Across industries, unstructured data management is no longer just a cost-saving tactic but a strategic enabler.

In every industry, data engineers and data scientists are looking to better leverage the data hidden within file shares and locked within different systems. They need this data to create workflows for AI and analytics tools so that they can learn more about their markets, create new products and services, or improve business operations and customer relationships.

The other side of this equation is that enterprise data brings ample risk that is not well understood by the business, nor even within IT. For example:

Data storage and backups make up at least 30% of most IT budgets when money needs to be spent on innovation, cybersecurity, and great customer experiences;

Most unstructured data is not leveraged for business value;

A large percentage of unstructured data, as much as 80%, is rarely used and takes up expensive storage space;

Many organizations don’t have clear retention and deletion policies, nor go through regular cleansing exercises of their data out of concern that departments will lash back. Duplicate data alone can easily constitute 30 to 40% of the average enterprise data footprint;

Unmanaged and unknown unstructured data estates across hybrid IT silos create additional security and compliance risks.

Here’s a look at several industries and their data management requirements and challenges, as culled from our years of working with customers on unstructured data management strategies.

Healthcare: Cutting Costs Without Cutting Access to Vital Data  

Healthcare is a data beast. By some estimates, healthcare organizations are the world’s largest producers of data, driven largely by the volume of a variety of clinical images and machine data. Cost containment is and has always been a dire mandate in healthcare, and even more so now with changes to Medicare reimbursement. Healthcare organizations also face strict data retention regulations and difficulties deleting data due to legal and research requirements. 

One leading health system, managing over 16PB of NAS storage, turned to unstructured data management to analyze and archive cold data to Azure Blob, reducing pressure on its primary storage systems. Without interrupting users, the IT organization moved more than 2PB of data, helping delay expensive hardware refreshes. 

For organizations with chain-of-custody requirements, common in regulated industries like healthcare, visibility into file location and metadata is critical. Finally, as compliance is an ever-present need given the high sensitivity around patient data, complete unstructured data visibility, search, and auditing give health IT directors greater control to lower risks.

Life Sciences: Turning Data Chaos Into Research Acceleration

Life Sciences organizations are also one of the largest data-producing sectors. They often deal with millions of small but high-value files, unpredictable data bursts, and the need for long-term retention without clear deletion policies. These factors complicate IT infrastructure planning.

In biopharmaceuticals and biotech labs, the explosion of TIF image files from scientific instruments creates additional challenges. In one example, a company transitioned from locally stored research data to a centralized NAS array. To keep pace with rapid growth, the IT team deployed cloud tiering to Azure and used data analytics to pinpoint and move stale data. This prevented overprovisioning and helped IT support research pipelines without bottlenecks. 

Enterprise IT teams typically can’t see information about their data stored in one place, said Anthony Fiore, a storage solutions expert at AWS. Detailed visibility, delivered by data management software, is exciting to IT people in life sciences and other sectors: “We have customers with NAS shares which contain many silos of data in a single share, and it’s hard to know how they can break it up by line of business or if they even care about this data. But once they see all the metadata, they get a better understanding of how everything works, and then they can tag and search for it later.”

Financial Services: Eliminate Risk and Power AI with Clean, Governed Data

Financial institutions operate under stringent regulatory constraints (e.g., SEC, FINRA, GDPR) and are often burdened by decades of file share growth. Data sprawl, decentralized IT control, and compliance make managing file-based data extremely complex. As well, financial institutions must ensure AI models used for credit risk, fraud detection, or trading are governed appropriately and free of bias and outdated data. Having a systematic way to understand, cleanse, classify data, and create safe and monitored AI data workflows is a growing requirement. 

For one multinational insurance firm, the move to Azure was about not just cost savings but also modernization, analytics, and AI readiness. They used unstructured data management to reduce capacity needs on expensive primary storage in the data center, tiering over 600TB to lower-cost object cloud storage. They also use cloud-based tools to develop AI-enhanced insurance products, with unstructured data forming a critical part of those predictive models. The ability to classify and segment unstructured data prior to AI ingestion is critical to managing costs and delivering accurate results.

See also: Escaping the Data Storage Trap in Real-time Visual Intelligence

State & Local Government: Extend Infrastructure Life and Strengthen Data Oversight

Public sector IT organizations tend to have aging infrastructure and a higher percentage of legacy applications than the private sector. They want to modernize, but budgets are usually tight, and there is minimal cloud adoption due to security policies, along with legal mandates that require data retention without clear deletion paths. IT leaders need to balance service delivery with strict compliance requirements. 

For state agencies, archiving files to the cloud helps reduce reliance on aging hardware. Visibility into last modified/access dates allows IT to move only rarely accessed data off high-performing storage. At the same time, integrating storage and data management systems with security and compliance tools is critical to ensure secure and auditable access. Though data tiering adoption remains cautious, many agencies are using reporting to build the case for unstructured data lifecycle policies, especially around stagnant departmental data.

Engineering & Architecture: Win More Projects by Unlocking Hidden Data Value

Engineering and design firms generate unstructured data at massive scale, especially from CAD, GIS, and 3D modeling files. These files are large and difficult to manage, especially across distributed teams and inherited legacy systems from M&A activity. These firms need to preserve historical project data for reference, liability, and reuse. One global firm, managing over 6PB, uses unstructured data management to identify and move project files older than three years to a Cloudian archive, backed up in Azure. This preserves performance on active HPE arrays while maintaining accessibility. In one case, queries across their file data stores helped them quickly locate soil test data for a project in an earthquake-prone area, saving time and supporting a critical infrastructure design.

As they continue to integrate acquisitions, the firm uses analytics to evaluate newly inherited file servers. This visibility lets them prioritize what to retain, migrate, or archive. Their goal is to eventually index all unstructured data, enabling AI-based modeling and reducing knowledge silos across business units.

Energy: Improve Field Efficiency and Compliance with Centralized Data

These companies face remote site constraints, variable bandwidth, compliance with international safety and operational regulations, and an increasing need to support data-driven remote diagnostics and digital twins.

In one company, the decision to adopt unstructured data management came from the need to retire edge storage across hundreds of remote locations. With video logs, drawings, and offshore maintenance records piling up, they began archiving cold data to Azure to centralize and control their unstructured data.

With chargeback models in place, file data insights became crucial to departmental accountability. The long-term goal is to support survey and inspection data workflows, like underwater ROV imagery, into AI-ready environments for predictive maintenance and compliance. 

Semiconductors / Manufacturing: Protect IP While Cutting Expensive Storage Footprint

Semiconductor firms must protect high-value IP, manage globally distributed data, and adhere to strict export control and security requirements while ensuring engineers have high-performance access to active datasets.

A global semiconductor manufacturer uses highly specialized scanning equipment that generates vast amounts of proprietary image data. With 97% of data still stored on-premises, they needed an efficient method to archive older scan data without compromising IP protection or retrieval performance. By pairing unstructured data management with Cloudian S3 storage, they implemented cold data policies to move any files not accessed in 12 months off primary servers. With symbolic link preservation and metadata tracking, the company ensured compliance with internal IP handling protocols and reduced reliance on expensive primary NAS.

Conclusion: Transform Unstructured Data Into a Strategic Business Asset

Across industries, unstructured data management is no longer just a cost-saving tactic but a strategic enabler. Whether it’s supporting AI workflows in insurance, maintaining regulatory compliance in healthcare, or streamlining infrastructure in manufacturing, organizations are recognizing the need to combine data governance with flexible data access and movement.