Why Unstructured Big Data Storage Must Be Central to Your IT Strategy

For many IT organizations, data storage is treated as an after‑thought rather than a strategic priority, yet when it comes to big data management, storage should be at the core of the strategy.

Unstructured data records key events graphically, captures paper documents, and reports operational conditions via sensors and IoT devices. A 2020 NewVantage survey of C‑level executives found that only 37.8% of respondents believed they had created a data‑driven culture, while 54.9% felt they could not compete in data and analytics.

Jeff Fuhman, senior vice‑president of marketing at Seagate, noted that about 43% of collected data remains unused, representing a huge untapped value in unstructured data. IDC research shows that unstructured big data accounted for 90% of global data in 2020, underscoring the importance of understanding, integrating, and leveraging it for business efficiency and growth.

A major challenge is data management. Companies need data architecture, tools, and processing expertise, as well as a clear big‑data storage strategy.

Cataloging and analyzing unstructured data is essential, but the cost burden often prevents organizations from performing these compute‑intensive operations, which require large data centers and cloud architectures to deploy ultra‑high‑capacity, disk‑driven storage systems. Once processed, the data must be replicated and reused across many departments and sites.

Fuhman emphasized the need to access unstructured data near its source and move it to various private and public cloud data centers as needed, driving a shift from closed, proprietary, isolated IT architectures to open, hybrid models.

In a hybrid model, storage must be orchestrated so different data types reside in appropriate locations—for example, IoT data for real‑time operational efficiency may be stored on edge servers at manufacturing plants, while compliance‑related data stays in an internal data center.

Because unstructured data lacks inherent structure, it must be tagged by meaning and purpose before subsets can be distributed to different enterprise points of need.

The scale of storage, cataloging, security, and distribution operations pushes many enterprises toward on‑demand, cloud‑based storage, avoiding the high costs of upgrading on‑premises data centers with high‑power storage drives.

Handling datasets ranging from 100 TB to several petabytes presents challenges across industries. In healthcare, for instance, the massive volume of data is critical for protecting and treating patients, and hidden correlations in raw formats could unlock life‑saving insights, but extracting value from such volumes requires first mobilizing the data.

When companies aim to maximize value from big data, storage should be a strategic focus rather than an afterthought. The emphasis should be on cost‑agile and data‑agile storage that can scale up or down as needed, with cloud‑based storage being the most suitable solution, while on‑premises storage is limited to highly sensitive data for compliance and IP protection.

It is also important to understand how managed data is distributed across the organization.

Fuhman concluded that we live in a data‑driven world, and successful enterprises recognize that if massive datasets cannot be moved efficiently and accessed easily, business value will suffer.