THE PROBLEM
Due to oversized and expanding unstructured data estates, many organizations grapple with identifying which data is business-critical, which poses risks, and which can be safely disposed of. Commonly termed as ROT data [Redundant, Obsolete, Trivial], this data often exists in duplicated form, is seldom accessed, or lacks business-critical relevance.
OUR SOLUTION
Our product, Zantaz Data Optimization, offers a comprehensive solution to tackle the challenge of managing ROT (Redundant, Obsolete, Trivial) data within organizations' data estates. Leveraging a robust index catalog and efficient workflow engines, Zantaz Data Optimization swiftly identifies and categorizes ROT data into three key areas:
Redundant: By analyzing unique hash identities, our system identifies duplicate data and enables organizations to designate a 'golden copy' to retain, streamlining data storage and organization.
Obsolete: Our solution allows organizations to define obsolescence based on customizable criteria such as last accessed, last modified, or creation timestamps, ensuring efficient identification and management of outdated data.
Trivial: Zantaz Data Optimization provides predefined selections of commonly known file extensions storing non-business-critical information, as well as the flexibility to define custom file extensions specific to the organization's needs, streamlining the identification of trivial data.
Once configured within the workflow engine, Zantaz Data Optimization automatically tags ROT data with relevant labels, such as 'Redundant,' 'Obsolete,' 'Trivial,' and 'Golden Copy.' These tagged files are then collated and routed through our AI Data Organizer, enabling organizations to take appropriate actions, including disposal, copying, or migration to cost-effective long-term storage.
By effectively managing ROT data, Zantaz Data Optimization optimizes storage usage and reduces costs associated with high-performance disk storage, allowing organizations to allocate resources more efficiently and prioritize storage for critical data without the need for additional procurement over extended periods.
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