IBM Optim Test Data Management

Provision AI-ready data with business context preserved

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Overview

Trusted data for testing, AI and DevOps

Application testing and AI/ML training rely on high-quality, production-like data. IBM Optim automatically provisions right-sized data as complete business objects, preserving application context and referential integrity. Built-in privacy controls protect sensitive information and ensure regulatory compliance.

This enables the continuous delivery of trusted, reusable datasets across non-production environments, accelerating AI innovation and reducing operational risk.

Features

Secure, production-like test data

Discover and provision related data as complete business objects across databases. Preserve referential integrity and data dependencies to deliver trusted datasets for testing and model training.

Provision production-like datasets tailored to specific business scenarios and edge cases. Enhance test coverage and AI training outcomes without disrupting production workloads.

Apply pre-defined masking policies through a centralized masking engine to consistently de-identify sensitive information without manual redaction. Maintain usability and context while remaining compliant with data regulations.

Rapidly provision trusted datasets through self-service, configurable workflow definitions that can be reused across teams and environments. Support CI/CD integration for continuous data delivery at scale.

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Use cases

Provision and protect data at scale

AI/ML and application development

Provision scenario-specific, secure datasets for AI/ML training and application development while preserving context and referential integrity. Accelerate innovation with compliant, high-quality data.

Provision non-production environments

Rapidly provision reusable test data across non-production environments at scale. Support CI/CD pipelines and improve time to market through automated, on-demand data delivery.

Compliant and protected test data

Protect sensitive information through policy-driven data masking while maintaining data usability and context. Mitigate data exposure risks and ensure automated compliance with evolving data regulations.

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