YData for Data Scientists
Faster access to sensitive data
Data Synthesization allows data scientists to have faster access to data that used to require a full privacy compliance process only for accessing it.
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The process of building datasets is now much faster and cheaper with data quality profiling, labelling and synthetic data generation
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YData helps Data Scientists that struggle with the access to sensitive data and poor data quality while having to build and deploy scalable AI solutions.
Our platform solves the data pains with synthetic data and tools that improve data quality in an automated way.
Unlock your most valuable and sensitive data
Connect to any data source and unlock the full potential of data, through the generation of new data with privacy by design.
Improved data for successful AI
Automatically preprocess your data. Get your labels for supervised learning from day one. Augment and balance datasets in a single step. Generate new data.
Faster development of AI solutions
Drive collaboration during development of AI solutions on a single platform where improved data is queen.
The platform to develop scalable AI solutions centered on what matters the most, data, so Data Scientists can have more time to do what they love the most, the models.
GDPR
CCPA
HIPAA
PIPEDA
YData helps adopters of AI to improve and generate high-quality data, so they can become tomorrow's industry leaders.
YData for Data Scientists
Data Synthesization allows data scientists to have faster access to data that used to require a full privacy compliance process only for accessing it.
Try it outFaster access to sensitive data
Combine scalable connectors with synthesizers to have fast and easy access to datasets spread across the organization.
Experiment and develop with no learning curve
Ready to use platform with all the frameworks and tools for Data Science with no infrastructure work required to develop AI solutions.
Deliver a ML flow with zero effort
Easy and fast development and deployment of scalable data pipelines and workflow management. Schedule and monitor your runs in every environment.
YData for Business Managers
Optimize the allocation of your resources and teams. Your data scientists time will be invested in the faster and better development of models that are crucial for the business.
Request a demoImprove your return on investment
Optimize the allocation of your resources and teams. Your data scientists time will be invested in the faster and better development of models that are crucial for the business.
Unlock new revenue streams
Put your sensitive data to use, while being compliant with privacy regulations. Fast and easy access to new data sources is a key driver for innovation. All of this in a collaborative platform.
Reduce time-to-market and risk
Less bottlenecks and miscommunications between different teams. Ensure faster and easy access to high-quality data, the key for successful AI solutions.
Bernardo Almeida, Innovation Project Manager at EDP
“YData helps us reduce development time and delivery of our data solutions, even with a small team. Using their platform, preparing training datasets is easy, straightforward and with a great user experience!”
João Reis, VP of Data at Lovys
“The problem YData is solving is foundational and core to machine learning. It is known that the quality of data is the most important asset for an AI solution and ensuring it is something really hard and expensive.”
Paul Horn, ex-SVP & Director of Research at IBM, Distinguished Scientist at NYU
“YData brings a data-centric approach to the innovative field of MLOps.”
Demetrios Brinkman, Founder at MLOps.community
“YData allowed us to create personalized products leveraging machine learning with customers’ data, while complying with their privacy.”
Filipe Ribeiro, CEO at Ciclo Mobility
“Without YData’s Platform we couldn’t create an end-to-end machine learning product with our team size. By using their platform, we could focus on building the models while they took care of everything else.”
João Fernandes, CEO at MySmartWallet
YData has an automated quality and privacy control process for every dataset generated with the goal to control the quality, utility, and privacy of the newly generated data.
For the quality, we use divergence metrics, correlation measures, and non-parametric tests, for the utility we apply the TSTR (Train Synthetic Test Real) methodology. In what concerns measuring the privacy leakage, we perform various tests, such as inference attacks.
YData has a strong foundation in the literature that supports the solution in what regards privacy safety. There is no privacy certification nor known process to date that certificates a solution that's compliant with privacy regulation and, comparing to traditional anonymization tools, which also do not have any form of certification, synthetic data copes with the GDPR and other privacy regulations in the sense that this data is generated having random noise as input, being impossible to trace a synthetic record back to a record in the original data - the same way as GDPR defines to be privacy by design.
YData's platform is deployed on your infrastructure (either cloud or on-premises), ensuring that there's never a data transfer between your company and YData.
Yes. It is not proprietary and does not contain PII (Personally identifiable information), plus YData generates an automated and detailed report regarding the quality of the generated data as well as the privacy level of the newly generated dataset.
You can't. If you want to perform operational activities, business as usual, and single record operations, you'll need the real data. In general, to be able to trace back to an original record goes against the concept of privacy by design, foundational to synthetic data.
There are some trending PET besides Synthetic Data, such as Differential Privacy, Federated Learning and Homomorphic Encryption, and each of them were created for different purposes. Differential privacy will be the best option for private analytics over a big dataset. Why? It works really well for big data and as low computational expenditure.
On the other hand, although synthetic data needs high graphic computational power, it is a method that provides the same granular format of data to data scientists, and solve problems like data augmentation and balancing, so common in data science projects. Moreover, synthetic data can be combined with differential privacy.
Data synthesization is the process of generating new data, not transforming the existing one. However, before YData generates new data, there's a preprocess of the original data in order to create new data with higher quality. However, the newly generated data is not a transformation of the real dataset, nor its records are traceable.
This is a very controversial question because bias can happen at several different levels, meaning it can happen at the level of the data collection, the data processing, or during the process of building a model. For the first case, the data itself is collected in a biased manner, for example, a class of certain types of events are not collected on purpose, in this case it's very hard to solve the problem unless the collection process is changed.
The other two are the ones that can be fixed or influence the analysis. If the original data already contains any type of bias, that means that the synthetic data will contain that same bias, but not create it or make it worse. Nevertheless, it is possible to fix bias through the process of synthesizing data, when domain knowledge is available.