of actual humans, complying with GDPR standards. The algorithm saves time and money as users don’t have to pay extra for a vehicle detection system.
The platform offers a free personal license that supports research and development efforts. For synthetic data, users must contact the company to get a custom quote based on the requirements.
Synthesized is a data development framework designed as a DataOps solution to create high-quality data products. Users can quickly connect the platform to a database, data warehouse, or data lake and apply relevant transformations.
The tool uses an SDK that lets users reshape and anonymize the original data. It also optimizes model performance as it automatically creates a generative model that’s appropriate for a specific dataset.
Users can generate unlimited data for testing and training purposes while complying with SOC II standards throughout the Extract, Transform, Load (ETL) cycle. Also, the migration of apps to the cloud is 20% faster, and users can schedule hourly copies of the entire production database in the cloud.
The SDK is also available in Google Colab, making it more accessible to data scientists and engineers. The company also released FairLens, a Python library that integrates with Synthesized SDK to help users extract more insights from data and detect inherent biases.
There is a free version to generate unlimited data, but users need to contact the company to get a quote for more advanced features.
Those looking for a high-end 3D image generator should try Anyverse, which uses perception modeling as its fundamental framework. With a modular design, Anyverse easily creates and renders 3D scenes, allowing for smooth sensor simulation. Users can quickly train, design, validate and test the perception system's AI.
Anyverse uses a pure ray-tracing engine that efficiently computes the spectral radiance from each of an object's light beams and composes intricate scenes through static images. The scenes can have dynamic factors added through Python scripts that allow for adjusting the images according to changes in the environment.
Users must contact Anyverse directly to get an idea of the price. There's no free version available.
Sogeti offers synthetic data generation through its Artificial Data Amplifier (ADA) solution, which can generate both structured and unstructured data. The tool is scalable and can help analysts produce as much data as required using minimal samples of actual data.
ADA is suitable for applications in engineering, quality assurance, and research. It also complies with GDPR standards so that customer identity is completely anonymized. ADA customizes the data generation process according to the attributes of real data without requiring any manual effort.
Users can contact specific experts to get a price quote.
Gretel.ai APIs support robust generative models for handling several data types, such as tabular and time-series data, images, texts, and relational datasets. It uses five neural network models, including variants of GAN, Long-short-term-memory (LSTM) models, data amplifiers, and Generative Pre-trained Transformer (GPT) models.
The tool also features a Natural Language Processing (NLP) algorithm that protects customer privacy by automatically detecting and classifying sensitive text data, such as email addresses, names, phone numbers, etc.
With the ability to process data streams in real-time and several customization options for configurations, Gretel.ai gives the user full control over workflows for better management.
The company offers 15 credits for free, after which there's a charge of $2 per credit. Each credit can generate more than 100k synthetic data points.
Neurolabs uses computer vision to provide retail market solutions for managing inventory. Through its algorithms, retailers can quickly detect stock shortages, inaccurate prices, and misplaced items on shelves. It easily integrates with Enterprise Resource Planning (ERP) and Robot Process Automation (RPA) software to provide real-time analytics.
The company installs cameras to detect Stock Keeping Units (SKUs) and alerts the staff whenever there's an issue. The platform comes with a public catalog of more than 100,000 SKUs from which users can choose to build their detection system. Also, the tool automatically adjusts to changes in packaging or pricing.
However, if an SKU is missing, users can upload six photos of their item, and the platform will generate replica images of the product within 48 hours. Users can easily customize configurations without the need to code.
They can book a demo to get an idea of the price.
With an advanced GAN architecture as its back-end framework, Tonic is a mathematically robust platform that protects PII by applying differential privacy and database de-identification methods.
It also uses intelligent linking generators that learn complex data patterns through neural networks without requiring any input from the user.
Also, Tonic allows developers to synthesize only a subset of data rather than the entire database. The feature helps reduce data size through its proprietary cross-database subsetting algorithm.
In addition, the platform integrates with all popular SQL and NoSQL databases, along with continuous integration (CI)/continuous development (CD) pipelines. Users can try the free version or get a quote for the professional or enterprise options.
Facteus provides synthetic data on debit and credit card transactions to help hedge funds, marketers, and researchers analyze consumer behavior and get valuable insights.
The tool uses custom-built technologies called Quantamatics and Mimic. Quantamatics is an excel plug-in that helps analysts get complete datasets and forecast models. It can also integrate with other platforms like Jupyter Notebooks through Representational State Transfer (REST) Application Programming Interface (API).
Mimic is the synthetic data engine that anonymizes customer data to comply with regulatory standards, such as GDPR. The technology adds mathematical noise to sensitive information, making it impossible to reverse engineer the data fields to reveal anyone's true identity.
Users can contact Facteus to discuss their requirements and get a tailored solution.
OneView provides synthetic image data for mulitple use cases such as defense, urban planning, finance, and insurance. The platform has automation features that let users quickly create datasets, configure environmental variables, annotate data objects, and analyze data in detail.
With OneView, users can accelerate remote sensing imagery analytics efficiently. It also collects, tags, and validates real images from drones and satellites automatically.
It recreates the actual environment by adding randomization factors to each variable, such as weather conditions, appearance, textures, colors, etc.
Users can request a demo and get a custom price quote.
GenRocket is a Test Data Management (TDM) solution with over 700 data generators to support application testing. With a modular and adaptable design, GenRocket is highly scalable and operates on a self-service architecture allowing developers and testers to generate synthetic data where and when they want it.
Users can have complete control over the generation process as the tool allows them to configure several parameters to suit the test case requirements and generate dynamic data based on the test application.
It features eighteen query generators that can query data from SQL or NoSQL databases in real-time. GenRocket's generator can also produce synthetic historical data. Moreover, it can also integrate with any test automation tool.
Users can request a demo to get an idea of how the platform works and explain the requirements to their experts.
BizDataX enables security officers, banks, business and data analysts, and test data engineers to generate synthetic data to protect personally identifiable information (PII) in a pre-production environment.
The platform ensures that users comply with GDPR standards by applying data masking techniques. The tool features an automatic sensitive data discovery module to detect sensitive information across multiple databases.
It maintains referential integrity while reducing the size of databases used for testing. The company offers basic, standard, and premium versions. The basic version costs €7500. It’s a one-time license fee. Users can pay it in installments within three years.
Synthetic data provides input for several applications, including ML products and services. However, synthetic data is just one component in the ML production lifecycle. After data scientists build and test a model, it goes into the hands of ML engineers, who deploy the model in the actual customer's environment.
The deployment process can be long and frustrating. KDNuggets’ poll suggests that data scientists fail to deploy more than 80% of ML models. Challenges may include incompatible production environments, latency, disconnect between the engineering and data science teams, use of different tools to create and deploy models, etc.
Several tools are available to automate and streamline deployment to overcome such difficulties. Qwak is one tool that simplifies the process of bringing ML models to production at scale.
Qwak simplifies the productionization of machine learning models at scale. Qwak’s Feature Store and ML Platform empower data science and ML engineering teams to Build, Train and Deploy ML models to production continuously.
By abstracting the complexities of model deployment, integration, and optimization, Qwak brings agility and high velocity to all ML initiatives designed to transform business, innovate, and create a competitive advantage.
You can check out Qwak here.