Today, nearly every piece of equipment in a data center has at least one sensor attached to it, and most every facility manager is aware that monitoring systems have the autonomy to send an alert in the event of any abnormality, such as a simple limit breach.

However, instead of empowering operators with the confidence to prevent incidents and outages, monitoring systems may actually create a false sense of security. The operations team can get locked into a passive, reactive role, being assured that the monitoring system(s) will alert them if there is an issue. Unfortunately, by the time the equipment does signal an operator, it is usually far too late to take any proactive action - the damage is already done and the staff is forced into reaction mode, or worse, emergency maintenance.

To avoid such unnecessary costs, facility managers must change their position from reactive to proactive in order to prevent failures before they become an issue. Fortunately, with nearly every piece of equipment connected to a sensor, the opportunity to do so is available.



Increasing the picture of awareness

Today, sensor data is typically used to alert an operator when there is an issue. Much less frequently, the gathered data is stored and reviewed throughout the asset’s lifecycle. Looking at performance metrics over a large span of time can reveal insights in how different variables can impact the performance and lifespan of an asset. By adding more data points collected from other identical equipment, abnormal performance patterns will be highlighted more clearly and factually, reducing the need for assumptions.

The more data considered overall, the higher the understanding of various factors’ impact.  Environment, surrounding equipment, usage, and maintenance activities all potentially influence performance and lifespan of assets. Having sufficient observations and data can help distinguish these impacts and identify outlier circumstances that may be helpful in fostering proactive approaches and predictive modeling.



Data is meaningless without context

Data is not knowledge in itself and must be transformed into useful information in order to provide any insight. Yet, making sense of large amounts of data from many different sources requires evaluating how current data relates to past data and also other sources of established information. Such context in data analytics enables the development of trends and a deeper grasp of the impacts of the various metrics. Once current and established data, and the relationship between them, are clearly understood, a predictive model can be defined that takes into account all relevant information. This model will generate more complete information that can enable better decisions.

Leverage experts using proven models

Because most monitoring systems are not yet intelligent enough to analyze the information collected, experts must interpret it all in order to extract insight. Failing to consider enough variables or selecting the wrong variable can lead to incorrect decisions and increased business risk. Proper analysis requires specific skillsets that are rarely best performed by data center operators, as they don’t have the time or expertise necessary to do so. As a result, companies are either considering or have already implemented outsourced asset monitoring and management. These focused experts can provide more accurate forecasts and steps to help plan and manage asset useful life. By partnering with specialized providers, companies have devoted experts maintaining their assets and can focus their internal resources on business priorities.

Get ahead of the curve

Predictive analytics enables organizations to gain deeper insight into their operations and to function more efficiently. It’s no longer enough to just look and see if a measurement line has been crossed and react to it.  Instead, the data can be used to predict an issue in advance. The proliferation of sensors in the data center provides the opportunity to realize a greater awareness into how assets are performing, and predictive models can demonstrate how they will perform in the future. However, in order to create and maintain a predictive analytics platform, organizations must have the right people, processes and technologies.

Integrating a predictive strategy into operations can most effectively be accomplished by partnering with an organization focused on predictive analytics with expert resources to accurately interpret data and guide asset management efforts.