Massive data sets need effective data management to extract the business value, reduce storage costs, and accelerate tiering between storage. Timely extraction of actionable information mean faster and more effective operations. But today’s research and high performance computing (HPC) environments are faced with unique problems: manual processes, lack of data classification, and the complexity of managing research data across diverse storage environments (HPC clusters & instruments, file, object, tape, and cloud storage). These problems are complicated by the stringent heritage and curation requirements of grant-funded projects, publishing, and external data provenance and metadata standards.


  • Data from instruments or parallel file systems difficult to access, protect, and share
  • Limited data insights: critical research data may go undiscovered and underutilized
  • Improper metadata classification orphans data and prevents accurate data provenance for study results
  • Applying custom taxonomies is labor intensive and often incomplete
    Correlation & collaboration of data is difficult or non-existent


StrongLink Autonomous Engines™ with Metadata Services, Data Insights and analytics automates metadata management. Metadata is harvested, groomed, aggregated, and analyzed in real-time to facilitate workflows & collaboration, and data classification. Data insights are derived from metadata to power workflows and drive dynamic data movement across all storage tiers.

  • Global namespace connects across files systems, platforms for easy access, collaboration and tiering/archiving
  • Protect and migrate files from instruments and parallel file systems to other storage type
  • Metadata can be automatically extracted, classified, normalized and aggregated from multiple sources to reduce manual processes
  • Automatically apply metadata through auto-tagging of files based on simple customizable taxonomies
  • Ensure data provenance and metadata portability to conform to external research standards
  • Streamline collaboration between groups and teams

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