5 tips for improving data storage efficiency

1. Plan your efficiency strategy

Take full inventory of:

• Where data is stored -- cloud or on-premises.
• How it is stored -- object, block or file.
• The type of data -- structured, semi-structured and unstructured.
• The amount of data.

Then, assess the current level of storage efficiency based on organizational goals.

From this foundation, you can develop a plan for improving storage efficiency in your current environment and maintaining that efficiency going forward. The plan should be tailored to your organization’s specific data and storage requirements with the following considerations in mind:

• Balancing budget and performance. Although increased efficiency can translate to lower costs, it can come at the expense of performance. Carefully weigh business requirements when strategy planning.
• Cloud pros and cons. The cloud provides a flexible and scalable environment for storing data, but it can bring with it cost, performance and security concerns. Evaluate cloud providers' storage services in terms of cost, performance and integration requirements. Some organizations use the cloud only for specific types of data -- such as backups or archives. Other organizations rely almost entirely on cloud storage services. Cloud usage should be based on business requirements and budget constraints. Right-size cloud resources to avoid over- or under-provisioning.
• Hybrid cloud strategy. If you use both cloud and on-premises storage, carefully plan and implement a hybrid cloud strategy to manage storage resources more efficiently. This can help you gain control and flexibility to address your security, compliance, scalability and performance needs, while enabling you to use your storage resources efficiently. Be aware that hybrid clouds can be complex and difficult to implement and maintain.
• Intelligent tiering strategy. Implement an intelligent tiering strategy that can automatically move data to the most appropriate storage tier and environment based on the data lifecycle management (DLM) initiative. Take egress costs into consideration whenever moving data to, from or between cloud services.
• Object storage. Use object storage for unstructured data when possible. It supports rich metadata, can easily scale, and is generally more cost-effective than other storage options.
• Abstraction. Consider a platform that abstracts data sources and provides a unified view of the data, such as a data fabric, API-based abstraction, federated data management, data virtualization or software-defined storage. Abstracting data sources can help business users better utilize the data, while streamlining data management and reducing the number of client support requests.
• Transition planning. Develop and implement steps for rolling out infrastructure changes, migrating data, decommissioning hardware and deleting data.

2. Update on-premises infrastructure

If your organization uses on-premises storage in any capacity, carefully plan in-house infrastructure to utilize storage resources efficiently. Storage and data networks should be able to efficiently support workloads and storage infrastructure. This applies not only to the network hardware, but also to the software and protocols that facilitate connectivity to and from the storage devices.

Ensure that your tiering strategy incorporates any on-premises storage. Some strategies tier data based on disk type, considering factors such as access frequency, performance requirements or the data’s importance. For example, you might adopt the following strategy:

• Hot data. This type of data supports mission-critical workloads. The data is stored on high-end SSDs and all-flash arrays. It often relies on NVMe or NVMe-oF to facilitate data transfers.
• Warm data. This type of data supports less critical workloads that don’t have the frequency or performance requirements of hot data. Warm data might be stored on high-capacity HDDs or midrange SSDs.
• Cold data. Backup or archival data is stored on midrange HDDs or tape storage.

This approach is just a guideline. A tiered storage approach is based on your specific requirements. For instance, you might introduce an additional tier between the hot and warm tiers that uses a hybrid array of both SSDs and HDDs or an HDD array that makes extensive use of high-performance cache. Many of today’s hybrid arrays can automatically tier data within the same storage system.

The cloud can still play an important role for certain types of data, as long as it provides the flexibility needed to maximize efficiency. If you’ve implemented a hybrid cloud or plan to implement one, your disk usage and tiering strategies should fit seamlessly into that environment and be consistent with your DLM initiative. For example, you might keep hot and warm data on-premises, but store your backup or archival data in the cloud.

3. Refine your data lifecycle management strategy

If you already have a DLM strategy in place, review and update it to meet your current needs. If you don’t, launching a DLM initiative should be a top priority.

DLM provides a structure for organizing and controlling your data. The more control an organization has over its data, the more efficiently it can store the data and accommodate changes to business requirements as they evolve.

A DLM strategy initiates specific steps during the data’s lifecycle. Some of the more common steps include the following:

• Classify data based on current business requirements, using categories such as sensitivity, importance, access frequency or performance requirements.
• Cleanse and transform data based on business and application requirements. As part of this process, identify and tag redundant, obsolete and trivial (ROT) data.
• Develop formal policies that encode the DLM strategy. The policies should address issues such as compliance, security, access control, disaster recovery, data loss prevention, retention, archiving and data deletion.
• Implement processes for automating policy enforcement, as well as for backing up and snapshotting the data.

With an effective DLM strategy in place, IT teams can better plan the types of storage assets they need and the most effective ways to ensure the data’s availability and security.

4. Employ data reduction techniques

Data reduction techniques help improve resource utilization. These techniques are often incorporated into the organization’s DLM initiative. Three of the most common data reduction techniques are compression, deduplication and thin provisioning.

• Data compression. Compression reduces the number of bits used to represent data, resulting in smaller files. Compressed data requires less storage space and network bandwidth and speeds up file transfers. The two main types of compression are lossless and lossy. With lossless compression, the file can be restored to its original state, which is important for files such as executables. With lossy compression, unimportant bits are permanently deleted, which is generally acceptable for files such as audio or video files.
• Data deduplication. Deduplication eliminates redundant copies of data, usually at the block or file level. Deduplicated data requires less storage space, can reduce network bandwidth and can speed up data transfers -- depending on whether the data is deduplicated before transmission or after. There are two primary types of deduplication: inline and post-processing. Inline deduplication removes redundant data as it is written to storage. This approach requires less storage than post-processing, but it can cause bottlenecks. Post-processing deduplication removes redundant data after it’s written to storage. This requires more storage space than the inline approach, but it eliminates bottlenecks and provides more flexibility for deduplicating specific data.
• Thin provisioning. This approach is a resource management technique that dynamically allocates capacity as it is needed, rather than allocating the full amount in advance. The system pools storage from multiple disks and allocates from that pool. Thin provisioning can help organizations better use storage resources and avoid over-provisioning while making it easier to introduce new applications. Thin provisioning can be especially useful when trying to accommodate the fluctuating storage needs of multiple VMs.

Many storage products now include compression, deduplication or thin provisioning capabilities built in. Carefully vet products to understand how vendors define these terms, how they measure capacity savings and how they implement these features.

5. Continuously monitor and maintain

Maintaining and improving storage efficiency is an ongoing process that requires continuous reassessment and refinement. Consider the following guidelines:

• Continuously monitor storage systems to identify bottlenecks and anomalies, as well as storage efficiency. This information can help forecast future requirements.
• Use key performance indicators and alerting capabilities to maintain continuous oversight of storage systems.
• Perform routine maintenance and upgrades to ensure the ongoing efficiency of storage systems. Regularly identify and clean up ROT data.
• Use advanced technologies such as AI and machine learning to manage and optimize storage systems. For example, you might use predictive analytics to identify potential security or performance issues early on.
• Automate storage management and monitoring tasks to allocate and optimize storage resources, as well as clean up ROT data. Incorporate tiering and DLM strategies into automation as well. You should be able to accommodate changing business requirements as they occur, without disrupting operations.
• Provide administrators with the education and training they need to understand the technology, apply best practices and use the tools available to them for performing their jobs.

Robert Sheldon is a freelance technology writer. He has written numerous books, articles and training materials on a wide range of topics, including big data, generative AI, 5D memory crystals, the dark web and the 11th dimension.