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Find out how Target Reply overcomes data management challenges
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.
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.
With just one training, you can generate an unlimited amount of data at any time, allowing for high scalability.
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.
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.
Through exploratory analysis, it allows an easy understanding of the dataset, being able to fully explore the characteristics and relationships present in the data.
Discover some of the use cases we explored with our customers.
The sharing of data and the achievement of an economic benefit take place without compromising the privacy of the original data.
A greater amount of data available makes it possible to train more performing Machine Learning models.
Training a synthetic model to replicate sensitive data makes it possible to use more realistic data for product development.