How Automating Relativity's Storage Types Boosts Workflow Efficiency

Ask any Relativity administrator what slows them down, and you’ll usually get the same answer: manual storage management. Every few weeks, someone has to check which workspaces are inactive, decide what to move, run archive jobs, and then double-check that nothing breaks in the process. It’s repetitive and error-prone and eats up time that could be spent improving workflow efficiency.

But that’s changing. Relativity’s storage automation options, from ARM jobs to Cold Storage and Data Grid, now enable teams to set smart, rules-based triggers. These workflows handle archiving, migration, and restores without human intervention. The result is smoother transitions between Repository, Review, and Cold Storage while cutting costs and improving performance.

This article explains how these storage types work, why automation matters, and how teams are using them to boost workflow efficiency in real-world environments.

Understanding Relativity’s Storage Types

Before talking about automation, it helps to understand the function of each storage type.

1. Repository Workspaces

Repository storage is the staging area, the first stop for data before it moves into full review. It’s cheaper than active Review storage and great for early case assessment or analytics work. Think of it as your preparation zone before you commit to the full Review cost.

2. Review Workspaces (Active)

This is where the real action happens. It’s the most resource-intensive storage type because reviewers are actively coding, searching, and analyzing documents. That’s why it’s important to move workspaces out of Review once activity slows down.

3. Cold Storage

Cold Storage is designed for inactive workspaces that may need to be accessed later. You can archive directly from Cold Storage or restore a case that reopens. The biggest benefit is that it keeps your data accessible while reducing overall costs.

4. Data Grid (Powered by Elasticsearch)

Data Grid stores extracted long text and audit data separately from SQL. This keeps your database light and your searches fast. It’s especially useful for scaling large environments, as audit tables can get massive over time.

By automating transitions between these types, you stop paying Review rates for inactive data and improve overall system responsiveness.

Automating the Lifecycle: The Core of Workflow Efficiency

Automation in Relativity starts with one simple idea: Let the system handle predictable work. Every workspace has a natural lifecycle of ingestion, review, closure, and archiving. Each phase requires different storage behavior, and automation maintains that consistency.

Here’s what workflow efficiency improves:

• Automated ARM Jobs: You can schedule archive, restore, and move operations using the REST service of ARM APIs or the NET client. Instead of manually running archive jobs, define policies like, “move workspaces to Cold Storage after 60 days of inactivity.”
• Cold Storage Manager: Use the Cold Storage API to program the shifting of workspaces. Teams can trigger these actions automatically when Matters close or projects go quiet.
• Data Grid Text Migration: Large audit tables and long-text fields can be moved to Data Grid using the Text Migration app. It reduces SQL strain and keeps Review environments speedy.
• Automated Workflows App: Combine triggers and actions. For example, “if there’s no activity in 45 days, run Text Migration → Cold Storage move → ARM Archive job.”

Each step replaces hours of manual checking, helping admins focus on value-added tasks. For developers, Relativity’s API documentation explains how to safely automate ARM and Cold Storage operations.

Real-World Automation Patterns

Every organization handles data differently, but most fall into one of these common automation models:

1. Inactivity-Based Cold Storage: Set a trigger to detect inactivity—maybe forty-five or sixty days without Review events. Once that threshold hits, the workflow moves the workspace to Cold Storage and sends an automated notification to the project owner.
2. Lifecycle-End Archiving: When a legal hold is released or a project is officially closed, trigger an ARM Archive job automatically. Store archive metadata and retention details in a shared tracker for compliance.
3. Restore-On-Demand: When a case reopens or a subpoena arrives, ARM Restore jobs can automatically restore the workspace to Review storage. There’s no need to hunt for backup files.
4. Data Grid Migration for SQL Relief: When SQL performance drops or a workspace hits a specific size threshold, schedule a Text Migration job to move long text into Data Grid. This keeps search indexes fast and stable.
5. Repository to Review Promotion: When custodians are approved for review, a trigger moves the workspace from Repository to Review automatically and updates permissions.

These patterns save time and mistakes, which are the biggest threats to workflow efficiency in large-scale environments.

Guardrails That Keep Automation Safe

Automation can go wrong if the order of actions isn’t correct. The good news is that Relativity’s documentation covers best practices. Here are a few to keep in mind:

• Migrate Text Before Cold Storage: If you move a workspace before migrating long-text fields to Data Grid, searches may fail later.
• Run One Text Migration at a Time: Only one Data Grid migration can run concurrently. Queue up jobs to avoid conflicts.
• Adjust Searches After Migration: Keyword Search won’t work against Data Grid text. Switch those to dtSearch for consistency.
• Cold Storage Billing: In RelativityOne, billing changes automatically based on workspace state. You can archive from Cold Storage directly without restoring it first.
• No File Validation During Migration: If you run file validation mid-migration, you’ll get false “missing file” errors. Wait until migration completes.

By following these guardrails, you’ll keep automation reliable while still improving workflow efficiency.

Step-by-Step: Building an Automated Storage Workflow

Let’s walk through how to set up a basic storage automation model that links activity triggers to storage actions.

1. Define Your Policy

Decide what inactivity means for your team—for example, no user activity in sixty days. Then, set your retention rules, such as keeping archived data for three years.

2. Enable the Tools

Install the Automated Workflows app. Confirm ARM and Cold Storage API access. If you’re on RelativityOne, make sure the Data Grid Text Migration app and its agents are active.

3. Create the Triggers

Set a daily automated check to calculate “days since last document view or coding activity.” If it exceeds your threshold, the workflow fires the next action.

4. Chain the Actions

• Run Text Migration if it hasn’t been done.
• Move the workspace to Cold Storage.
• Schedule an ARM Archive job after another thirty days.
• Notify the Matter owner via automated email.

5. Test Before Rolling Out

Start small. Use a low-risk workspace and monitor the process. Check dtSearch functionality, audit logs, and access controls after the automation completes.

6. Plan Rollback Steps

Document your restore plan. You can use ARM Restore jobs to bring back any workspace, but always verify indexes and permissions afterward.

These steps will give you a baseline automation that immediately boosts workflow and consistency across your Relativity environment.

Measuring the Payoff

Once automation is live, the benefits are visible almost immediately:

• Time Savings: Manual workspace transitions take hours of admin time every month. Automation cuts that nearly to zero. In firms with over 200 workspaces, that’s hundreds of hours saved annually.
• Performance Gains: Moving long text and audit data to the Data Grid reduces SQL growth. Searches become faster, and analytics stay stable even as data scales.
• Cost Reduction: By automatically sending inactive workspaces to Cold Storage or Repository, you reduce active storage costs by up to 50%. Those savings compound as your data footprint grows.
• Compliance Confidence: Automation leaves an audit trail of every move, restore, or archive. This helps meet discovery and regulatory requirements with minimal effort.

For context, automated lifecycle management can cut operational costs by up to 30% in large data platforms. That same principle applies in Relativity environments but with less manual work, more structured control, and better use of admin time.

Advanced Ideas for Bigger Teams

If you manage a large RelativityOne environment, consider going beyond simple triggers:

• Portfolio-Wide AutoArchive: Use external orchestration tools or scripts to monitor workspace activity across your tenant. Automatically call the ARM and Cold Storage APIs for multiple Matters at once.
• File Transfer Optimization: For massive projects, Relativity’s Transfer SDK can automate file moves between local sources and RelativityOne file shares. This minimizes downtime when archiving or restoring.
• Data Grid Analytics: Schedule Data Grid migrations during off-hours, and monitor performance using Relativity’s analytics dashboards. You can see real-time ingestion rates and search health.

Let Storage Work While You Work

Automation isn’t about removing people from the process; it’s about removing friction. When you automate Relativity’s storage types, you connect system behavior to business logic. Workspaces move, archive, and restore based on actual activity, not someone’s reminder note.

That’s where workflow efficiency becomes more than a buzzword. It’s measurable. You can spend less time managing systems and more time reviewing data, serving clients, and meeting deadlines faster.

Storage automation turns Relativity from a tool that you manage into a system that manages itself. CaseFlow is a Relativity application that automatically transitions your cases to the different lifecycle stages. Once it’s in place, you’ll wonder how you ever worked without it.

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