Executive Summary
Data center infrastructure monitoring depends on consistent, normalized data — but disparate device protocols and vendor implementations make normalization a persistent challenge. Most DCIM solutions normalize data late in the pipeline, increasing the chance of errors. Modius OpenData normalizes data at the point of collection, the earliest possible step, ensuring homogenous rules, alarms, and reporting across all monitored infrastructure.
The current problem with other DCIM platforms
The world is an imperfect place, but it’s a fact of life for those of us managing critical infrastructure. When you combine that problem with the tsunami of disparate data sources involved in monitoring that infrastructure; the situation does not get easier.
The world of choices by vendors and coders results in masking the use of intended standards. Remote monitoring of data center systems is your eyes and ears, and no matter how ready you are to respond, things go quickly awry if you miss a critical event or state. Poor handling of normalization can be your weakest link. Most DCIM solutions struggle with this.
To achieve steadfast, reliable monitoring, it is imperative to normalize your monitored data. In the chain from the device, through the protocols, into your system, and into your database – the goal is to normalize the data as quickly as possible. The fewer the steps before normalization occurs, the less chance of error. Ideally, the data is normalized at the first step in your monitoring system when it is retrieved from the device.
The OpenData approach
The Collector component can normalize data as soon as it is returned by the base protocol. Scaling and offsets can be factored in as the raw data arrives. Ensuring you follow standards, like converting all power data to kW, lets you maintain a homogenous set of rules and alarms for handling state monitoring and reporting. With rules like alarms and reports all driven by normalized data, you avoid mistakes that bleed into your infrastructure over time as your system evolves and grows.
\We know how to do this and are happy to assist you with your project. You can reach us at sales@modius.com to see how we can help bring a bit of “sanity” to the data from your critical infrastructure — whether that be a captive data center, co-lo, telecom networks, or distributed assets located in colo’s or edge data centers
Frequently Asked Questions
What is data normalization in DCIM and why does it matter?
Answer: Data normalization in DCIM is the process of converting raw device telemetry into a consistent, standardized format so that monitoring rules, alarms, and reports can be applied uniformly. It matters because devices from different vendors — and even different firmware versions of the same device — often represent the same measurements in different units or scales. Without normalization, monitoring gaps and false alarms become inevitable as infrastructure scales.
Where in the monitoring pipeline should data normalization occur?
Answer: Normalization should occur as early as possible in the pipeline — ideally at the point of data collection from the device. The further downstream normalization happens, the more opportunities there are for inconsistent raw data to cause errors. Modius OpenData‘s Collector component applies normalization at the moment data is retrieved from the device, which is the earliest possible intervention point.
How do most DCIM solutions handle normalization compared to Modius OpenData?
Answer: Most DCIM solutions apply normalization later in the data pipeline — during processing or at the database layer — which increases the risk that inconsistent raw data propagates into alarms and reports before it is corrected. Modius OpenData normalizes at the Collector level, the first step in the chain, so all downstream rules and reporting work from a consistent, standardized data set from day one.
What types of infrastructure can Modius OpenData normalize data from?
Answer: Modius OpenData handles data normalization across all major infrastructure types, including captive data centers, co-location facilities, telecom networks, and distributed edge data centers. The platform supports Modbus, SNMP, BACnet, and other standard protocols, normalizing data from any device that communicates via those protocols.
What happens if normalization is not handled correctly in a growing data center?
Answer: Poor normalization creates compounding errors. Inconsistent unit standards mean alarms may trigger incorrectly or fail to trigger when they should. As infrastructure grows and more device types are added, the problem worsens. With rules and reports driven by inconsistent raw data, monitoring accuracy degrades over time — making it increasingly difficult to trust what your monitoring system is telling you.