The following is a guest article by Steve Leeper, VP of Product Marketing at Datadobi
Healthcare organizations generate and store vast amounts of data, much of it unstructured. This includes medical imaging, scanned records, emails, claims documents, and device outputs (among various other data types). While this information holds potential for improving patient outcomes, operational efficiency, and fraud detection, when mismanaged, it also introduces serious risks.
The challenges are significant, with fragmented systems, legacy infrastructure, and inconsistent governance making it difficult for many providers and insurers to maintain full and proper oversight of their data estates. The knock-on effect of these issues is enormous, and according to industry reports, healthcare fraud alone accounts for an estimated $105 billion in annual losses, where poor data visibility and control are likely to have played a role.
The fundamental issue, therefore, is no longer about whether data has value, but about how effectively it is managed and protected because, without a strategic approach, organizations risk undermining both financial performance and care delivery.
The Unstructured Data Challenge
Like many other sectors, healthcare’s unstructured data is often spread across multiple systems and formats, making it difficult to manage, analyze, or protect. The consequences of this fragmentation can be significant, with poor visibility into what data exists and where it resides leading to storage inefficiencies and excessive costs. Data silos hinder operational workflows, delaying important processes and increasing administrative overheads. Even more crucially, unmanaged unstructured data elevates the risk of compliance failures and security breaches, while sensitive information that lacks proper classification or governance can also fall out of regulatory scope, with obvious consequences.
Despite these risks, many healthcare providers and insurers continue to rely on a storage-centric approach to managing unstructured data, which does not adequately address the complexity of their environments. Indeed, without modern tools to identify, classify, and monitor data across hybrid infrastructure, the gap between data growth and data control continues to widen.
The Case for Modernization
So, what needs to change? In many cases, modernizing data infrastructure is essential for addressing the volume and complexity of unstructured data in healthcare. This process begins with visibility; organizations must be able to locate, categorize, and assess all data assets across on-premises and cloud environments. This includes understanding who owns the data, how frequently it is accessed, and whether it holds clinical, financial, or compliance-related value.
Organizations are then much better placed to apply effective governance policies that define how data is classified, archived, and secured based on its type, age, and regulatory requirements. Automated rules can help relocate dormant or redundant data to lower-cost storage tiers, ensuring that sensitive or high-value information remains readily accessible and protected.
Modern vendor-neutral data management platforms now enable stakeholders to visualize key metadata, such as file age, ownership, and access frequency. This visibility supports informed, policy-based decisions about what data to retain, archive, or delete, allowing healthcare organizations to reduce risk and optimize storage environments. These platforms also integrate with diverse systems to enable seamless data mobility, which is particularly important for large-scale data migrations, often involving millions or even billions of sensitive files that require absolute precision, especially when dealing with deeply nested structures and strict regulatory requirements.
As well as allowing healthcare organisations to reduce risk and streamline operations, this heterogeneous approach also puts them in a much stronger position to prepare their data environments for AI, analytics, and future technology adoption.
Building an Environment Fit for AI
Given the healthcare sector’s enthusiasm for AI – 85% of organizations across the sector are already pursuing AI initiatives, according to data from McKinsey – structured, well-managed data is a prerequisite for successful deployment.
When data is fragmented or poorly governed, it limits the accuracy and reliability of these tools. Conversely, when unstructured data is brought under control so it is classified, secured, and organized across systems, it becomes a valuable asset that can enable organizations to properly deliver on their AI objectives.
Predictive modelling depends on structured, well-managed data to support fraud detection and financial risk mitigation. In healthcare, the ability to detect anomalies in claims or irregular patterns in large datasets relies on data quality and consistency. Without this foundation in place, organizations risk deploying AI tools that produce unreliable or biased outcomes.
However, by creating a data management plan that supports governance, interoperability, and performance at scale, the healthcare industry can reduce costs, increase efficiency, and enhance the various experiences of patients, providers, and payers alike. From an investment perspective, this goes beyond a routine technology upgrade; it represents a strategic commitment to strengthening healthcare’s ability to adapt, operate securely, and deliver better outcomes.
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.
