Key Points
- The University of Manchester selected Datadobi’s StorageMAP platform to improve storage optimisation and manage unstructured research data more efficiently.
- The deployment is intended to deliver significant cost savings over the next five years by identifying and archiving ageing data.
- The university was facing rising data volumes, with research processes generating up to 15TB of data per day.
- Its primary NAS storage was heading towards a major refresh, from 10PB to at least 20PB, creating significant infrastructure cost pressure.
- The Research IT Data Management Team said manual scripting across billions of files would have been slow, labour-intensive and riskier than automated lifecycle management.
- Datadobi said the move shows how intelligent data management can reduce the need to buy more primary storage while aligning resources with research priorities.
What did the university announce?
The University of Manchester has chosen Datadobi’s StorageMAP to transform its storage optimisation strategy, according to Datadobi’s announcement on May 08, 2026, with additional reporting from Scott Thompson of EdTech Innovation Hub. The decision centres on improving how the institution manages huge volumes of unstructured research data while lowering storage costs over time. The first paragraph in a news report should lead with the most important facts, and this story clearly does so: who made the decision, what platform was selected, and why it matters.
As reported by Scott Thompson of EdTech Innovation Hub, the university said StorageMAP will help it “transform its storage optimisation strategy” by identifying ageing data that can be archived instead of kept on expensive primary storage. That approach is designed to reduce the need for continued expansion of the university’s primary NAS environment. Datadobi described the platform as a way to make data-driven decisions that improve storage economics in research-heavy organisations.
Why is storage pressure rising?
The university said its research processes generate up to 15TB of data per day, with total data volumes increasing year on year. According to the report, the institution was approaching a critical five-year refresh of its primary NAS storage infrastructure, which was set to move from 10PB to at least 20PB at significant cost. That scale of growth helps explain why the university is looking for ways to manage data more intelligently rather than relying only on hardware expansion.
Wayne Smith, Research Data Management Lead at The University of Manchester, said the main challenge was finding which datasets among billions of files could be safely moved to archive storage. He added that a manual approach would have required scripting through massive volumes of data, consuming significant staff time and introducing risk through human intervention. In his words, StorageMAP provides “visibility and confidence” to make those decisions efficiently.
How does StorageMAP help?
StorageMAP is being used to scan billions of files and petabytes of data so the university can identify ageing and unused datasets suitable for archiving. By removing inactive data from primary storage, administrators can spend less time on manual processes and apply lifecycle management decisions more effectively. The platform also gives visibility into storage utilisation patterns across the research environment, which supports longer-term planning.
Datadobi’s chief revenue officer, Michael Jack, said research-intensive universities face unique data challenges because volumes and infrastructure costs are growing at unprecedented rates. He argued that the University of Manchester is showing how intelligent data management can change the economics of storage. Rather than simply buying more expensive primary storage, Datadobi says the university is using StorageMAP to align resources more closely with research priorities.
What does this mean for universities?
This development reflects a broader pressure on universities and research institutions to manage fast-growing digital workloads without letting storage bills spiral. Research environments often produce vast numbers of files that remain valuable for compliance, reproducibility or future study, but not all data needs to stay on premium storage forever. Tools that can separate active from inactive data are becoming more important as institutions scale up their research operations.
For the University of Manchester, the move is framed as both a financial and operational decision. Financially, it may delay or reduce the need for major primary storage purchases. Operationally, it could free staff from time-consuming manual sorting and improve decision-making around data retention and archiving.
Background of this development
The background to this story is the rapid growth of unstructured data in higher education and research. As universities generate more data from experiments, simulations and digital workflows, their storage systems have to scale quickly and at considerable expense. Datadobi’s StorageMAP is positioned as a lifecycle management tool that helps institutions find older or unused data and move it to archive storage more intelligently.
The University of Manchester’s case is especially relevant because it combines rising daily data creation with a looming infrastructure refresh. That makes the institution a useful example of how research universities are rethinking storage strategy in response to cost, scale and staff capacity. The announcement also fits into a wider trend in enterprise IT, where organisations are trying to extract more value from existing storage before committing to large new purchases.
What is the likely impact?
For university IT teams, this kind of deployment could set a practical example of how to manage data growth without expanding primary storage at the same pace. Research staff may benefit indirectly through better system performance, clearer data governance and fewer manual bottlenecks in storage management. Over the longer term, the biggest gain may be financial, because better data classification can reduce unnecessary capital spending.
For the wider higher education sector, the development may encourage more institutions to adopt automated storage optimisation tools. That could lead to faster decisions on archiving, lower infrastructure costs and more sustainable data management practices. In a sector where budgets are under pressure, the appeal of controlling storage growth without disrupting research work is likely to remain strong.
