Executive Summary
Data centers generate vast, time-stamped operational data across power distribution, cooling, and IT assets. When stored in a structured, normalized format through a DCIM solution, this data becomes ideal for machine learning. Modius OpenData AI uses that historical dataset for anomaly detection—flagging potential failures well before manual alarms trigger, protecting uptime and SLA compliance.
What is Structured Data?
Data centers produce a wealth of detailed information in their day-to-day operations. Making the best use of this data will help operators run a tight ship. One of the ways data centers can make the best use of this data is by having it in a structured data format. Structured data is a data set that is stored in a manner that allows you to group, filter and correlate that data in different ways. An important aspect of structured data that maximizes its value is normalization of values and format. For time-series data − such as real-time collected values − the time markers should also be normalized (for example, GMT), not localized, so the time stamps are uniform. Data accuracy here is critical; obviously, well-structured but inaccurate data is not valuable, as it translates to inaccurate analysis. This large amount of data includes details from top level power distribution gear like utility transformers and MVS (Medium Voltage Switchgear), power distribution like PDUs, RPPs, UPSs, as well as resource utilization, and environmental factors such as temperatures of rooms and racks. For some deployment this data even includes servers or CPUs.The Value of Structured Data
Structured data is highly useful for several reasons, and its advantages include:- Interoperability and ease of analysis
- Ease of storage and retrieval
- Data consistency
- Data validation and integrity
- Reporting and visualization
DCIM Should Provide a Structured Data Set for Your Infrastructure
DCIM collects a significant amount of data, which by its nature should be stored in a well-structured format. This data is often used to analyze the infrastructure, historical events, and to drive real-time alarm notifications. These requirements lead to a need for high quality of data, normalization, as well as accurate time stamping of the value.DCIM Data is Ready-Made for Machine Learning
With a large data set, high accuracy, and time stamp tracking, the historical data in a DCIM is very well suited for a “tidy” data set essential for machine learning. The historical data set can be used for model building and training − the basis of machine learning. The larger the data set, the more accurate the models and results, thus the very large data set of a DCIM solution can produce high quality machine learning output.Modius® OpenData® AI for anomaly detection
Modius has released its first phase of machine learning for OpenData AI– anomaly detection. This module can create user defined models, with user selected devices, points, and time periods. OpenData AI can “self-train” from the historical data captured in OpenData. This allows the machine learning module to begin analyzing real-time almost immediately upon deployment. Anomaly detection can determine when real-time data does not align with the thousand or tens of thousands of past scenarios and determine when new conditions indicate a potential problem − well before a human could detect the same issue − and even before point values trigger manually defined alarms. In high uptime data center operations, machine learning brings awareness of potential issues before they become problematic, giving you an edge, and helping ensure optimal performance and protecting critical SLA agreements. OpenData AI also supports predictive and condition-based maintenance programs.Frequently Asked Questions
What is structured data in a data center?
Answer: Structured data in a data center is operational telemetry—power, environmental, and utilization metrics—stored in a normalized, consistently formatted dataset with uniform timestamps. This makes the data groupable, filterable, and ready for analysis or machine learning. A DCIM solution creates and maintains this structured dataset automatically.
Why is structured data important for machine learning in data centers?
Answer: Machine learning models require “tidy” datasets: high-accuracy, consistently formatted records with reliable timestamps. DCIM-generated structured data meets all of these requirements. The larger the historical dataset, the more accurate the resulting ML models—making a long-running DCIM deployment particularly valuable for ML applications.
How does Modius OpenData AI use machine learning?
Answer: Modius OpenData AI uses historical DCIM data to train anomaly detection models. The module self-trains from captured data, then analyzes real-time readings against thousands of historical scenarios. When conditions deviate from established patterns, OpenData AI flags the anomaly—often before point-value thresholds would trigger a manual alarm.
How does ML-based anomaly detection differ from traditional DCIM alerting?
Answer: Traditional DCIM alerting relies on manually defined thresholds—a value must exceed a set point before an alarm fires. ML-based anomaly detection in OpenData AI learns normal operating patterns across many data points simultaneously, identifying subtle deviations that no individual threshold could capture. This detects emerging issues earlier and with fewer false positives than threshold-only approaches.
What data points does OpenData AI monitor for anomalies?
Answer: OpenData AI allows operators to select specific devices and data points for model training, including power distribution gear (PDUs, RPPs, UPSs), environmental sensors, and resource utilization metrics. User-defined time periods allow models to reflect actual operational patterns rather than generic baselines.
How do I evaluate a DCIM solution for machine learning readiness?
Answer: Key criteria include data normalization practices, timestamp consistency, historical data depth, and whether the vendor offers an integrated ML module. The DCIM Buyer’s Guide provides a structured framework for evaluating these capabilities across vendors.
If you are looking for a next-generation DCIM solution that can help you better understand your data center’s status and opportunities efficiencies, consider Modius® OpenData®. OpenData provides integrated tools including machine learning capability to manage the assets and performance of colocation facilities, enterprise data centers, and critical infrastructure.
OpenData is a ready-to-deploy DCIM featuring an enterprise-class architecture that scales incredibly well. In addition, OpenData gives you real-time, normalized, actionable data accessible through a single sign-on and a single pane of glass.
We are passionate about helping clients run more profitable data centers and providing operators with the best possible view into a managed facility’s data. We have been delivering DCIM solutions since 2007. We are based in San Francisco and are proudly a Veteran Owned Small Business (VOSB Certified). You can reach us at sales@modius.com or 1-(888) 323.0066.