There’s no question that data centers consume vast amounts of energy. In 2025, data centers represented 5% of capacity and 5.5% of total energy usage in the U.S., roughly twice the amount from 2022.1 Those numbers are expected to double over the next 5 years. This energy consumption comes with a hefty price tag. On average, the typical data center dedicates about 40% of its operating budget to power, at a cost of around $7.4 million each year.2
Confronted with a volatile energy market and spiking costs, data center stakeholders are seeking smarter strategies for lowering energy consumption. That’s where Energy Intelligence comes in. Energy Intelligence refers to the ongoing, data-driven optimization of how energy is used, distributed, and consumed across building systems, equipment, and assets. This data is pulled from myriad sources, including Building Automation Systems (BASs), Building Management Systems (BMSs), Energy and Power Monitoring Systems (EPMS), IoT sensors, automated controls, meters, and more.
Through Energy Intelligence, data center stakeholders can extract actionable insights from massive amounts of operational data to predict, optimize, and adapt their facility’s performance, drive energy efficiencies and savings, and improve resilience. The key to true Energy Intelligence lies with analytics.
To ensure your analytics deliver actionable insight, however, there are key considerations to keep in mind when selecting and deploying the right platform. Here are five steps to ensure your analytics solution turns raw data into real Energy Intelligence that drives efficiency and cost savings.
1. Unify, Normalize, and Standardize Your Data
Data centers generate massive amounts of data from a wide range of equipment made by different manufacturers, as well as BMS, SCADA, and EPMS platforms, all run by different teams. This leads to data silos that can result in inaccurate information, inefficient workflows, and missed opportunities. Unifying this disparate data is essential to building an effective analytics strategy. In fact, studies show that unifying data can result in up to 30% reduction in energy usage by consolidating workflows.3
Your analytics strategy should work to normalize data so that it can scale and adjust diverse datasets (such as energy usage, temperature readings, and occupancy metrics) into a common range or distribution. This ensures fair comparisons and accurate modeling.
Data should additionally be standardized into a uniform format, ensuring consistent units, naming conventions, and structures. This ensures you’re basing decisions on a single source of operational truth, dramatically minimizing data errors. Standardization also supports enhanced modularity, repeatability, and scalability, making it easier to leverage data across an entire portfolio.
2. Filter Your Data
When it comes to actionable analytics, the quality of the data is more important than volume. As such, an effective analytics platform employs screening frameworks to prioritize and filter vast datasets to pinpoint variables with the biggest impact on energy usage so you can act on those opportunities quickly.
Frameworks are also useful in analyzing sensor data from servers, cooling systems, and power units to predict failures before they happen. By processing real-time metrics like PUE and temperature trends, frameworks pinpoint inefficiencies in energy or cooling, help improve resource allocation, and extend asset lifespans.
3. Capture Time-Series Data
For true Energy Intelligence, analytics should serve up both real-time and historical data, or time-series data, that allows you to respond quickly to issues while also revealing long-term trends. Historical data can show you where energy peaks or equipment degradation impact energy usage, address chronic issues, as well as predict future needs. So rather than reactively fixing problems as they pop up, your facilities team can proactively and continually fine-tune systems for sustained energy efficiency.
4. Finding an Analytics Expert
Choosing, implementing, and managing an analytics platform takes resources you may not have to spare. That’s when partnering with an outside analytics expert makes sense. When selecting a partner, here are a few must-haves to look for:
- Proven expertise in energy management, building systems, OT and IT, and analytics.
- Understanding of how to clean, normalize, standardize, and unify data from disparate systems.
- Assistance in setting up frameworks to get the right data into the right hands, in the right format.
- Access to time-series data (real-time and historical).
- Ability to scale your analytics across your portfolio.
- Open solutions that won’t lock you into a specific platform.
- Robust security protocols for protection against cyberthreats.
- Competitive SLAs with uptime agreements, prompt issue resolution, data accuracy guarantees, and remediation for breaches and data loss.
- A collaborative approach with ongoing communications, transparency, training resources, and regular reviews.
Increase Your Energy Intelligence
To increase energy efficiency and reduce energy costs, data center stakeholders must embrace a proactive and predictive analytics strategy that drives better Energy Intelligence. The good news is you don’t have to go it alone. Reach out to Albireo Energy today to put your analytics strategy in place, and start making smarter, faster data-driven decisions to improve your performance.
Sources
1. Venditti, Bruno. “Charted: The Energy Demand of U.S. Data Centers (2023-2030P).” Visual Capitalist. May 27, 2025.
2. Saul, John et al. “AI Data Centers Are Sending Power Bills Soaring.” Bloomberg. September 29, 2025.
3. Essick, Kristi. “The cost of compute: A $7 trillion race to scale data centers.” McKinsey & Company. April 28, 2025.
