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Transform every dataset into a secure, shared workspace for faster collaboration without privacy risk.

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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.

Built for data creators, buyers, and enterprise partners.

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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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CUBIG's non-access architecture and differential privacy framework ensure 100% protection — no exposure, no compromise.

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Home / Glossary / Data cleansing

What is Data cleansing?

Data cleansing, also known as data scrubbing, is the process of identifying and correcting errors, inconsistencies, and duplicate records within a dataset. It enhances data quality, improves accuracy in analytics, and ensures reliable outcomes in AI models and business intelligence systems.

Related Glossaries

  • Data Science and Predictive Analytics Data Science and Predictive Analytics is a field that applies data mining, machine learning, and statistical techniques to forecast future trends based on historical data. It is used in business intelligence, healthcare, and finance to optimize decision-making, risk assessment, and…
  • Synthetic Data Synthetic data refers to artificially generated data that mimics real-world data while preserving privacy and security. It is used in AI training, testing environments, and data augmentation to enhance model performance.
  • Data Governance Data governance refers to the policies, processes, and frameworks that ensure data quality, security, and compliance within an organization. It defines roles, responsibilities, and standards for data management, enabling regulatory compliance and risk mitigation in data-driven businesses.
  • Prompt Engineering Prompt engineering refers to the practice of designing effective inputs for AI models, particularly large language models, to achieve desired responses. It optimizes AI interactions in applications such as chatbots, content generation, and automated reasoning.

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