Prescriptive Maintenance in Your Data Center: Three Use Cases That Show Why It’s Time to Adopt

Predictive maintenance is becoming standard across modern data centers, yet the next step in operational intelligence is already here. Prescriptive maintenance goes further by providing direct, actionable insights that improve reliability, efficiency, and resource usage. It delivers measurable benefits and demonstrates both immediate value and long-term potential.

Why Prescriptive Maintenance Matters

Data centers are growing more complex and must meet expanding environmental and performance requirements. Identifying the root cause of inefficiencies or failures often demands more time, experience, and diagnostic skill than detecting the issues themselves. Whether the source is faulty hardware, normal wear, or dynamic system interactions, fast and accurate diagnosis is essential for reliable operations.

The industry also faces a shrinking pool of experienced professionals. Replacing decades of operational knowledge is difficult, making advanced diagnostic tools even more important.

AI has helped alleviate some of these challenges. Modern models can detect anomalies, predict equipment failures, and identify early warning signs. However, predictive maintenance stops short of telling operators why something is happening or what to do next. That is where prescriptive maintenance provides a significant advantage.

At Lucend, we have been developing and refining AI models within Gradient, our intelligence platform, to support deep prescriptive maintenance capabilities.

What Is Prescriptive Maintenance?

Predictive maintenance warns operators that a system or component may fail. Prescriptive maintenance goes further by explaining why it is failing and how to address it.

Gradient analyzes equipment condition data using advanced machine learning models that offer:

• Clear identification of symptoms
  
  
• Root-cause insights
  
  
• Recommended actions
  
  
• Expected outcomes based on different operational scenarios
  
  

Prescriptive maintenance enables proactive decision-making that improves asset longevity and overall system performance. It is not simply “repair this” or “replace that.” It can recommend adjustments to how equipment is used, helping operators extend asset life while maintaining optimal performance.

Prescriptive Maintenance in Practice

When we first began building prescriptive models in Gradient, the field was dominated by pilots and proofs of concept. Today, these models run across production environments and deliver consistent, verifiable results. The use cases below highlight real examples of prescriptive maintenance in action.

1. Filter Cleaning

One customer received a Gradient alert indicating a temperature anomaly near the buffer vessel in the drycooler chiller system. The model identified clogged filters as the likely root cause. The team initially dismissed the alert because the filters were brand new.

Gradient’s analysis showed a clear temperature difference caused by back pressure, and it also quantified the energy waste associated with the issue. Mixing hot and cold water led to unnecessary chiller operation at higher loads, and the clogged filters placed additional strain on pumps.

Upon inspection, the filters were found to be extremely dirty despite limited runtime. Cleaning them resulted in savings of 75 MWh per month.

2. Water Nozzle Maintenance

Water has become a critical resource for data centers, and many operators track WUE (Water Usage Effectiveness) to meet sustainability targets. Facilities that rely on adiabatic cooling need precise control to avoid unnecessary water use.

Gradient monitors, predicts, and recommends corrective actions for water-related issues, including clogged or damaged nozzles. Factors such as vibration, water hardness, and contaminants can degrade nozzle performance, but manual detection is slow and inconsistent.

At one facility, Gradient continuously validated the performance of each nozzle in the adiabatic cooling system and highlighted issues immediately. This enabled timely inspections and adjustments. The result was a 22 percent reduction in water consumption.

3. Beyond the Specs

Prescriptive maintenance requires evaluating equipment based on real-world conditions, not just vendor specifications. Many variables influence actual performance. Some are dynamic, such as weather. Others are static but non-obvious.

For example, consider three cooling units placed side by side on a roof. The two outer units may perform according to specifications because they receive unobstructed airflow. The middle unit, however, may receive warmer exhaust air from its neighbors and perform differently. Gradient models incorporate all these contextual factors to predict true energy consumption and identify inefficiencies that would otherwise remain hidden.

By analyzing assets in their actual operating environment, engineers can focus on what is really happening rather than what should happen in theory. This improves accuracy and increases team productivity.

Prescriptive Maintenance Is Ready for Deployment

As data centers grow more complex and skilled staff become harder to find, now is the time to adopt AI-driven support that strengthens human expertise. Prescriptive maintenance surpasses predictive maintenance by reducing troubleshooting time, improving resource usage, and enabling more reliable operations.

Importantly, prescriptive models have proven themselves in the field. Our engineers see successful deployments and measurable improvements every day.

For more information on how prescriptive maintenance through Gradient can make your data center more reliable and sustainable, contact our team at hello@getlucend.com. We are always available to discuss your challenges and how Lucend can support your operational goals.