56% Infrastructure Optimization, 43% Decrease in Storage Costs and Risk Mitigation with AI/ML-Powered Data Analytics

For a Fortune 200 European Corporation and the World’s Second-Largest Sportswear Manufacturer

Business Need

  • Classify the 200 TB Isilon dataset to maximize data utilization and streamline data storage efficiency.
  • Streamline storage operations, reduce costs, and execute a seamless migration from Isilon to the advanced NetApp infrastructure.
  • Elevate data speed, accessibility, and overall workflow efficiency through a meticulous data migration to the high-performance storage infrastructure provided by NetApp.
  • Implement a robust strategy to identify and manage dark data, addressing security threats, ensuring compliance, safeguarding privacy, optimizing operational processes, reducing storage expenses, preserving reputation, and enhancing data quality—all while gaining valuable business insights.

Challenges Faced

  • Data Knowledge Deficiency: Inadequate understanding of Isilon data types contributes to security vulnerabilities, compliance risks, suboptimal storage utilization, and an elevated overall risk profile.
  • Dark Data Accumulation: The accumulation of inactive data spanning over two years burdens storage systems, amplifying security risks.
  • Escalating Storage Costs: Inefficiencies in data storage lead to rising expenses
  • Inefficient Storage Infrastructure: The high maintenance costs associated with Isilon strain the organization’s financial resources due to an inefficient storage infrastructure.

Solution Offered

In collaboration with a leading IT infrastructure services provider, Data Dynamics employed StorageX, which featured:

  • AI/ML-driven metadata analysis for the classification of a substantial 187 TB dataset and intelligent identification of cold and hot data, optimizing storage efficiently.

  • Utilized data assessment criteria, analyzing file size and activity, to identify dormant files over a two-year period, orphan files older than two years, media files untouched in two years, and .PST files untouched for a year.

  • Provided storage optimization recommendations post-metadata analysis, including archiving cold data to Azure Cool Blob or purging after thorough verification.

Business Impact

  • Successful data classification resulted in the identification of 98.59 TB of data untouched for two years, leading to a recommendation for archiving and substantial storage savings.
  • The discovery of 3.17 TB of inactive media files spanning two years prompted a suggestion for tiering to object storage. Additionally, 10.89 TB of orphaned data untouched for two years was identified, with a recommendation for archival or purging after verification. Moreover, 130 GB of PST files, untouched for a year, were suggested for archival or deletion.
  • Approximately 60.31% of the dataset was eligible for archiving, effectively optimizing storage and significantly reducing storage costs.
  • The efficient archiving of this dormant data played a crucial role in mitigating dark data risks.
  • The implementation of effective data classification and optimization recommendations holds the potential to reduce storage costs by 43%.
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