With the exponential growth of unstructured data, effective data management has become more critical than ever. But managing all of this data (a staggering 90% of all enterprise data, according to IDC) has become a challenge that organizations have only recently started to wrap their heads around. To maintain a strong data protection posture, it’s time to address the unstructured data challenge and conquer this mountain of crucial and sensitive information often contained within these documents and files.
Learn about four key unstructured data challenges and how to solve them.
Challenge 1: Data Proliferation and Lack of Visibility
The sheer volume and diversity of unstructured data make it difficult for organizations to manage and protect it effectively. From email communications to invoices to sensitive human resources documents, unstructured data is varied and often scattered across multiple repositories, in the cloud and on-premises. The exponential growth of this data – IDC predicts a 21.2% compound annual growth rate over the next five years – exacerbates the challenge, as it becomes increasingly difficult to locate and organize data in a meaningful way. Without a handle on data growth, organizations risk losing control of growing storage costs and an expanding attack surface.
Solution: Improved Data Discovery and Management
The ability to find and organize data is crucial for organizations to increase efficiency, decrease risk, and reduce costs. By implementing an intelligent solution to continuously discover, classify, and manage unstructured data, organizations will be armed with data insights that can be acted upon to , and take advantage of opportunities for storage cost optimization.
Challenge 2: Scalability and Speed Limitations
Many existing solutions for discovering and managing unstructured data are slow and inefficient, particularly when dealing with large datasets. Manual-based approaches are error-prone and incapable of keeping pace with the rapidly evolving data landscape.
In 2022 it took an average of 207 days to identify a breach and another 70 days to contain it, according to research from IBM. The longer this takes, the more costly the data breach is to the organization — in many instances costing more than $4 million. With stakes this high, organizations can’t afford to waste time when it comes to classifying, managing, and protecting their unstructured data.
Solution: Automated Data Management that Keeps Up With Growth
As data grows in size and complexity (IDC predicts more than 200 zettabytes of unstructured data will be created by 2026), organizations need scalable solutions that can process and analyze unstructured data as quickly as it’s being generated. The answer lies in platforms that deploy artificial intelligence and machine learning to quickly and accurately scan and process unstructured data – and automatically modify or remove permissions or shared links. These solutions enable faster identification, classification, and protection of unstructured data, reducing the time and cost associated with data breaches, and potentially even preventing them before they occur.
Challenge 3: Data Siloes and Integration Challenges
According to estimates, an organization with 500-2,000 employees uses an average of 1,558 cloud apps each month – 138 of which are used to upload, create, share, or store data. Many of these unstructured data repositories are often siloed, designed for specific purposes but lacking seamless integration across the organizational ecosystem. This fragmentation hampers effective data management, restricting the reach and effectiveness of data loss prevention systems and other data protection mechanisms.
Solution: Data Management Platforms with Cross-System Integration
Organizations need to bridge these silos and enhance data visibility and control across all systems to overcome this unstructured data challenge. Modern platforms for managing unstructured data offer extensive system connectivity, providing a unified interface to effectively identify and manage all data repositories. This unification ensures that classification labels and governance policies are applied to all enterprise data repositories, improving compliance and data protection.
Challenge 4: Lack of Ownership and Accountability
Unstructured data often lacks clear ownership and accountability within organizations. Remote-hybrid work arrangements and frequent workforce changes have led to orphaned data and disconnected ownership. This situation raises questions about who should take responsibility for managing and securing this data. Is it an IT problem, a security concern, or a broader business responsibility? Resolving this challenge requires collaboration and a clear delineation of roles and responsibilities — but even before those can be defined, an organization has to understand its data.
Solution: Modern Approach to Data Management
Implementing a solution to continuously discover and classify unstructured data is one part of the equation. It will provide clear visibility into where data is stored, whether its vulnerable, and who has access to it. But just as important is a modern approach and methodology to ensure there is a convergence of data management initiatives across departments. This makes establishing ownership and accountability much more effective.
Overcome Your Unstructured Data Challenges
While each organization’s data landscape is unique, these challenges are not. The explosion of data has left many organizations scrambling to keep up. But the risks of ignoring unstructured data are growing, and help has arrived.
The DryvIQ platform offers a single solution to continually classify, manage, and protect unstructured data across more than 40 on-premises and cloud-based platforms — at speeds of up to 100TB per day, at petabyte scale. More than 1,100 worldwide organizations have put their trust in DryvIQ to help them address their large-scale, complex unstructured data management projects.
If you’re ready to take control of your unstructured data but aren’t sure where to begin, contact us for a free sensitive data discovery assessment.