In the world of Data Center Infrastructure Management (DCIM), staying ahead of infrastructure issues is no longer optional. It’s essential. As demand for uptime, efficiency, and capacity grows, operators need more than just real-time visibility. They need foresight.
That’s exactly what Modius® OpenData® provides. Using predictive analytics, OpenData transforms traditional DCIM software into a proactive solution that helps operators anticipate problems, prevent downtime, and optimize performance across the entire facility.
Let’s explore how predictive analytics with OpenData is transforming data center management today.
What Is Predictive Analytics in DCIM?
Predictive analytics uses real-time data, trends, and statistical models to forecast future conditions. In DCIM, this means turning raw sensor data into actionable insights that can prevent equipment failure, optimize energy use, and support smarter capacity planning.
With OpenData, predictive analytics is built directly into the platform. Operators can create digital twins, which are virtual replicas of their infrastructure that constantly update based on live data. These digital models allow teams to simulate scenarios, test strategies, and stay ahead of potential risks.
Key Benefits of Predictive Analytics in Data Center Management
Predictive analytics delivers measurable improvements across your operations. Here’s how:
- Reduce Unplanned Downtime: By identifying warning signs before failure occurs, OpenData allows teams to act before an outage happens.
- Detect Equipment Drift Early: Sensor drift or component degradation can be hard to spot. Predictive tools identify these changes and alert teams in real time.
- Forecast Capacity Requirements: Tracking infrastructure usage over time helps operators know when and where resources will become constrained.
- Improve Energy Efficiency: Pinpoint where systems consume more energy than necessary and adjust accordingly.
These advantages work together to make your data center more reliable, efficient, and cost-effective.
OpenData Predictive Analytics Capabilities
OpenData supports a wide range of predictive functions. Each is designed to help operations teams detect risks earlier and make more informed decisions.
Power Quality
- Harmonic analysis and waveform monitoring help identify electrical noise.
- Grid input evaluation ensures consistent, stable power delivery.
- Detect poor power factor conditions before they affect performance.
Capacity Forecasting
- Compare design capacity, actual load, and derived metrics.
- Identify stranded capacity to maximize existing infrastructure.
- Delay capital investments through better resource allocation.
Imbalance Monitoring
- Calculate percent deviation across power phases A, B, and C.
- Trigger alarms when thresholds are exceeded.
- Prevent breaker trips caused by overloaded phases.
Loss Analysis
- Track energy loss in PDUs, UPS systems, and transformers.
- Pinpoint inefficiencies that may otherwise go unnoticed.
Temperature Monitoring
- Detect overheating in transformers and breakers.
- Spot mechanical wear or faulty components before failure.
Cable Temperature (TMS)
- Monitor cable temperature at the breaker level.
- Identify generator stress or poor contact that leads to overheating.
- Enhance safety through breaker status tracking.
Breaker Analytics
- Monitor the number of state changes over time.
- Predict wear based on manufacturer guidelines.
- Schedule preventive maintenance accurately.
Battery Health and Runtime
- Monitor cell voltage, temperature, and impedance.
- Spot outliers and replace only the failing cells.
- Calculate expected runtime based on fuel, load, and consumption data.
Generator Monitoring
- Estimate remaining runtime using fuel level and load trends.
- Trigger alerts when refueling is needed.
- Optimize refueling schedules based on actual use.
Cooling Performance
- Monitor valve positions, compressor cycling, and fan speeds.
- Detect short cycling due to dirty coils or failing hardware.
Use Case: Fixing Phase Imbalance with Computed Points
In one case, an operations team faced recurring breaker trips caused by phase imbalance. Rack PDUs weren’t evenly distributing power across A, B, and C phases, creating overloads on a single leg.
The team used OpenData to calculate percent imbalance across phases and set alarms at defined thresholds. They then visualized the data in dashboards and tracked trends over time.
Outcome: The imbalance incidents dropped significantly. The team was able to adjust the load distribution proactively based on real-time analytics.
Use Case: Estimating Generator Runtime
Another customer wanted a more accurate way to monitor generator fuel levels and predict how long generators would last during an outage.
Using existing data from the main switchboard meter and fuel monitor, OpenData created computed points that calculated runtime based on actual fuel consumption trends.
Outcome: The operations team set alarm thresholds (such as 4 hours remaining) and displayed these values on dashboards to improve planning and avoid outages.
Generator Runtime Example
| Data Source | Metric | Purpose |
| Fuel Sensor | Gallons Remaining | Measures available fuel |
| Load Meter | 5-Minute Average Load | Determines current usage rate |
| Slope Data | Manufacturer’s Consumption Curve | Calculates consumption rate |
Use Case: Monitoring Redundant Circuit Loads
Redundant power circuits provide failover protection, but only if each leg has enough capacity to handle the full load. OpenData created computed points to assess both load and voltage for each member of a redundant pair.
The system flagged conditions where combined load exceeded the safe capacity of either circuit. A best-fit line chart was also used to forecast when the load might cross critical thresholds in the future.
Outcome: The team avoided cascading failures and improved their ability to plan for redundancy and load balancing.
How It Works: Native, Computed, and Alarm Points
Predictive analytics in OpenData is built on three categories of data points:
- Native Points: Real-time data from sensors and devices (e.g., temperature, voltage, fuel level).
- Computed Points: Formulas that derive new values like phase imbalance or battery runtime.
- Alarm Points: Thresholds that trigger alerts based on either native or computed data.
These points are trended, visualized, and analyzed across time using dashboards and BI reports.
Projections and Forecasting with Best Fit Lines
OpenData analytic reports support statistical projections. For example, if capacity trends indicate that a circuit will exceed safe load levels within 30 days, a best-fit line on the report makes that future risk easy to see.
This predictive insight helps operators act before thresholds are crossed.
Moving Toward Smarter, More Resilient Operations
Reducing downtime is only part of the value of predictive analytics. The real power lies in building trust in your infrastructure. It gives your team confidence to take the right action at the right time, backed by data that is always up to date.
As OpenData continues to expand with AI and machine learning, the future of DCIM is moving toward real-time adaptation. Systems will soon be able to recommend actions, self-optimize, and respond dynamically to new conditions.
At Modius, we believe that the future is already here. We are passionate about empowering our clients to run more profitable data centers while providing unmatched visibility into operational data. Modius has been delivering DCIM solutions since 2007. We are based in San Francisco, are proudly certified for ISO/IEC 27001 and are a Veteran-Owned Small Business (VOSB). Contact us at sales@modius.com or (888) 323.0066 to learn more.
Explore how OpenData can transform your approach to DCIM. Schedule a demo or sign up for a free trial today.
