In today’s world, organizations generate huge volumes of data, volumes that double every two years, on average. In data-intensive industries such as financial services, healthcare, education, high performance computing, oil/gas and life sciences, most of that data is unstructured.

As the volume and velocity of unstructured data increases, so do the challenges involved in data management, including end-to-end data risk protection, governance, discovery and access – regardless of the file type, device or social origination.

With traditional storage infrastructures, unstructured data is scattered in disparate silos across multiple applications and platforms, causing increased storage costs. It also can make data inaccessible to the organization and vulnerable to compliance issues, which can create risk for the organization. This data comes in all different shapes and sizes and presents considerable challenges for organizations unable to make sense of its datasets due to sprawl across intelligent portable devices, desktops, servers or collaborative applications. All data is not created equal, and treating it as though it is has significant capacity and cost implications. Organizations that do not understand the complete data landscape can be at a significant disadvantage.

An alternative to this is to implement a data-defined storage architecture that addresses data-management challenges.

Implementing data-defined storage means emphasizing the value of information according to accessibility and content over the traditional parameters of storage media, location and cost. Taking a data-centric approach to data management enables a business to leverage data as a strategic asset instead of as an ongoing cost center. Silos are removed and application, information and storage tiers are united into a single, integrated management architecture. Data-defined storage delivers massive scalability, end-to-end data protection and security, and helps organizations realize value from unstructured data by separating and abstracting the metadata from the data, accelerating time to insight, creating greater opportunities and driving higher revenue. This can reduce total cost of ownership by increasing cross-silo data availability and capacity utilization, improving operational efficiencies. This unified approach to managing unstructured data reduces the storage costs and the associated overhead of data search and discovery while allowing strict data retention and security policies to be enforced.

In a data-defined storage architecture, all unstructured data is automatically captured and stored in a massively scalable repository using virtualized object storage pools locally or around the world. File system virtualization is used to provide a single unified view of all data. End-to-end data governance, protection, security and mobility across storage, servers and smart devices ensure data compliance and risk protection.

In many industries, the transfer and flow of information is a competitive advantage that drives an ever-increasing need for faster, more reliable access to data. Without consistent, timely, and reliable discoverable access, organizations face unacceptable delays in the retrieval of information, which could result in missed opportunities or fines by regulatory agencies for missing data discovery deadlines. Financial service organizations, for example, are subject to regulations addressing the storage of digital data. New rules require retention of all forms of business communication, including documents, email and attachments, instant messages, and other forms of digital communication. With a data-centric approach, organizations can lower regulatory risk, which means lowering legal, commercial and reputational risks. Organizations can utilize identity-focused compliance, information governance, and improved data security while receiving on-demand access to the right information in the right place at the right time via deep content indexing.

In addition, data-defined storage technology enables organizations to leverage analytics through integration with big data analytics tools to tap into the full value from their vast stores of unstructured data. The distributed metadata repository efficiently exposes content throughout the grid, allowing users to point analytics tools at data in place, allowing real-time insights and enabling enhanced decision making and improved competitive advantage.

Research firm IDC states that only 3 percent of data is tagged and less than 0.5 percent is analyzed. As unstructured data growth continues unabated, the market for next-generation scale-out storage platforms will see a higher growth rate relative to that of traditional disk storage systems. IDC estimates that worldwide revenue for scale-out unstructured data storage solutions, such as data defined storage, will exceed $34.6 billion in 2016. By 2017, IDC predicts that more than 80 percent of data storage capacity will be shipped as scale-out solutions, which deliver value that modern big data techniques and technologies can extract and harness. By then, most data will be unstructured, making it important to consider the advantages that data-defined storage solutions offer for managing unstructured data.

Shahbaz Ali is the President and Chief Executive Officer of Tarmin, a leading provider of data-defined storage management solutions for enterprise infrastructure, storage and data management. A seasoned entrepreneur with a history of successful ventures, Ali has more than 20 years’ experience creating world-class software driven solutions for enterprise clients. Ali holds a BSC (Hons) in Software Engineering from London Southbank University and has completed a PhD course of study in Software Requirements Engineering from the prestigious Open University.