HP Storage Optimizer, HP ControlPoint Offer Software Solutions For Unstructured Data Storage, Governance
HP announced two new software solutions, HP Storage Optimizer and HP ControlPoint, to help businesses address Big Data-related governance and data storage issues.
HP Storage Optimizer is designed to help customers manage the storage of large volumes of unstructured data, while HP ControlPoint is a file analysis tool that allows customers to access, understand and classify data.
HP Storage Optimizer uses a combination of file analytics and data storage tiering to allow customers to intelligently store and back up unstructured data. HP Storage analyzer analyzes files based on metadata to help customers decide which data to offload from tier 1 infrastructure to tier 2 storage in order to manage costs. According to HP, prioritized data backup can help reduce backup times by as much as 50 percent.
HP Storage Optimizer analyzes unstructured data across different storage platforms within an organization, including support for Hadoop, SharePoint, Exchange and HP StoreAll. Redundant data can be deleted, data deduplication across different repositories and tiers can be implemented, and complex policies can be applied to help optimize storage allocation.
The HP ControlPoint file analysis software assists organizations in information governance through the application of analytics. HP ControlPoint can analyze and identify unmanaged dark data sitting in rarely accessed repositories. Data bound for records management systems can be managed for long-term governance and compliance. Enterprise data can be intelligently migrated to cloud-based storage to lower storage costs and improve user access and security.
ControlPoint provides administrators with a graphical visualization of information clusters across an enterprise. Two-dimensional heat maps can be created that show information grouped by concepts. Three-dimensional spectrographs can help visualize how data changes over time. Advanced reporting tools can help admins understand how differing data types are distributed across an enterprise and enable the implementation of policy based clean-up processes.