This blog is part of an industry series on unstructured data management. Read the first post here.
The life sciences industry has undergone significant transformation in recent years. The pace of change is accelerating due to economic pressures and a call to leverage AI to address data-driven pain points.
According to Precedence Research, the global life science market will grow from USD 100.88 billion in 2025 to nearly USD 278.40 billion by 2034, with an expected CAGR of 11.94% from 2025 to 2034.
The financial climate for life sciences has become considerably more complex as a high proportion of patents are expiring, threatening revenue loss, while R&D productivity has suffered in recent times, according to research by Deloitte.
Like many sectors, life sciences organizations are prioritizing cost-cutting measures such as restructuring and offshoring. AI is increasingly an area of investment, given its high potential for both productivity and cost-cutting opportunities.
Common Data and IT Challenges
Unstructured data, including medical imaging, clinical notes, genome sequencing and other instrument data, research documents and audio recordings, is one of the fastest-growing data types in life sciences. It requires advanced tools for processing and analysis. With massive volumes generated daily from drug research, patient trials, and laboratory results, maintaining data integrity is a top priority, particularly given stringent regulatory requirements.
Data silos and fragmentation. Life sciences organizations continue to struggle with siloed data that hampers visibility to optimize decision-making. As unstructured data volumes grow, managing storage, backup, and disaster recovery costs is an intensifying priority across IT budgets.
Search and access. When moving data from clinical applications and instruments into storage, contextual data is easily lost. Enriched metadata such as project, disease type, instrument type, and patient demographics allows researchers to search on keywords and find what they need without manual digging.
Regulatory and compliance complexity. Rapidly evolving regulatory standards add layers of complexity to data management. Life sciences companies must embed cybersecurity, software validation, data privacy and AI governance into regulatory strategy.
Cybersecurity threats. Pharmaceutical companies must simultaneously adhere to HIPAA for patient data, GDPR for data privacy, and CCPA for consumer protection, demanding extensive resources for data tracking, reporting, and audit preparation.
The AI Opportunity
AI is now reshaping virtually every dimension of life sciences operations from clinical trials to R&D. The global market for artificial intelligence (AI) in drug discovery is projected to grow from $4B in 2026 to $20B in 2032, according to Statista.
The success rate of 21 AI‐developed drugs that completed Phase I trials is 80%–90%, significantly higher than 40% for traditional methods, according to Clinical and Translational Science. The WHO International Clinical Trials Registry has identified 596 clinical trials utilizing AI worldwide by 2025.
Along with AI’s promise comes heightened responsibility. Organizations need tools and processes to manage AI-related data risks, including data quality, accuracy, bias, and protection of sensitive and regulated datasets. Regulators increasingly expect AI governance to be embedded into product and data architecture from day one, not bolted on after the fact.
Unstructured Data Management: The Critical Enabler
Faster, safer development of life sciences products depends upon getting the right unstructured data to the right tools at the right time. This includes instrument data from laboratory systems, genomics data, diagnostic and monitoring data from patient wearables, and patient demographics and outcomes data from clinical information systems.
Key priorities include metadata management, extracting metadata from unstructured data and integrating it with structured datasets, and ensuring unstructured data complies with data protection regulations, particularly when integrating IoT and other emerging data sources.
- An unstructured data management platform can index data across diverse storage platforms to extract metadata into a Global Metadatabase.
- It allows users to apply metadata tags such as project, disease type, instrument type and demographics to enrich the searchable context for the files.
- When IT moves files to new storage or to archives such as in the cloud, researchers can search on keywords and find what they need without manual digging.
After a project has finished, a researcher can add tags to the resulting data sets to support new searches and projects. Data classification can also tag personally identifiable information such as ePHI and company sensitive data such as medical record numbers and IP to ensure proper handling of this data with AI.
Unstructured data management solutions also help life sciences companies manage the vast expense of data storage, by identifying data that can move to cold data storage. Tools that can effectively migrate data to the cloud and to the right tier of storage based on the data set’s age and value will be imperative—especially with regulations requiring the retention of certain data types for many years.
Automated workflow capabilities such as Komprise Smart Data Workflows automate and simplify the process of finding, ingesting, copying or tiering data to cloud data lakes and AI tools. This technology can also rapidly enable PII detection across billions of files to exclude this data from being used in AI and move it into secure storage.
Two Life Sciences Case Studies for Unstructured Data Management
Pfizer is saving 75% on storage using Komprise to analyze and continuously tier and migrate cold data to Amazon S3 as it ages. Pfizer storage managers and researchers are finding additional benefits from analytics-driven unstructured data management, including zero user disruption and a foundation for delivering self-service to line of business teams.
The company is looking to use Komprise further by leveraging Deep Analytics and the Global Metadatabase so that authorized research users can search for their own data and copy or move it to locations for analysis. Read the blog.
“You can use Komprise to scan all your data, analyze costs and create business rules and then Komprise will act automatically against those rules,” — Pfizer IT director.
A major U.S. biotechnology company with more than 10,000 employees needed to address 20%+ YOY growth in unstructured data across multiple sites, storage platforms, and file types. Research data was expanding quickly, putting pressure on primary NAS capacity, hardware budgets, and IT operations.
Using the Komprise Global Metadatabase to index and understand its unstructured data across storage and departments, the biotech was able to:
- Tier (archive) more than 2PB to the cloud, saving $3M and avoiding user and app disruption with Komprise Transparent Move Technology.
- Gain deep visibility and easier search into research data for AI projects/
- Improve compliance, using Komprise Smart Data Workflows for PII detection and mitigation. The team has already surfaced 360,000 files requiring review and action.
Life sciences organizations are critical to the U.S. and global economy, delivering new therapies and technologies to cure chronic, deadly diseases and improve patient health and quality of life. The strategic, storage-agnostic management of unstructured data will be an imperative capability in years to come.
In our next post in this industry series, we take a closer look at the healthcare industry, another sector with massive unstructured data challenges and which has been going through enormous transitions in recent years.
Learn more about Komprise unstructured data management for Life Sciences and Genomics.
