From Silos to Strategy: Why Holistic Data Management Drives GenAI Success

In the era of GenAI, data management goes beyond storage, security and resilience; it’s a strategic necessity for turning data into enterprise value. A holistic approach to data management is essential not only for operational resilience but also for maximizing the value of data in today’s interconnected and cloud-reliant world. AI is the ultimate workload for hybrid cloud, requiring enterprises to securely unify data across hybrid environments.

This is why today’s AI-centric organizations must think hybrid when it comes to their data management strategy. We can look at an incident last year as an example—when a significant outage at major cloud provider disrupted operations for several multinational companies, including delays in financial transactions and interruptions to enterprise applications. This event, which caused a seven-day outage, underscores the critical need for organizations to diversify their data and storage strategies to ensure resilience against potential single-provider failures.

Data is the Cornerstone of AI

Data serves as the essential resource that enterprises depend on for their operations and is at the heart of market competitiveness. Effective data management is a prerequisite for advancing AI innovation and enterprise business transformation. When dealing with complex and disorganized data, enterprises must harness the “transformative power of data organization” and utilize data management platforms to centralize “data storage.” This is the crucial first step when advancing AI development and driving digital transformation.

According to IDC, only one-third of enterprise data is stored in the public cloud, while the remaining seventy percent is dispersed across data centers, internal systems, and edge locations. With the increased adoption of GenAI, much of the data driving these use cases is expected to be created at the edge rather than in the cloud, further contributing to this fragmentation. Our regular discussions with management have shown that enterprises are now not only focused on improving the efficiency, visibility, elasticity, and scalability of their data but are also planning to implement multiple backup systems with equal functionality to prevent single points of failure.

Additionally, it’s important to note that many enterprises often end up scattering data across multiple locations without a clear plan. This unplanned approach, often referred to as “hybrid-by-accident”, complicates data management and increases the risk of security breaches during data transfer. Additionally, it leads to the inefficient use of IT and financial resources. As a result, companies struggle to effectively utilize their valuable high-quality operational data, which in turn hampers AI training and application processes.

To address this, enterprises need to transition to a “hybrid-by-design” strategy, where infrastructure, applications and data deployment across hybrid environments are intentional and well-managed. This shift enables companies to better leverage their high-quality operational data, allowing for enhanced AI training, inferencing, fine-tuning and retrieval augmented generation applications.

Creating Data Management Architecture to Drive Transformation

While data distribution is essential to mitigate risks, it requires a unified approach to be effective. Many enterprises are recognizing the value of implementing unified data architectures that simplify storage and data management and centralize the management of diverse data platforms. These architectures, combined with intelligent data platforms, enable seamless access and analysis of data, making it easier to support analytics and ingestion by generative AI.

IT managers can further enhance a system’s data analysis, network security, and introduce a hybrid cloud experience to simplify data management. Today, the tech industry is focused on streamlining how enterprises manage and optimize storage, data, and workloads and a platform-based approach to hybrid cloud management is critical to manage IT across on-premises, colocation and public cloud environments.  Innovations like unified control planes and, software-defined storage solutions are being utilized to enable seamless data and application mobility. These solutions allow enterprises to move data and applications across hybrid and multi-cloud environments to optimize performance, cost, and resiliency. By simplifying cloud data management, enterprises can efficiently manage and protect globally dispersed storage environments without over-emphasizing resilience at the expense of overall system optimization.

Data Management Should Not be Overlooked by Management

Effective data management is essential not only for AI development but also serves as a critical risk management strategy. Neglecting AI can result in significant reputational and financial damage. The most talked about outage of 2024 and the largest in IT history might have been mitigated by unifying the data formats of the main and backup systems to allow for seamless switching between them. Additionally, during ransomware attacks, storing mission and business-critical data in a cyber vault offers an extra layer of protection, ensuring rapid recovery and minimizing disruption.

It’s clear that management and IT managers must be prepared for unforeseen risks, and data management, though fundamental, must become unified to unlock its value. Data must move from just storage to become a value creator and that’s only possible when data is accessible, secure, ready to be analyzed and ingested by AI and the daily operations of enterprises. Without effectively utilizing scattered operational data, discussing how to convert data into business value becomes challenging, and falling behind in the AI era is a risk no business or organization should take.