Migrating large volumes of unstructured data often measured in petabytes and encompassing billions of files, is a daunting task facing IT teams today. These files vary in type and size, from tiny logs to massive video files, and are often scattered across legacy systems, local storage arrays, and different business units. Enterprises commonly face major hurdles with these large-scale migrations, including performance bottlenecks, data loss, migration delays, misaligned storage placement and compliance concerns.
Despite these challenges, cloud migration is a component of many IT strategies today, including AI, technology modernization, data center consolidation and overall cost efficiency. A data-centric approach with analytics is a great jumping-off point for cloud migrations and cloud expansions.
First, let’s review why unstructured data migrations are so difficult
Unlike structured data that fits neatly into databases, unstructured data lacks a consistent schema. It’s stored in countless files and folders, spread across different environments, and used by various departments for diverse purposes. This makes large-scale migrations more than just a “lift and shift” operation. When migrating petabytes of data, even small oversights can lead to massive complications.
Massive file counts and large volumes of small files can overwhelm traditional scanning and indexing processes, causing unexpected delays. Network interruptions, file locks, or system errors can derail transfers and result in data loss. Legacy tools or free utilities often fail to scale beyond a few hundred terabytes, creating performance bottlenecks.
Without insight into data usage patterns, organizations may misplace cold data in expensive cloud storage or, conversely, hinder productivity by storing frequently accessed data in slower tiers.
The Case for Analytics-Driven Unstructured Data Management
Before moving any data, IT leaders must understand what they have, how it’s being used, and what it costs to store and manage it. In large, distributed organizations, data is often spread across silos, legacy storage, cloud storage, and even servers under desks or in closets – making it difficult to get that visibility. An analytics-first approach addresses this need by scanning and indexing all unstructured data across environments, then categorizing it based on access frequency (hot, warm, or cold), file types, data growth patterns, departmental ownership, and sensitivity (e.g., PII or regulated content).
With these insights, enterprises can:
- Right-place data: Ensure active data resides in high-performance cloud tiers, while infrequently accessed files move to lower-cost archival storage.
- Reduce scope and risk: By offloading cold data first or excluding redundant and obsolete files, the total migration footprint is much smaller.
- Avoid disruption: Non-disruptive migrations ensure that users and applications can still access data during the transfer process.
- Optimize for compliance: Proper classification helps ensure sensitive files are placed in secure, policy-compliant storage.
Enterprise-Grade Data Migration Capabilities
An enterprise-grade solution for large-scale cloud migrations delivers a lot of advantages over free tools. They are built to handle multi-petabyte, multi-billion file workloads and with speed. They should be able to move small and large files efficiently over the WAN and accomplish this in hours or days – not weeks.
Here’s what else to look for:
- Analysis of all unstructured data across all storage gives details on data volumes and growth rates, file sizes and types, owners and more so you can make the best decisions for its target storage class.
- The ability to detect and confine sensitive data prior to a migration.
- File-level tiering to right-size your migration, save on storage costs, achieve cloud-native access and allow transparent access to moved data.
- Ensure data integrity by migrating all file attributes and permissions with full checksums on every file.
- Users and applications can access data during a migration: business as usual.
- Included tools to proactively identify potential bottlenecks and other issues that can explode your migration.
- Preservation of all metadata and access controls from source to target.
Continuous Optimization After the Migration
Data migration isn’t a one-time event. Once in the cloud, unstructured data continues to grow and without ongoing management, cloud costs can spiral. Analytics-driven solutions support ongoing data lifecycle management, so IT can automatically tier aging data from high-cost hot storage to cooler, more affordable tiers as it becomes less relevant.
This continuous optimization approach ensures sustained ROI from the cloud, even as data volumes increase. You also achieve operational efficiency. Automated tiering reduces manual processes and errors while maximizing cost savings.
Final Thoughts: A More Strategic Approach
A successful cloud migration avoids costly delays and data loss, while ensuring that your data is placed in the right storage tier the first time around. So don’t think of a cloud migration as just a one-off project. With an unstructured data management strategy you can continually right-place your data, flexibly move it to new locations for analysis, AI or compliance and save more.
By Benjamin Henry