How To Monitor Data Storage Systems: Metrics, Tools, & Best Practices

Imagine this: you walk into the office on Monday morning, coffee in hand, ready to tackle the day. Your systems dashboard looks clear…but an hour later, users start complaining that an application is crawling. A quick check reveals the culprit: your storage is nearly full, and a critical process has stalled.

It’s a familiar scene for IT and DevOps teams: a minor capacity or latency issue snowballs into an outage that impacts user experience, revenue, and trust. In fact, a 2023 Uptime Institute report found that 44% of data center outages stem from power and IT system failures, with storage issues ranking among the most disruptive.

The truth is storage rarely fails all at once. It sends signals — but most teams aren’t equipped to see them in time. With the right monitoring, you have visibility into early warnings before they escalate into costly downtime and unhappy customers

That’s where data storage monitoring comes in. In this guide, you’ll learn exactly how to monitor data storage systems effectively — from defining key metrics to setting up alerts and dashboards that prevent costly outages.

What is data storage monitoring?

Data storage monitoring is the ongoing process of tracking, analyzing, and managing how your data storage systems perform. It includes managing capacity, performance, availability, and health metrics. Monitoring your storage is about more than just preventing disasters. It’s about creating visibility to see how your data behaves.

In the past, this meant watching disk space on a few local servers. Today, it means orchestrating a mix of storage systems (SAN, NAS, DAS, and cloud) that together support massive, distributed, and data-hungry workloads.

Modern monitoring tools surface trends, predict capacity issues, and even automate routine maintenance, keeping systems stable as data volumes skyrocket.

(Related reading: monitoring vs. observability: what’s the difference?)

Benefits and why storage monitoring matters

Data powers every modern business function — analytics, AI, customer applications, security, observability, backups, and more. If your storage layer underperforms, everything built on top of it feels the pain.

Most enterprises manage petabytes of data (across hybrid clouds, remote offices, edge locations, etc.) which introduces operational risk. Effective storage monitoring addresses all of this. It allows teams to:

• Prevent outages by detecting early signs of drive degradation or I/O bottlenecks.
• Optimize performance by redistributing workloads and reducing latency.
• Manage capacity to delay expensive expansions or optimize cloud storage tiers.
• Maintain compliance through continuous visibility into access and permissions.

How storage monitoring works: key components

Modern data storage monitoring covers multiple layers, from hardware and firmware to application-level access. These components form the foundation of effective visibility and reliability.

Capacity monitoring tracks used versus available storage and provides trend data to forecast when capacity thresholds will be reached. Predictive models can highlight growth patterns and help plan expansion or tiering before it becomes critical.

Performance monitoring measures how efficiently data moves through the system, focusing on throughput, latency, IOPS, and bandwidth utilization. These indicators reveal bottlenecks, overloaded volumes, and underperforming disks that can affect user experience.

Availability and health monitoring checks the status of physical and logical components: disks, RAID arrays, controllers, and file systems. Health metrics provide early warning before minor errors cascade into failures.

Storage utilization and efficiency looks beyond capacity to measure how effectively space is used. Tracking deduplication ratios, compression, and snapshot growth ensures full value from existing infrastructure.

Security and access monitoring monitors access activity, permission changes, and suspicious read/write patterns. In an era of data exfiltration and insider threats, visibility into who’s accessing what is non-negotiable.

Core metrics for storage performance monitoring

Once you understand the key components, the next step is to measure them with meaningful metrics. Metrics translate those components into actionable insight.

• Capacity utilization. Track how much storage space is used vs. available, and how quickly it’s being consumed. This will prevent sudden outages and support practical capacity planning.
• Input/output operations per second (IOPS). Measuring the number of read and write operations that occur each second. This indicates whether storage can handle current workloads or if bottlenecks are developing.
• Bandwidth (throughput). Shows amounts of data transferred over time. This is critical for apps that rely on large or sequential data transfers.
• Latency. Captures the time it takes to complete storage operations. High latency leads to slower applications and degraded user experience.
• Error rates. Counts failed reads, writes, or retries. This gives early warning of hardware failures or misconfigurations and it helps prevent data loss.
• Data integrity. Confirms that data retrieved matches what was written, via checksums and validation routines. This protects against silent corruption and ensures ongoing trust in retrieved information.
• Storage costs. Monitors spending on storage resources, especially for cloud environments. (This may be monitoring per-GB or per-operation.) This supports cost optimization and resource allocation—right-sizing, deleting unused volumes, optimizing hot vs cold placement, etc.

Other metrics may be crucial depending on your organization, industry, team, challenges, and goals. Start with your overall goals for data storage, and choose the metrics that directly reflect the performance, reliability, and efficiency metrics you care about most.

Common storage architectures (and what to watch)

Different infrastructures will need different monitoring setups. For example:

• On-premises systems will need tools that can track disk health, SMART data, and NAS.
• On the other hand, cloud-native tools are vital for integrating directly with services such as S3, EBS, or Blob Storage.

Network-attached storage (NAS)

NAS offers shared, file-level storage that’s accessible over a network. Ideal for team collaboration or content repositories, but prone to congestion under heavy read/write traffic.

What to monitor: queue depth, read/write latency, network throughput.

Storage area network (SAN)

SANs deliver block-level storage used for databases and critical applications that demand high performance and redundancy.

What to monitor: I/O latency, port congestion, HBA performance, and LUN utilization.

Direct-attached storage (DAS)

DAS connects directly to a server or workstation, making it fast and simple but hard to scale.

What to monitor: individual drive health, temperature, and local I/O patterns.

Cloud and object storage

Cloud storage is scalable and elastic, but introduces new dimensions — like API reliability and data egress costs.

What to monitor: request rates, retrieval latency, object counts, and storage class transitions.

Hybrid and multi-cloud systems can correlate these metrics (and many other data points) for unified observability. For example, Splunk Observability Cloud bridges gaps, combining metrics, logs, and traces across diverse environments for a single view of performance and reliability. The goal is not to mix too many tools early on. Instead, start with a core system and integrate others as needed.

(Related reading: infrastructure monitoring, explained.)

The process: How to monitor data storage systems

Now that you understand what to monitor, how exactly should you set up the necessary tools? Here are the steps to choosing and setting up the right system for you:

Step 1: Establish baselines

Start by defining “normal.” Understand typical IOPS, latency, and capacity growth under healthy conditions. Baselines are your compass for identifying real anomalies.

Step 2: Identify visibility goals and key metrics

The metrics and the monitoring tools you choose should align with your overall visibility goals. Once you’ve decided which metrics are vital to your team and organization, ask yourself:

• Do you need real-time alerts, long-term trend analysis, or both?
• What is your data environment: on-prem, cloud, or hybrid?
• Who needs the data? Will more than one team need access, such as DevOps, IT, or Finance? (For instance, infrastructure teams may focus on throughput and hardware errors, while FinOps teams care more about cost per GB stored.)

When you clearly understand your needs and goals, you can avoid wasting time on overly complex or incomplete setups. As systems grow more complicated, focus becomes essential — know which signals matter most and why.

Step 3: Focus your monitoring

It’s tempting to track every metric, but more isn’t always better. Too many alerts create noise, overwhelm dashboards, and lead to alert fatigue — where even critical warnings get missed.
Instead, prioritize metrics that are actionable and business-critical:

1. Start with systems that directly impact uptime or user experience.
2. Add performance optimization metrics that improve efficiency.
3. Finally, include cost and resource metrics to fine-tune over time.

A focused monitoring approach ensures clarity, reduces noise, and keeps teams responsive to what truly matters.

Check out this article on how to tier your observability >

Step 4: Collect, correlate, and analyze data with appropriate tools

Once you know what to monitor, the next step is gathering and interpreting the data that brings those metrics to life. Collect telemetry from drives, arrays, hypervisors, and cloud APIs — then correlate events to see how patterns align.

For example, recurring latency spikes might coincide with nightly backups or replication jobs. Machine learning and anomaly detection can highlight these trends automatically, helping you act before users notice performance issues.

Tools like Splunk Observability Cloud can ingest and unify this data across hybrid environments, giving you full-stack visibility and context for smarter, faster troubleshooting.

Step 5. Integrate, automate, and alert smartly

The best monitoring setups fit naturally into your existing workflows. Look for monitoring tools that:

• Integrate with your incident management or ticketing systems.
• Automate data collection and alert routing to avoid manual configuration overload.
• Support tagging and labeling so you can group metrics by region, environment, or application.

Set intelligent alerts that drive action — for example, “disk at 85% capacity” gives a clear directive. Tier alerts by severity (info, warning, critical) and test thresholds regularly to prevent alert fatigue. Finally, automate routine responses like resizing volumes or initiating replication, so your team can focus on prevention, not reaction.

Related articles: using metadata for monitoring and observability and setting adaptive thresholds.

Step 6. Create clear dashboards and visuals

Collecting metrics on their own won’t improve or protect your data storage. Instead, focus on making them actionable. A clean dashboard will save time and the headache of troubleshooting when something goes wrong.

Step 7. Plan for growth and maintenance

Just as your team and organization will grow and evolve, your monitoring setup will need to change. Take steps now to ensure your setup can continue to grow with your needs:

• Document configurations and thresholds.
• Review and refine metrics regularly.
• Build flexibility for scaling storage or migrating environments.

How data storage monitoring supports observability

Storage is one layer in a much larger digital ecosystem. When a service slows down, the root cause might be a blocked process, a bandwidth bottleneck, or a database issue — not necessarily the storage device itself.

By integrating storage monitoring into end-to-end observability, teams can connect the dots between infrastructure, network, and application data. Logs, metrics, and traces combine to reveal the why behind every performance issue. That holistic view reduces mean time to resolution (MTTR), eliminates blame between teams, and helps prioritize fixes that matter most to the business.

Splunk excels at this, mapping storage telemetry directly to service-level objectives so you can troubleshoot in context, not in isolation.

Common challenges (and how to overcome them)

Monitoring storage sounds simple until you try to do it across multiple systems, vendors, and clouds. Teams often encounter these pain points:

• Modern complexity = fragmented visibility: Juggling multiple monitoring dashboards leads to blind spots. Standardize on a unified platform that normalizes data across environments, from on-prem to containers.
• Reactive vs. proactive monitoring: Threshold-based alerts catch symptoms, not causes. Predictive analytics lets you act before performance drops — for example, forecasting when I/O will exceed safe limits.
• Runaway data growth: Storage growth outpaces budgets. Use automated tiering, data lifecycle policies, and deduplication to rein in unnecessary costs.
• Talent and process gaps: Storage expertise is scarce. Document workflows and cross-train teams so insights lead to timely, coordinated responses instead of alerts languishing in queues.

The future of data storage monitoring

The next phase of storage monitoring is intelligent, automated, and deeply integrated with the broader observability stack.

• AI-driven anomaly detection that spots subtle issues before users feel them.
• Predictive capacity planning using machine learning to model growth and recommend actions.
• FinOps integration, dynamically optimizing cost across cloud storage classes.
• Deeper insight into ephemeral storage used by containers, VMs, and serverless functions.
• Sustainability tracking, helping organizations measure energy consumption and carbon footprint across data centers.
• Edge and IoT monitoring, as critical data processing shifts closer to the user.

As environments become more distributed and automated, modern observability is about building resilience across the enterprise — making visibility the foundation for both performance and innovation.

FAQs about Monitoring Data Storage Systems