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Use LLMs on enterprise data — safely.

Mask sensitive fields before they reach the model. Compliance logging, policy control, on-prem deployment.

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Discover and trade verified synthetic datasets — all in one marketplace.

Buy, sell, and sponsor synthetic datasets with full transparency and compliance.

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Your AI is only as good as the data it trains on.

DTS solves unusable data — whether it's restricted, imbalanced, or missing coverage your model needs.

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Measure what matters.

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Redefining how organisations trust, protect, and use data.

CUBIG builds AI-driven systems that redefine data security — from generation and transformation to validation and integration.

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Home / Glossary / Social data analysis

What is Social data analysis?

Social data analysis involves extracting insights from social media platforms, online communities, and behavioral datasets. It helps businesses, governments, and researchers understand trends, consumer sentiment, and public opinion.

Related Glossaries

  • Upsampling Upsampling refers to increasing the resolution or quality of data, particularly in image processing and machine learning. It enhances model accuracy in tasks like super-resolution and data augmentation.
  • Context-aware De-identification Context-aware De-identification refers to the removal or replacement of identifying elements based on their meaning and role within the data or prompt. Rather than simply deleting information, it aims to reduce exposure while preserving usability.
  • Statistical Fidelity (distribution preservation) Statistical Fidelity, or distribution preservation, refers to how well transformed or synthetic data retains the statistical characteristics of the original data. It measures whether important distributions, relationships, and patterns remain sufficiently preserved for analysis and execution.
  • Safe LLM Execution Layer Safe LLM Execution Layer refers to the operational layer that inspects, transforms, and controls prompt payloads before they reach an external language model. It helps organizations use LLMs more reliably in real business environments.

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