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Synthetic Data
Generator
Find out how Target Reply overcomes data management challenges
THE CHALLENGE
Data quality and security: challenges and solutions
Synthetic data is artificially generated data that has the same characteristics and statistical properties as real data. They are indistinguishable from original data and can be generated in large quantities, offering a scalable and reliable solution to meet growing data needs.
Data management is an increasingly complex challenge that, in some cases, can be solved by increasing the available data, while in others, despite having a considerable amount of data, ensuring their quality requires significant efforts.
Data collection and data labeling can take significant time and resources, as it is essential to ensure regulatory compliance and data security.
In addition, the GDPR imposes strict restrictions on the use and processing of personal data, adding an additional level of complexity to their management.
Addressing these challenges requires effective strategies and a constant commitment to maintaining high quality and safety standards.
Tailor-made solution for our customers
From the analysis of the needs of our customers regarding the generation of synthetic data, Target Reply has developed a solution capable of accelerating the process of generating synthetic data, in a way useful to data scientists for the development of Machine Learning models. The use of synthetic data makes it possible to generate reliable and bias-free artificial datasets, greatly simplifying the collection and management of traditional data. This leads to a reduction in business operating costs and encourages data sharing in accordance with the GDPR.
Single training, unlimited data
With just one training, you can generate an unlimited amount of data at any time, allowing for high scalability.
Integrated quality report
The generated data is complemented by a report on their quality, which provides a detailed assessment of the accuracy, reliability and consistency of the data generated.
Integrable into on-premise and cloud environments
In this way, it is possible to ensure wide flexibility in implementation and adaptability to needs, ensuring a tailor-made solution and optimal data management.
Automated exploratory analysis
Through exploratory analysis, it allows an easy understanding of the dataset, being able to fully explore the characteristics and relationships present in the data.
Use Case
Discover some of the use cases we explored with our customers.