IBM InfoSphere® Optim™ solutions manage data from requirements to retirement. They boost performance, empower collaboration and improve governance across applications, databases and platforms. By managing data properly over its lifetime, organizations are better equipped to support business goals with less risk.
IBM InfoSphere Optim Test Data Management
Extract data across systems and databases into test environments for rapid software testing, while maintaining test integrity.
IBM InfoSphere Optim Virtual Data Pipeline
Get secure, virtual copies of production databases, while consuming no additional storage resources.
IBM InfoSphere Optim Data Privacy
Protect non-production confidential data, using a variety of masking techniques across databases, applications and systems.
IBM InfoSphere Optim Archive
Decommission and archive applications and their data to reduce storage costs and risks, while keeping the necessary data.
IBM InfoSphere Optim Test Data Fabrication
Accelerate software development lifecycles, using rule-based synthetic data creation to meet testing needs, while reducing risk.
IBM InfoSphere Optim Test Data Orchestrator
Use easy-to-define rules to calculate and extract the test data values needed to match the exact requirements of test cases.
IBM InfoSphere Optim Data Privacy for Unstructured Data
Protect unstructured data in testing, development and analytics environments across the enterprise.
Archive data from decommissioned applications and historical transaction records, while providing ongoing access to the data for query and reporting that is compliant with retention regulations.
Scale data across applications, databases, operating systems and hardware platforms to help secure your test environments, accelerate release cycles and reduce costs.
Obtain proven data lifecycle management capabilities, maximizing the business value of data warehouse and big data environments through managing data growth and lowering TCO.
Lack of data archiving can impair the performance of mission-critical enterprise systems. Solve data growth problems at the source, improve efficiency and minimize the risks associated with managing structured data throughout its lifetime.