Healthcare’s Unstructured Data Problem: The Case for Modernization

Like most other sectors operating in the digital economy, healthcare has become hugely reliant on unstructured data. It’s widely acknowledged that this data type, which doesn’t follow a predefined format and resides outside traditional databases, now comprises 80-90% of all organizational data.

In healthcare settings, this can encompass a wide range of items, including medical imaging, scans, emails, claims documents, and device outputs, among many other possibilities. This information is essential for managing care and administration, but as its name suggests, its unstructured nature makes it harder to organize, search, and analyze using standard tools.

And the challenges don’t end there. Many healthcare organizations rely on fragmented technologies and ageing infrastructure, leading to a range of integration and performance issues. This environment introduces serious efficiency, security and governance risks, made worse by poor data visibility, which collectively contribute to an estimated $105 billion in healthcare fraud each year.

This perfect storm leaves the industry in an increasingly difficult position because, as unstructured data volumes grow (at 50% or more per annum), existing data management systems will fall even further behind, making it much harder to maintain control over critical information.

Big data brings big responsibilities

To unpack this further, healthcare organizations need to analyze and manage their various datasets to deliver high levels of patient care and operational efficiency. They also have a duty to protect that information against misuse, loss, or unauthorized access, and, at the same time, are among the most heavily regulated and scrutinized sectors.

Yet, from a data management standpoint, poor visibility into what data they have and where it resides is commonplace across the industry. Many organizations have little choice but to manage this tactically by bolting on additional storage and software tools in an attempt to fix problems, which almost inevitably leads to inefficiencies and excessive costs.

Elsewhere, data silos hinder operational workflows, delaying important processes and increasing administrative overheads. Even more crucially, unmanaged unstructured data can significantly increase the risk of compliance failures and security breaches. At the same time, sensitive information that lacks proper classification or governance can also fall short of regulatory requirements, with potentially serious consequences.

The case for data management modernization

Given this situation, modernizing data infrastructure is becoming essential to help healthcare organisations manage the growing volume and complexity of unstructured data. The starting point is visibility: without a clear understanding of where data resides, across both on-premises and cloud environments, effective control is not possible. This includes identifying data owners, access patterns, and determining whether the information holds clinical, financial, or regulatory value.

With this foundation in place, organisations are better equipped to implement governance policies that define how data is classified, retained, and protected. Automating these policies allows dormant or redundant data to be moved to more cost-efficient storage, while ensuring that high-value or sensitive content remains secure and accessible.

Vendor-neutral data management platforms now support this process by visualizing key metadata, such as file age, ownership, and usage frequency. This enables data-driven decisions on what to keep, archive, or delete, helping reduce risk, and improve storage efficiency. These systems also support interoperability across environments, which is critical when migrating large volumes of sensitive data, often involving complex file structures and strict compliance requirements.

Supporting AI adoption

Beyond operational benefits, this approach puts healthcare organizations in a stronger position to prepare their data estates for advanced technologies, such as AI and analytics. Indeed, as interest in AI grows across healthcare, where 85% of organisations are already exploring its use, well-managed data is fast becoming a prerequisite for success.

This is crucial because when data is fragmented or poorly governed, AI outputs become less reliable; an issue that raises significant concerns in an industry such as healthcare, where decisions can have profound consequences. In contrast, when unstructured data is properly classified, secured, and integrated, it can be used with greater confidence to support strategic goals. For example, predictive modelling depends on high-quality data to help identify anomalies in claims or assess financial risk. Whatever the use case, effective AI integration relies on consistency across datasets, something that can’t be achieved without robust controls.

The way forward is to build data environments that more effectively support governance, interoperability, and performance at scale. In doing so, healthcare organizations can create a win-win scenario whereby cost reductions and increased efficiency can be delivered alongside the industry’s ability to operate securely and the levels of service every stakeholder wants to see.

About Steve Leeper 

Steve Leeper oversees the market development for Datadobi and manages the Presales Sales Engineers team globally. A 30-year veteran of IT, Steve has held a variety of technical and sales roles at Andersen Consulting, Sun Microsystems, and EMC.