Synthetic data, as the name says, is something that is artificially made by AI programs. It can be everything from text, image, voice, and even video footage.
Now the real question – Why not simply use the real data?
The reason is the lack of control over data.
Amazon alone generates over 1000 petabytes of data every day. Many other tech or social media giants too generate massive amounts of user data. But the control of these real data is under a handful of tech giants only.
The smaller companies or startups, however, don’t have access to such abundance. And therefore, synthetic data can be a profitable opportunity to train prototypes and create models.
Also, the fact that digitization has paved the way for companies to capture our data to train their ML models. This isn’t an issue for us as long as they use our data to generate revenue.
But the big problem occurs when the hacker breaks into a system and is able to retrieve sensitive data.
The use of traditional anonymization techniques is yet another problem. The technique uses pseudonymization, row and column shuffling, directory replacement, and encryption.
Though it looks promising, studies reveal that the identity of 80% of credit card holders can be re-identified from the last 3 transactions and 87% of them are at risk if their birth date, gender, and postcode are exposed.
To overcome this problem, companies are now shifting to synthetic data generation tools. While they offer an alternative way to capture real-world data, the processed data stays uncompromised.
What is synthetic data generation?
Synthetic data generation is a mathematical and statistical process performed by machine learning models that are trained using real objects, people, and the environment. However, the output data doesn’t carry any sensitive data but preserves the behavioral features of real data.
Synthetic data generation is not just an innovation but a solution for accurate, secure, and cost-effective data modeling. According to Gartner, synthetic data is going to overshadow real data by 2030. Moreover, the impact is already visible where some startups are capitalizing on this innovation.
What are the benefits of synthetic data?
- Synthetic data generation is a secure, fast, and scalable solution as compared to traditional anonymization tools.
- It saves time and cost by automating the manual and mundane preparation of data.
- Real-world data is highly biased towards particular outcomes or categories. Synthetic data removes such skewed behavior and provides a diverse outlook on the possibilities.
- It provides complete control over the data where developers can adjust parameters to adapt to changing circumstances.
- With the help of synthetic data, researchers can model scenarios that may not exist that could potentially foster innovation.
- Without compromising sensitive data, marketers can create a customer persona that resembles a real customer journey and behavior.
- Rebalancing features may help to mitigate the inaccuracies and missing information to provide a comprehensive and quality dataset.
- It can ensure data fairness by fair distribution of data while complying with the privacy policies.
- Synthetic data can be potentially applied to almost all sectors – IT and software, retail, finance, defense, healthcare, agriculture, food production, construction, gaming, and many more.
Best tools to generate synthetic data
I am now going to share the 15 best platforms for synthetic data generation that can potentially reduce the gap between the real-world and simulated world.
Hazy is a UK-based synthetic data generation startup that aims to train raw banking data for fintech industries. Hazy’s data modeling lets the developers ramp up analytics workflows while avoiding any high risk of fraudulence as found while collecting real customer data.
Even though banks are supposed to provide APIs for data privacy and transparency, working on Hazy’s synthetic data gives a sigh of relief as it offers safety and complies with GDPR policies.
A complex amount of data is generated during financial services and is generally stored in silos within companies. However, sharing real financial data within different lines of businesses especially for research purposes is severely restricted under law.
This also prevents companies from creating new revenue streams while selling valuable customer insights. Hazy, therefore, makes sure companies can monetize data by selling insights, not identities.
Sharing data is a part of operational requirements for banking institutions. This allows them to understand customer behaviors based on the transactions and synthesize new samples without storing any real data or part of it. But having synthesized customer data in hand, mapping back to the original form is just impossible.
Hazy also ensures to manage such resource-intensive processes within days that could otherwise take months.
Launched in 2020, Datomize is one of the top startups and an emerging synthetic data generation tool. Datomize’s AI/ML modeling is geared towards customer data from global banks. Having a vendor that understands technical requirements and respects the regulatory board is half the battle won. And Datomize truly stands out as a successful artificial data generator, 3rd party collaborator in testing, development, innovation as well as monetization.
With Datomize, you can easily connect to enterprise data servers such as PostgreSQL, MySQL, Oracle, etc., and process complex data structures and dependencies with hundreds and thousands of tables. The algorithm then extracts behavioral features of the raw data and creates identical data twin but completely unrelated from the original data.
With the APIs integration, cloud collaboration, data simulation, and privacy features, Datomize provides state-of-art AI solutions.
Recently, Datomize secured $6 million worth of seed funding for the commercialization of its services and enabling the organizations to ramp up their digital transformation services.
Often called “The fake data company”, Tonic.ai offers an automated and anonymized way of data synthesis for use in testing and development. The platform also implements database deidentification which means to filter out Personally Identifiable Data (PII) from the real data and protect customer privacy.
Tonic’s robust AI algorithm categorizes different tables across databases with the help of the Generative Adversarial Network or GAN model.
It consists of two sub-models – Generator and Discriminator. They work against each other.
The generator fetches real input data and creates similar yet completely new data instances. Discriminator, which is trained from a generator on both real and synthetic data, distinguishes between both the data. The process continues as long as the discriminator couldn’t differentiate between the real and fake data.
The platform preserves behaviors and dependencies within the data and makes it easy for the data science team to work with equally valuable data by eliminating hours of manual work.
Recently, Tonic has launched Smart Linking Generator which makes use of neural networks to mimic complex datasets that require a lot of categorization.
Mostly.AI is a Vienna-based synthetic data platform that serves industries like insurance, banking, and telecom. It enables cutting-edge AI and top-tier privacy while extracting patterns and structure from original data to prepare completely different datasets.
Mostly.AI strictly adheres to the privacy laws of GDPR and claims to be the first synthetic data generator to have received SOC 2 Type 2 certification. This means the company’s work ethics comply with security, transparency, and confidentiality.
The platform allows users to sample raw data from scratch while considering the probability of creating synthetic data that they bear no direct relationship with.
But this also means there’s no two-way system where re-identification of the synthesized data could be reversed.
Not just data synthesis, Mostly.AI’s machine learning system also paves way for data engineers to visualize multiple attack scenarios and take risk-aversive measures if needed.
Packed with predictive modeling, advanced analytics, and fraud detection features, Mostly.AI is a go-to software for businesses looking for seamless performance, collaboration, and innovation.
Sogeti is a subsidiary of Capgemini Group and a cognitive-based solution for data processing and synthesis. It uses natively built technology called Artificial Data Amplifier (ADA) that can learn and reason any type of data fed to it whether it’s structured documents or unstructured documents such as handwritten, photos, scanned or tabular copies.
ADA utilizes deep learning methods that can mimic recognition capabilities like humans and sets Sogeti apart from its competitors.
Whether it comes to data extraction, the ADA system can identify the relevance of the information to be processed and classify them according to the categories.
Once the synthetic data is generated, Sogeti retains data characteristics and correlations that are statically similar to the original data but with the absence of any identity. With the implementation of the ADA system, Sogeti has emerged as one of the best data science-based solutions specifically for engineering, research, quality assurance, and testing.
Synthesized.io is an all-in-One AI dataOps solution for data provisioning, augmentation, secure sharing, and collaboration.
The platform generates versions of the original data as well as multiple test data scenarios where it identifies missing values and sensitive information. The company understands the issue of data imbalance such as missing values or biased information when it comes to predictive modeling.
Therefore, the platform uses an automated generative model called Synthesized SDK that helps to reshape data as needed. The model can make sure the data is relevant, free from any bias, and doesn’t contain sensitive information. Moreover, data engineers can also anonymize data for repurposing.
Recently, the company released Synthesized SDK in Google Colab which makes an ideal platform to derive critical insights and work together on deep learning libraries. The platform lets R&D teams and enterprise customers familiarize each other and allows them to learn the features to create high-end synthetic data.
Besides, the company also launched an open-source Python library called FairLens that works with Synthesized SDK and allows developers to gain data insights, discover biases, and ensure fair use of data.
Synthesized SDK is now trusted by several insurance and banking firms and makes it a demanding platform for data scientists to deliver faster and better results than conventional synthesizing techniques.
YData is a Portugal-based startup that helps data scientists to resolve the issues of poor quality of data or access to large user data using scalable AI solutions.
The company offers proprietary tools and automated frameworks to ease the process of accessing data, profiling, and generating synthetic data while following user privacy and protection compliance.
YData not only delivers high-quality synthesized data but ensures they are free from bias or any PII (Personally Identifiable Information). Though the platform was launched in 2019, the service has already been adopted by many organizations such as retail, finance, healthcare, telecom, and even public utilities like electricity or water supply.
While performing tests like inference attacks, YData engineers keep accountability of any risks of identity leakage or re-identification. Therefore, they make use of the TSTR (Train Synthetic Test Real) method that evaluates the capability of AI-generated data to be used for training prediction models.
YData recently closed $2.7 million worth of seed funding to expand its services across the globe thus enabling partners to utilize its data generation capabilities.
The digitalization of the healthcare industry has generated an abundance of patient data that allows the industry to harness the information for personalized care.
However, to access the clinical data, researchers had to depend on the mediators to access patients’ data. The process was, however, slow and limited the flexibility and accessibility of data. The patient’s privacy was also a major concern.
MDClone, therefore, offers a systematic approach to democratize healthcare data for research, analytics, and synthesis in real-time without infringing the sensitive data of patients.
This Israeli-based life sciences platform uses ADAMS infrastructure (ask, discover, act, measure, and share) to help the users to overcome common barriers such as data availability, innovation, security, and foster new partnership opportunities.
MDClone has taken a transformational step in providing cloned yet anonymous patient data. The technology helps to create synthetic data based on real statistical characteristics of patients without actually having such patients.
With the help of fictional patient data, healthcare providers can have a diverse range of information based on the patient’s age, gender, medical history, etc. For example, they can study the reaction of different types of medications prescribed for specific diseases that can help them find better treatment measures.
Currently, in the beta stage, Gretel.ai is one of the budding platforms for creating synthetic data. Gretel is self-proclaimed as “Privacy Engineering as Service” that generates statistically equivalent datasets without retaining any sensitive customer data from the original source.
While training the data for synthesis, Gretel’s ML algorithm compares and contrasts real-time information by utilizing a sequence-to-sequence model to enable the prediction while generating new data. The platform is powered with its neural network called Long Short-term Memory (LSTM) to mimic any structured data from the original ones.
Gretel also implements differential privacy that ensures no original data is memorized or re-identified in the system.
As far as Gretel’s credibility is concerned, training the datasets has resulted in over 70% accuracy thus maintaining the relevancy of the generated data.
Although Gretel is yet to launch its commercial-grade services, the company has reached proof-of-concept with many prospects. In its early stage, Gretel looks promising as a next-gen data generator as the platform vows to work on finances, healthcare, and gaming sectors in the near future.
Facteus is a fintech-based synthetic data generator that captures actionable insights from transaction details such as credit and debit card transactions without compromising the sensitive details of the customers.
The platform uses native technologies, Mimic and Quantamatics to perform analytics, testing, training, and cloud sharing while complying with regulatory and privacy laws.
Mimic’s data synthesis technology enables engineers to access high-quality user data, ramp up innovation and generate new revenue systems. While Mimic helps in generating synthetic data, Quantamatics allows users to fill up the missing piece of the information and predict future performance.
Facteus has recently joined hands with Pacific Epoch, an investment research company to offer synthesized US consumer spending details to investors in the Asian market. This partnership will provide investors with unique market insights to strengthen investment models across Asia and US.
The company is also working with Snowflake Partner Network to leverage the data migration, use of analytics tools, and computing capabilities of the platform using Snowflake’s cloud services.
Perception modeling is cutting-edge technology in machine learning. Anyverse uses this technology to create synthetically simulated 3D environments. This is all about capturing real-world footage and producing sensor-specific data that needs to be validated, trained, and tested.
Anyverse renders and configures different scenarios for sample data with the help of a ray-tracing engine. The technology computes the interaction of light beams with the objects in the scene at a physical level. This is quite useful in recreating completely different scenes and dynamic properties while filling any data gaps in the original scene.
Last year, Anyverse partnered with Lidar-based safety solution provider Velodyne to serve rapidly growing autonomous development industries.
The platform supports a wide range of use cases where developers can programmatically control the captured footage and generate multiple versions of the data.
For example, training smart cameras for drones, vehicles or indoor scenarios such as CCTV allows the platform to collect rich databases and use them to model scenarios. This can be helpful in modeling smart cities or traffic congestion issues while capturing people’s behavior and physics involved in the real world.
Packed with multiple ML algorithms, CVEDIA offers synthetic computer vision solutions for improved object recognition and AI rendering.
The platform works along with NVIDIA’s Metropolis program that encompasses hardware and software for high-end engineering solutions. It uses a suite of tools, sensors, and IoT for the development of advanced AI applications.
For example, CVEDIA’s TALOS is an advanced human detector that can identify faces with high accuracy even in crowd situations.
In the pandemic-hit world, social distancing has become a new norm. ACESO tool, therefore, uses heat maps to gain insights into public hotspot areas and their behavior.
Similarly, HERMES uses a deep learning algorithm to classify types of vehicles from bicycles to buses. Though the tool anonymizes the license plates ensuring compliance with GDPR regulation.
The same applies to TALOS and ACESO that ensures the faces are absolutely unrecognizable.
Besides, TALOS can also be used to detect guns and rifles as well as compare normal pedestrians and gun holders while retaining accuracy in busy environments.
Neurolabs is a Romania-based synthetic data platform that uses computer vision models for the grocery market. Retail outlets often face issues with real-time inventory management, misplaced or missing products, and out-of-stock situations. Neurolab, therefore, integrates its homemade solution called Re-Shelf to monitor such issues and alert the in-house staff.
Re-Shelf uses machine learning and computer-generated imagery to train pixel-perfect data in real-time. The platform allows users to access thousands of popular Stock keeping units (product code used to identify stock from the lists or invoices) and use them to add synthetic product images that are missing on the database.
For optimum performance, they use automated feedback loops between the computer vision model and synthesized data. This results in a high-quality 3d replica that closely mimics color and lighting variations of real products.
Neurolabs claims its object recognition capability is about 20 times faster and 100 times cheaper.
The platform is currently working on several use cases such as identifying manufacturing defects, sorting and crop grading in agriculture, waste recycling, etc.
Rendered.AI generates physics-based synthetic datasets for satellite, autonomous vehicles, robotics, and healthcare industries.
The company claims to make synthetic data generation as easy as the click of a button. There is a no-code configuration tool and APIs for engineers to make quick changes and analytics on datasets. They can perform data generation on the browser and enable easy operation on machine learning workflow without much computing power.
Say, a company wants to introduce a new type of sensor for satellite image sensing and wants to receive funding for the project. In case, the company would need real data to demonstrate the application. Therefore, a synthetic data solution from Rendered AI comes into play where the company can render various real-time scenarios and replicate satellite-like images.
The platform also enables collaboration where multiple users can work together on data generation channels and share insights securely via the cloud.
Oneview is an Israeli data science-based platform that uses satellite imagery and remote sensing technology for defense intelligence. With the use of cameras, mobile, drones, and satellites, Oneview’s algorithm help in object detection even in the conditions where images are blurred, low in resolution, or hard to spot.
From a person’s point of view, a vehicle is relatively bigger in size. However, from a satellite’s point of view, it is seen as an area of 500 square pixels which is difficult to identify. Also, the fact that information on objects includes views from different angles, sensor type, time of the day, etc.
Oneview overcomes such issues by using accurate and detailed annotation on the virtually created imagery that closely mimics the real-world environment.
Oneview has previously worked on a pilot project with Airbus to test the effectiveness of synthetic data for satellite imagery. The collaboration proved that synthetic data can improve rendering accuracy by up to 20% in contrast to real-world geospatial imagery.
Besides, Oneview also provides services in the energy, construction, urban planner, and finance sectors.
So far, it’s safe to assume that the 2020 decade can disrupt the synthetic data industry, just like what crypto did since the beginning of the last decade. It’s catching the same fire.
For customers, data won’t be scary anymore. Whereas with better implementation of privacy policies, companies would be reducing their customer dependencies.
For hackers, I think they might need to change their profession.
And for data, things are going to be interesting. With the advancements in machine learning models, insights could be as real as how humans think and perceive. Different permutations and combinations would make it possible to get alternate results – even at a scale that no one has ever experienced. This sounds as intense as multiverse theory, isn’t it?
Lastly, good news for CEOs of tech giants. For them, synthetic data means no more congressional hearings.