Health system CIOs are being pulled in three directions at once: protecting the organization from growing cyber threats, rapidly deploying AI, and delivering measurable results with constrained budgets and leaner teams. Here’s what doesn’t make that list but probably should: there are data scattered across too many systems, moving too slowly, and potentially sitting exposed. The mission of healthcare hasn’t changed and the data infrastructure is progressing slowly.
Bad data infrastructure is why security is hard to guarantee, why AI projects stall out, and why “doing more with less” keeps turning into “doing the same with less.” There are four areas where I see this challenge appear over and over. We have an opportunity to make sure data infrastructure evolves, which is why I’ll be exploring Qumulo.
What is Qumulo?
The Qumulo data infrastructure is a universal adapter for enterprise’s data, bringing all files into a single, flexible system so enterprises can see and control data anywhere, from on-premises to any cloud. Qumulo’s data storage, management, and acceleration platform is built to solve a problem most CIOs already have, and few have fully fixed: unstructured data imaging files, video, research datasets, and sensor output piling up faster than the infrastructure built to hold it and prices for new infrastructure have increased up to 4.6x in the last 12-months. Its core function is to let an organization or research team keep one copy of its data while making it usable across on-prem hardware and major cloud providers, rather than forcing IT teams to constantly copy, migrate, and reconcile the same files across systems. For a CIO, that matters less as a feature and more as a budget, workforce collaboration, and risk decision — every duplicate copy of data is a duplicate cost, a duplicate compliance boundary, and a duplicate place ransomware can land.
What makes it relevant to the current CIO agenda specifically is the overlap between security and AI readiness. With Qumulo NeuralProtect, the platform detects threats at the point a file is written, offers predictive caching for performance-sensitive workloads, and includes AI-searchable metadata across large file estates. This speaks directly to the two things most executive data strategies are being judged on right now: can you stop an attack before it spreads, and can you actually feed your data into the AI tools you’ve promised the board? Qumulo is one vendor among several addressing that gap. Still, the underlying shift treats data infrastructure as a security and AI enabler rather than a storage line item, something CIOs should be paying attention to, regardless of which platform they end up choosing.
The imaging bottleneck nobody budgeted for
Radiology alone can throw off terabytes of data a day through multiple modalities— 3D mammography, digital pathology, AI-assisted diagnostics all produce bigger files than the last thing you bought storage for. Most PACS and vendor-neutral archive systems were sized and priced on a per-study basis, and providers would need to keep the same vendor for 10+ years before that model saves money compared to the provider hosting their own storage. When a radiologist is sitting there waiting for an image to load, that’s not a minor annoyance – it costs 100s of hours per year, per radiologist. I’ve watched delays run anywhere from 45 seconds to several minutes on large studies, and if you’re the one reading the scan, it feels longer than the clock says. In some cases, that wait matters to the outcome. PACS providers are charging more per study for less performance and more reader frustration.
For context, Qumulo’s data infrastructure platform reports 82% of reads delivered with sub-millisecond latency in production PACS and VNA environments. Worth noting: the bottleneck usually isn’t the network. It’s the object storage latency underneath the system, which requires between 5-10 milliseconds every time. The Qumulo solution solves for large PACS data volume with their cloud-based archiving without manual data movement with the cloud data fabric.
Fragmented data, fragmented care
Most health systems buy commercial software rather than designing their data environment. When we inherit that environment, we create silos: EHR storage lives in one place, imaging in another, research data in a third, and half of it is duplicated across on-prem arrays and a handful of cloud accounts nobody fully maps anymore.
Every one of those silos is a separate point of failure, a separate compliance boundary, and one more reason a specialist can’t pull up the full patient picture when they need it. Getting that sprawl under one logical roof — reachable from on-prem and cloud without shuffling copies of files around — is more of a governance fix than a tech upgrade.
Qumulo’s Cloud Data Fabric is built around that idea: keep one copy of the data and make it available wherever it’s needed, instead of replicating it everywhere.
Ransomware that targets the data, not just the network
Healthcare gets hit by ransomware more than almost any other sector, for an obvious reason: hospitals can’t tolerate downtime, so they’re more likely to pay. The usual defenses — backups, endpoint tools, perimeter security — only work after the fact. They catch the attack once files are already encrypted.
What’s actually new is software that inspects data as it’s written, before encryption happens. Qumulo’s NeuralProtect, which launched in May, uses deterministic, statistical, and behavioral AI models to flag known and zero-day threats as data hits disk, and the company puts its false-positive rate below 0.01% in production. One strategy for a healthcare technology leader is to catch it at the point of write, not after recovery.
AI ambition outrunning data readiness.
Everyone wants AI-assisted diagnostics, predictive analytics, and faster research. Almost nobody actually has the data architecture to support it. If your engineering team has to pull data out of five siloed systems to feed one model, they’ll spend more time copying files than building anything useful. Tools that can query data where it already sits — Databricks is a common example — cut that cycle down significantly, which Qumulo has laid out in its work on lakehouse architectures built directly on top of its platform.
This joint integration lets an enterprise lakehouse extend across an organization’s entire data estate rather than staying boxed inside a single cloud region. Databricks can now read, write, and govern data through Qumulo storage wherever that data lives: on premises, at the edge, or across any major cloud. For a CIO, the practical significance is that raw and historical data, including medical imagery, sensor logs, and application records, no longer has to be migrated or consolidated into one bucket before analytics or AI tools can touch it. Databricks can query it in place while governance, permissions, and audit trails stay consistent through Unity Catalog. In early validation testing, that architecture cut API related storage costs by 60% or more and compressed time to first results by 40% or more compared with workflows that stage data through cloud object storage first. That’s the kind of number that matters less as a technical detail and more as a signal that data gravity, not compute, has become the real constraint on enterprise AI timelines.
What this looks like in practice
Dayton Children’s Hospital is an example of this playing out. The pediatric network needed faster image retrieval and a way to get to the cloud without spending its budget on new hardware every year. By incorporating Azure Native Qumulo into their data estate strategy, DCH slowed the annual hardware refresh and allowed it to focus on assisted diagnosis research it couldn’t previously afford while still getting clinicians their images the moment they needed them.
Life sciences show a similar data problem at an even more extreme scale. A single Cryo-EM project, used to visualize proteins and molecular structures at the atomic level for drug discovery, can generate anywhere from 10 to more than 200 terabytes of high-resolution microscopy data. That data is captured in the wet lab, but the compute needed to process it – motion correction, particle picking, 3D refinement – lives in a data center or the cloud. The traditional fix has been to copy the data from lab to compute environment, then copy it again for validation and collaboration, provisioning duplicate storage at every site along the way. For a pharmaceutical company running concurrent programs across multiple locations, that can mean tripling storage costs and waiting a full day or more before GPU processing can even begin.
Qumulo doesn’t make the copying faster. It eliminates the copying altogether. A dataset written by a microscope in one lab is temporarily cached locally (spoke) then becomes immediately visible to GPU clusters in the cloud based on persistent storage (the hub) and extended to research teams somewhere else (multi-spoke), at the same time, without anyone running a transfer job. In deployments where that’s in place, teams have gone from waiting days for usable results to waiting minutes.
What’s actually slowing drug discovery down in a lot of organizations isn’t the microscope, and it isn’t the compute. It’s how long it takes data to move from one place to another. Fix that, and you’ve found a real lever for shortening R&D timelines, not just a cleaner architecture diagram.
The old approach of buying more disk, relying on backups, and hiring more people to babysit the sprawl doesn’t scale against how much clinical data exists now, how fast it moves, or how much it’s worth. Unifying data across environments, catching threats at the point of write, and making that same data usable can now live under one solution. Health systems must treat their data architecture as core clinical infrastructure.
Thank you to Qumulo for sponsoring this post! Explore their life sciences solution: https://fandf.co/4eSMWbk