Data drives everything, and businesses must prepare their data to fully leverage technologies, including generative AI (GenAI), cloud computing, and advanced applications. Archiving is a key strategy in this process, ensuring data is clean, accessible, optimized, and cost-efficient.
By consolidating and archiving inactive or redundant data, companies can create the high-quality datasets that are essential for training GenAI models and achieving reliable outcomes.
But first, it’s important to clarify what archiving actually means. Unlike tiering, which functions like a permanent librarian selectively fetching individual files from deep storage, true archiving is a one-time event that moves files based on defined policies, such as last access or modification date. Once archived, files are stored on a long-term platform and remain accessible without reliance on any intermediary system or application.
In this context, one of the main challenges is that most enterprise data is unstructured, including everything from images and videos to emails and social media content. Collectively, these vast and diverse data lakes present a formidable management challenge, and without rigorous control, organizations risk falling victim to the classic “garbage in, garbage out” problem. Poor-quality inputs lead to biased or meaningless AI outputs, undermining even the most promising AI investments.
These issues are widespread. According to McKinsey, nearly three-quarters (70%) of organizations implementing AI face data management obstacles, including “defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data plays in capturing value.”
This is where archiving plays a foundational role. By identifying and isolating inactive, redundant, or outdated data, archiving creates the clean and organized data estate needed to train GenAI models effectively. It ensures that only relevant, high-quality data is fed into AI systems, accelerating time to insight and reducing risk. With the proper data management and archiving practices in place, businesses can then shift from reactive data storage to proactive data preparation, unlocking GenAI’s full potential in the process.
Cloud Migration and Adoption
Elsewhere, archiving also plays a critical role in data migration and cloud adoption. First, it can simplify migration by identifying and keeping essential data. Second, it can reduce costs and complexity by allowing you to retire outdated or duplicate files.
Additionally, by offloading inactive data to cost-effective archives, businesses can free up cloud resources for active workloads and adopt cloud-native formats to enhance scalability and efficiency.
In contrast, traditional NAS (network-attached storage) gateways or tiering systems are often used to bridge on-prem NAS storage with cloud environments.
They provide a global file system that enables cloud-based access to locally stored files, with all metadata managed through the gateway itself. While this setup can give the appearance of archiving, it falls short of true archiving because access to the data remains dependent on the gateway. If the gateway is decommissioned or the vendor exits the market, data recall becomes a major risk.
Modern archiving technologies that connect directly to both primary and archive storage platforms eliminate the need for a middleman, drastically improving migration speed, accuracy, and long-term data accessibility. This means organizations can migrate only what’s necessary, ensuring high-value data is cloud-ready while offloading cold data to cost-efficient archival platforms. This not only reduces cloud storage costs but also supports the adoption of cloud-native formats, enabling greater scalability and performance for active workloads.
Driving Optimization and Efficiency
Beyond that, archiving also drives storage optimization and operational efficiency.
For modern enterprises, where more than 60% of enterprise data is typically inactive and often goes untouched for years, organizations are still consuming high-performance (and high-cost) storage.
Using metadata-driven policies, such as file age, access frequency, or ownership status, companies can identify these cold datasets and relocate them to appropriate archive platforms. This clears space on primary systems and optimizes storage tiers for active workloads.
For this kind of use case, tiered storage, deduplication, and advanced monitoring tools not only reduce storage costs but also ensure long-term data preservation.
This is crucial for compliance, analytics, and unlocking the hidden value of historical data. If done this way, businesses can transform data into a strategic advantage that fosters innovation and sustainability.
In addition, vendor-neutral archiving solutions also allow data to be moved and retrieved across heterogeneous environments without lock-in, ensuring long-term accessibility and flexibility. Policy-based automation further streamlines lifecycle management, eliminating the need for manual oversight and reducing the likelihood of human error.
Advanced archiving platforms can also support customizable dashboards and granular visibility, helping IT teams track what’s stored, where it’s located, and how it’s being used. These capabilities are critical for ensuring regulatory compliance, preparing for audits, and unlocking the hidden value of historical data.
Bring all these capabilities together, and effective archiving can transform a reactive storage strategy into a proactive, performance-led approach. What’s more, by embedding archiving into their digital infrastructure, businesses not only optimize how data is stored and managed but also unlock its full potential as a driver of long-term value.