When it comes to data center monitoring, management, and visualization software tools, there’s quite a few options that are available in the market. Depending on what you’re looking for and what your goals are, one type of software may be more preferable than another. For example, CFD (computational fluid dynamics) modeling software is a great option for simulating airflow when evaluating a new build or rack configuration, while BMS (building management systems) solutions are a great option for monitoring and controlling aspects of an entire building (including lighting, ventilation, etc.). You also have EPMS (electrical power monitoring systems) and DCIM (data center infrastructure management) solutions that have carved out their own niche in the industry.

For the most part, these solutions are fairly different and can typically play in their own arenas (in some cases, they can even complement each other). What they all have in common is that they collect a ton of data. But the problem that arises with all of this data is, how do you make sense of it? With rows and rows of datasets, how do you thin slice this information to make it actionable? This is where Artificial Intelligence and Machine Learning can come into play. And while some of these solutions do use this technology to certain extent, software that is primarily based on AI and ML algorithms is the best way to make sense of all the data and make it actionable, especially when it comes to removing thermal risk and optimizing the cooling infrastructure of a data center:

To learn more about data center containment strategies and considerations, check out our recent webinar titled, Data Center Containment Considerations: Determining the Right Solution for Your Facility and How to Maximize ROI.