The idea of ‘operational Intelligence’ is everywhere at the moment.
In an industry full of buzzwords and acronyms, the important questions to ask are what does it mean and why should data center operators care?
Data centers typically rely on four core systems: BMS, DCIM, CMMS, and EPMS, which are designed to monitor, regulate, manage, and provide visibility into different functions to keep a data center operating at optimal levels. The problem is that these systems create fragmented data and blind spots when they operate in isolation. Operational intelligence bridges those gaps, giving operators a more complete picture of their internal processes and systems.
Operational intelligence can often be boiled down to simply knowing more about internal processes. But we believe there are far more opportunities for data center operators. The way we see it, operational intelligence is about understanding risks ahead of time, being proactive without compromising security, and better empowering the team operating your portfolio. Now is the right time to think about operational intelligence, as data centers become increasingly complex and the value of the systems inside them increase exponentially, meaning siloed systems have emerged that limit visibility and undermine the resilience the industry requires.
Why operational intelligence is essential
Data centers are scaling at pace, running at higher densities and placing greater demand on power and cooling; facilities are becoming more complex by the day. The truth is that operators can no longer afford to look at systems in isolation. They need complete visibility over their facilities, with a clear understanding of how it is operating in its entirety and how that influences the decisions they make.
Traditional monitoring can reveal if something has changed or equipment has failed. Operational intelligence goes beyond this, connecting data across different systems, identifying patterns and anomalies. Operators don’t need another dashboard; they need a clear picture of what requires attention, why it matters and what action should be taken.
That visibility is what maintains performance. Every small issue can multiply and develop into substantial risks. By knowing exactly how assets are performing in real time, operators don’t need to rely on fixed reporting and maintenance schedules. Instead, they can identify and react to events before they happen, helping them not only drive performance, but also optimize resources, manage costs and improve efficiency in the long run.
Using AI with confidence
Operational intelligence is non-negotiable now that AI has become increasingly involved in every operator’s decision-making process. It helps combine AI-driven insight, human judgment and the governance required to use AI with confidence. While AI is invaluable for identifying patterns and providing suggested actions, operators need to maintain accountability over every decision, by questioning whether the recommendation is appropriate and understanding the outcome.
With operational intelligence, AI becomes more than a simple tool for automation. With accountability and governance in place, AI can be an exceptional learning aid, giving less experienced teams direct insight into why something has happened, allowing them to learn alongside the innovation. Informed decisions are not made by removing people from the process. They come from giving operators the insight they need to make them.
Becoming operationally intelligent
Looking forward, operators need to connect their data, from cooling, IT infrastructure to other core systems, to give them a complete view of their facilities. Once this is achieved, advanced analytics can explain what is happening and machine learning can reveal patterns and forecast how key systems are trending.
More data is not the answer. The ultimate goal is to understand all of the data in order to make insights accessible to the people actually making decisions. The most valuable intelligence in any facility is still that of the team operating it; tools like analytics and machine learning provide the all-important efficiency boost to help teams work faster and smarter. By combining integrated and normalized data, human expertise and key insights, operators will never second-guess themselves and remain confident in their ability to adapt as data centers become even more complex.