Diskover, a large-scale file and object data management vendor, boosts usable flash capacity by eliminating useless clutter data consuming storage space.
Its software leverages native and derived file/object metadata to let customers discover, monitor and govern entire data estates. It builds data pipelines to relocate, sort and filter data for applications, acting as a unified virtual abstraction layer across multi-vendor, siloed distributed file and object storage, delivering a single pane view of all data assets.
CEO Will Hall explained: “We sit as an abstraction layer over all cloud and on-prem storage. One client operates 13 separate storage vendors within a single business unit, and our logical data representation aligns purely with business priorities. AI workloads rely heavily on interconnected data ecosystems; siloed storage negates the full value of a unified federated view. Platforms like Snowflake only need a single connection to Diskover’s virtual abstraction layer to access all underlying data, without building dedicated connectors for every individual storage provider.”
The data discovery function often reveals eye-opening realities. Hall shared a case of a global pharmaceutical giant that estimated it held 200 million files worldwide. Diskover’s initial scan uncovered 8 billion files within just one cluster at a single datacenter, with four or five comparable clusters remaining unassessed—such massive data underestimation is common, and full scans deliver complete data visibility.
Diskover acquired CloudSoda in June 2025 to integrate data movement and orchestration technology, combining data discovery with data delivery capabilities. The platform now delivers full visibility into data sprawl, optimizes data placement to cut storage spending, maintains regulatory compliance, and filters and cleans datasets for AI pipelines.
Storage systems frequently accumulate redundant, obsolete and trivial (ROT) data: temporary application byproducts left undeleted, scratch files from rendering jobs and other digital clutter. Diskover estimates organizations without dedicated data management waste up to one-third of total storage capacity on ROT data.
Hall noted the platform runs daily scans covering 600 client projects, uncovering massive volumes of ephemeral workflow data such as rough drafts that can be safely purged once workflow logic is mapped. Diskover’s AutoClean tool identifies and removes ROT data automatically. The core workflow requires regular full data estate scans to build a searchable catalog, clear data clutter, and recurring rescans to sustain clean storage environments—Hall describes this as “getting your storage fit and staying fit.”
AutoClean tags ephemeral workload data for automated removal. It maps source workflows to distinguish valuable signal data from useless noise, tagging both categories. High-quality tagged data flows downstream to tools like Snowflake, while tagged clutter gets purged in bulk.
Olivier Rivard, Diskover VP of Product, stated the platform integrates layered business context beyond basic filesystem metadata. It connects to project records in Jira, log datasets in Splunk, and vertical industry tools including media management platform Autodesk Shotgrid. Media workflows also generate Legal, Support and Finance (LSF) tracking jobs; failed tasks create orphaned datasets that linger indefinitely without automated cleanup rules. The system embeds pre-built logic for such scenarios: partial Aspera transfer files untouched for over 15 days are flagged as failed transfers and auto-deleted.
LSF workflows also apply to EDA, with shared data generation patterns spanning media & entertainment, life sciences, healthcare, pharmaceuticals, semiconductor design, oil & gas and automotive manufacturing. While industry terminology differs, data creation is usually database-driven. Diskover’s extensible APIs link application context to file metadata via custom auto-tagging to correlate related records.
Diskover injects business context into captured file and object metadata, typically through application APIs or JSON files, enabling precise identification of application-generated ROT data. Its tagging system categorizes data by business value into four tiers: high-value active core assets, cold archived high-value assets, low-value transient scratch data, and regenerable cold ROT data marked for capacity reclamation.
Hall emphasized rising storage costs and constrained hardware supply make optimized data governance critical. “Getting fit and staying fit” relies on automated purging based on these four data classification tiers. Mapping vertical industry workflows and enabling rule-based automation forms Diskover’s core value proposition.
Regardless of scale, Diskover streamlines bloated data estates to maximize limited SSD and HDD storage utilization.
Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
WhatsApp / WeChat: +86 13426366826
Email: yangyd@qianxingdata.com
Website: www.qianxingdata.com/www.storagesserver.com
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