This blog is part of an industry series on unstructured data management. Read the previous post on life sciences here.
Personalized medicine, patient-centric care, telemedicine, digital imaging, digital pathology and AI-driven disease management are driving massive unstructured data growth in the healthcare industry.
Healthcare is one of the largest industry creators of data.
- Roughly 30% of the world’s data volume is generated by the healthcare industry.
- On a per-hospital basis, a single health system can generate around 50 petabytes of patient and operational data per day.
- The big data healthcare market, valued at approximately $93–$111 billion in 2025 depending on scope, is expected to more than quadruple by 2035, driven by the proliferation of AI-powered diagnostics, wearables, and electronic health records, according to Roots Analysis.
Consider common medical files such as lab slides, X-rays, MRIs and CT scans. These everyday files take up petabytes of high-performing storage. Regulations often require their retention for several years. Clinicians may need to review some images again months later, so IT can’t hide them in a dusty basement tape archive.
Dictation and nursing notes also contain patient data that’s valuable for data mining projects which organizations need to improve patient outcomes and develop personalized medicine programs.
Common data management challenges in healthcare
Managing data growth is a large initiative in healthcare.
The global healthcare data storage market size is expected to grow from $8.20 billion in 2025 to $21.70 billion by 2034 at an 11.32% CAGR, according to Fortune Business Insights. Beyond data volumes, there are many different systems and clinical file types as technologies and protocols evolve.
This complexity makes it laborious to search for specific files, meet compliance challenges and manage storage costs. Most healthcare providers are under tight budgetary constraints following the pandemic and ongoing industry pressures to lower the cost of care.
The financial stakes of poor data management have grown dramatically. Healthcare data breaches cost an average of $7.42 million per incident in 2025, making healthcare the most expensive industry for breaches for 14 consecutive years, according to IBM. More than 250 AI-related bills have been introduced across 46 states, adding a patchwork of compliance obligations on top of federal requirements like HIPAA.
The AI Opportunity
The healthcare industry is on the brink of revolutionizing patient care. Two decades ago, electronic health records were still rare. Today, digitization has accelerated quickly with mobile apps, wearables, telemedicine and the integration of AI technologies into daily practice:
- AI adoption in healthcare has reached approximately 85% industry-wide in 2025 but adoption is outpacing governance.
- Most (63%) of organizations have no AI governance policies in place, creating significant compliance exposure.
- Generative AI solutions are reducing the paperwork burden of clinicians and even improving communications between physicians and their patients.
- AI’s ability to analyze medical imaging continues to accelerate: machine learning models are now routinely flagging anomalies in radiology, pathology, and cardiology scans, helping prioritize urgent cases and reduce diagnostic errors.
- AI and big data analytics are helping medical leaders create holistic care plans by analyzing demographic and social data for patients with a particular condition and delivering better preventive care by analyzing chronic disease data.
The Healthcare Metadata Enrichment Challenge
Healthcare organizations hold billions of DICOM imaging files across PACS, VNA, and NAS systems that are rich with clinical data but effectively locked away from AI. The metadata embedded in each file is invisible to the storage layer, creating a fundamental mismatch between how the data is stored and what AI pipelines actually need.
KAPPA (Komprise AI Preparation and Process Automation) addresses this directly as a serverless compute framework that runs custom functions directly on files in place. It extracts DICOM header metadata at scale without requiring proprietary connectors to imaging systems, without modifying clinical applications, and without creating costly duplicate copies. Read the blog.
AI’s positive impact on healthcare and the dangers of data bias and incomplete data.
How unstructured data management helps:
Unstructured data management solutions help healthcare organizations lower the overall cost of data storage (including backups and disaster recovery) by 70% or more through intelligent analysis and placement of files. This frees up money for AI and analytics programs required to maintain profits, high standards of care, grants and funding and patient satisfaction.
The right unstructured data management solution can also bring deep analysis to data, allowing managers and researchers to understand data usage, easily locate and use or move data as needed and avoid compliance issues. Automated workflow capabilities create more efficient ways to find data, copy or move it to an AI tool for analysis, tag the results with metadata and then archive or delete the original data once the AI has finished.
Case in point: NewYork-Presbyterian
Komprise customer NewYork-Presbyterian needed help with a high-priority pathology AI project that would serve as a benchmark for other clinical AI programs, An unwieldy data environment was a barrier, according to the IT project leader:
“We have large datasets for unstructured imaging, and needed a way to ingest it, store it, query it and move it, while being cost effective and meeting the strict performance requirements of our stakeholders.”
The IT infrastructure team reduced cloud costs by 96% using a Komprise automated AI workflow that curates a small subset of files and then deletes cloud copies after 30 days. This approach pared down AWS storage from 1PB to a rolling 33TB while ensuring data curation for AI.
FAQs: Healthcare Unstructured Data Management & AI
Why is unstructured data management critical for healthcare AI?
Most healthcare data is unstructured: DICOM imaging, digital pathology, genomics, clinical notes, etc., and it is the primary input for AI.
Without proper management, this data is difficult to find, trust, and use. Unstructured data management adds visibility, metadata, and control, enabling organizations to deliver the right datasets to AI for better accuracy and outcomes.
What are the biggest challenges using healthcare data for AI?
Healthcare data is fragmented across PACS, EHRs, VNAs, and cloud storage, often without consistent metadata.
This makes it hard to locate relevant datasets like DICOM studies or digital pathology images, increases AI costs, and raises compliance risks around sensitive data.
A metadata-driven approach helps teams find, filter, and govern data before AI ingestion.
How does Komprise make healthcare data AI-ready?
Komprise analyzes unstructured data across DICOM, pathology, and genomics datasets, then automates how it is managed and delivered.
With global visibility (see Global Metadabase), intelligent tiering, and automated Smart Data Workflows, organizations can:
- reduce storage costs by up to 70%
- enrich metadata for search and AI
- deliver curated datasets to AI pipelines
The result is faster AI data preparation and more reliable outcomes.
