YData for Telecommunications

Data-Centric AI for Telecom

From anomaly detection to data monetization, YData Fabric provides Telcos innovation leaders with a complete workbench for data-centric AI applications development accelerated by synthetic data and data profiling.

Use cases for Telecommunications

Improved anomaly detection

Save money by detecting more anomalies

Network defects can be detected using Anomaly Detection algorithms. But the algorithms are severely hampered due to the lack of good data. YData can automatically classify the different types of anomalies and synthesize data accordingly to train better predictive models. YData’s Pipelines allow the user to compare, contrast and fine-tune the ML model for the detection job.

YData telecommunications use case

Data monetization

Unlock a new revenue stream by monetizing data assets

The value of information assets has never been greater. Synthetic data has the same statistical and business value as real data, but it is not traceable back to real individuals. This makes it suitable to be monetized without concerns around privacy.

YData telecommunications use case
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AIOps - AI Operations

Optimize operations and improve RoI for AI

With AIOps, teams are able to tame the immense complexity and quantity of data collected from modern IT environments, prevent outages, maintain uptime and attain continuous service assurance. YData enables the creation of training datasets and models, and seamlessly integrates production ML models and pipelines into existing IT operations.

YData telecommunications use case

Join AI innovation with the right data

Become the best in class by delivering faster and better AI solutions with improved data.

How to pick the best fit data catalog for your data stack?

Dive into data management with our latest whitepaper, which presents an in-depth Gap analysis among YData Fabric, Alation, and Informatica—three solutions in the realm of data catalogs. These platforms are chaging how organizations govern,...

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How to evaluate the re-identification risk in Synthetic Data?

While allowing for meaningful data behavior, it is crucial that synthetic data safeguards individual privacy. Therefore, ensuring the efficacy of synthetic data applications also requires a strong assessment of re-identification risks.

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How is diversity preserved while ensuring privacy in synthetic data?

One of the most valuable and unique characteristics of synthetic data is that it keeps the properties and behavior of original data without a one-to-one link with the real events, thus fostering data privacy and enabling secure data...

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