You’re probably familiar with the traditional FM maintenance approach, in which equipment servicing, upgrades and replacement schedules are dictated by equipment manufacturers or corporate standards. However, the calendar-based approach ultimately adds to capital expenses without necessarily supporting reliability.

Equipment performance deteriorates according to the level of usage and environmental conditions, not according to a calendar. For example, the manufacturer may recommend replacing your new HVAC unit at 15 years. However, your facility is in an area with temperature extremes that stress the HVAC system and it fails in year 12. Your data center begins to overheat and operations must be temporarily shut down while your FM scrambles to replace the HVAC quickly. Installation takes place over the weekend, incurring overtime charges, with little consideration for the lifecycle costs of the new unit.

A better approach is to use sophisticated facility technologies that combine sensor data with predictive modeling, analytics and artificial intelligence (AI)-driven machine learning to analyze building equipment performance. These tools enable building engineers to detect performance anomalies not visible by human perception alone.

With these data-driven early indicators, your FM team would recognize that the HVAC required maintenance long before indicated by the manufacturer’s schedule. As a result, your building engineers would be able to remediate the performance issues early, avoiding premature equipment replacement and—most important—costly downtime that could damage your data center’s reputation.

Also important, intrusive contact with building equipment itself creates risk. Any human interaction with a piece of equipment introduces the possibility of an error, such as an incorrect procedure or a misplaced tool.

In critical environment maintenance, even a simple mistake can lead to costly downtime—and most errors are human-made. That’s why forward-looking data centers are investing in FM tools that help remove the human element. Today’s advanced FM software minimizes human error by automating data center operations and centralizing and standardizing information.

For example, a data center FM operations platform like MCIM—a leading solution for mission-critical FM operations—consolidates data and documentation into a single system. Corrigo, another popular platform, is a robust computerized maintenance management system (CMMS) that helps the FM team maximize personnel productivity while improving accuracy and reliability, and generating savings.

For example, the data might indicate that one of your data centers uses far more energy than others. Using data and analytics, your engineers can explore the issue and take targeted action to reduce energy waste rather than reactionary steps that could create additional problems.

In a busy data center, where today’s emergency is more important than tomorrow’s, lifecycle management of your assets—your building equipment and the building itself—can be an afterthought. However, creating a comprehensive asset management program is more than worth the effort because it makes your data center more cost-efficient and resilient.

The first step is to inventory your building equipment and capture accurate data about age, condition, performance and other issues. With robust data, you can determine replace-or-repair priorities and create an objective capital plan that optimizes facility performance and improves your data center TCO.

For example, your predictive analytics data indicates that your data center will need a new chiller within the next year. Rather than buying a chiller with the lowest upfront cost, you will save more by comparing chillers based on upfront cost, energy efficiency, reliability and projected lifespan. The cheapest chiller today may actually have the highest lifetime cost if it is less energy-efficient or has a poor repair record compared to a higher-priced product. Through this analysis, you can create a more accurate capital plan.