Why Modern Data Archiving Is Key to a Scalable Data Strategy

The Critical Role of Archiving in Enterprise Data Transformation

To succeed in the information economy, businesses invest time and money into their data strategies. This requirement has become significantly more urgent in the last couple of years, driven by the widespread integration of generative AI technologies, which depend on access to high-quality, well-structured datasets at volume.

However, organizations are quickly learning they can’t simply throw all data, new and old, at an AI strategy; instead, it needs to be accurate, accessible, and, of course, cost-effective. Without these requirements in place, it’s far from certain AI-powered tools can deliver the kind of insight and reliability businesses need.

As part of the various data management processes involved, archiving has taken on a new level of importance. In this context, the role of archiving is to identify and relocate data to long-term storage based on set policies, without relying on intermediary systems for future access. It differs from tiering or NAS cloud gateways by enabling one-time, policy-driven data movement that supports efficient storage management and long-term data governance.

Part of the challenge is that much of the data required to drive AI projects is unstructured, meaning it lacks a predefined format, resists easy categorization and requires advanced tools to manage, analyze and extract value, particularly across today’s diverse storage environments. Trying to “feed” AI systems with unstructured data, whether it’s text, videos, images, emails, social media posts, sensor data, or a myriad of other sources, can quickly become overwhelming.

Without visibility into what unstructured data exists, where it resides, how it’s being used and whether it holds any actual value, organizations risk training their AI on poor-quality inputs, leading to unreliable outcomes and the potential for significant business risk. The issues are widespread, with McKinsey reporting governance and integration issues are among the biggest obstacles getting in the way of successful implementation.

In many of these situations, effective archiving is the missing link. Instead of reactive data storage, where organizations continuously add capacity to keep up with growth, archiving enables a proactive approach that identifies inactive or low-value data and relocates it to more appropriate, cost-effective storage. This not only frees up primary systems for high-value workloads but also helps create the clean and fully organized datasets required to train AI systems properly.

Functional versatility

The role of archiving doesn’t end there. Across various cloud adoption and data migration scenarios, it is vital for organizations looking to streamline processes and reduce complexity.

For organizations that need to migrate data, for example, archiving is used to identify which essential datasets, while enabling users to offload inactive data in the most cost-effective way. This kind of win-win can also be applied to cloud resources, where moving data to the most appropriate service can potentially deliver significant savings.

Again, this contrasts with tiering systems and NAS gateways, which rely on global file systems to provide cloud-based access to local files. The challenge here is that access is dependent on the gateway remaining available throughout the data lifecycle because, without it, data recall can be interrupted or cease entirely.

The most effective archiving tools address this problem by connecting directly to primary and archive storage systems, thereby eliminating the need for a middleman gateway and delivering significant performance improvements. In practical terms, migration can focus on ensuring high-value data is cloud-ready and cold data is moved to the most suitable platform.

This can also drive a wider storage optimization and efficiency strategy, where organizations can identify their cold or inactive data (typically more than 60% of the modern enterprise data estate). If this data is stored on high-performance, high-cost infrastructure, it can be moved to a more suitable platform or service, thereby releasing capacity on primary storage systems for active workloads.

It then becomes practical to strike a much better balance across the typical enterprise storage technology stack, including long-term data preservation and compliance, where data doesn’t need to be accessed so often, but where reliability and security are crucial. For each of these use cases, the use of vendor-neutral archiving solutions allows data to be managed across heterogeneous environments without the risk of lock-in, ensuring long-term accessibility and flexibility. When examining specifications, the availability of policy-based automation further streamlines lifecycle management, eliminating the need for manual oversight and reducing the likelihood of human error. Organizations pursuing this approach can target some transformational benefits. Whether the focus is on ensuring AI projects deliver on their objectives, optimizing storage performance, or both, modern archiving technologies can bridge the gap between objectives and impact.

ABOUT THE AUTHOR

Steve Leeper

Steve Leeper oversees the market development for Datadobi and manages the Presales Sales Engineers team globally. A 30-year veteran of IT, Leeper has held a variety of technical and sales roles at Andersen Consulting, Sun Microsystems, and EMC.