The University of Manchester in the UK has deployed Datadobi’s file mapping solution to detect aged inactive files and migrate them into economical archive storage infrastructure.
The university boasts a distinguished heritage in the field of computing innovation. Back in 1947, researchers at the institution developed the world’s first fully electronic high-speed memory. Later, collaborating with technology firm Ferranti, its research team pioneered the virtual memory concept and implemented it on the Atlas computer in 1962. This groundbreaking technology enabled computers to rapidly switch resources between diverse programs and users, becoming an essential foundation for timesharing computing architecture.
Currently, the university runs a comprehensive suite of IT infrastructure to sustain teaching and research operations. Its Research Data System relies on a Dell PowerScale (formerly Isilon) clustered storage platform to retain research documents, with an incremental data influx of up to 15 terabytes daily. Last year marked the five-year hardware refresh cycle for its 10 petabyte PowerScale storage system, and the institution initially faced an expensive expansion plan targeting 20 petabytes of raw capacity.
After internal assessment, the university discovered that a large portion of its 3.5 billion stored files had become stagnant and rarely accessed. To tackle this issue, it sought an unstructured data management utility capable of scanning and analysing file metadata. The platform needed to support custom labelling for files and folders, including tags such as “cold”, “retain-until-2040”, “faculty”, “school”, and “classification”, and facilitate data migration to a new tape-based cold storage repository.
For the university’s internal IT team, manually auditing 3.5 billion files to filter out outdated and low-access datasets for tape archiving was practically unfeasible. A full manual inspection would consume several years and incur massive labour costs.
The university ultimately selected Datadobi’s StorageMAP to automate the entire data sorting and migration workflow. The tool efficiently scans file metadata at scale, completing analytical tasks in a vastly shorter timeframe compared to manual human operation.
Wayne Smith, Research Data Management Lead at the University of Manchester, commented: “The core hurdle was distinguishing archivable datasets from billions of miscellaneous files safely and accurately. Manual processing would require complex scripting against massive data pools, draining manpower and bringing potential human-induced operational risks. StorageMAP delivers transparent data visibility, allowing us to execute data governance decisions efficiently and reshape our research data management framework.”
StorageMAP identifies ageing, idle datasets eligible for archiving and removes them from high-performance primary storage. According to Datadobi, this optimisation will generate substantial cost savings for the university over the next five years. By transferring dormant data to low-cost archives, the institution effectively cuts down the demand for pricey primary storage expansion — marking a clear business win for Datadobi amid Dell’s lost expansion revenue.
Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
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Email: yangyd@qianxingdata.com
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