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Finding the low hanging fruit through data center operational maturity levels

A maturity model, typically applied to software, can be used to visualize where data center operators are in their operational journey.

A maturity model, typically applied to software, can be used to visualize where data center operators are in their journey toward more mature operational processes. This can be utilized by data center physical infrastructure vendors to optimize their go-to-market (GTM) strategy and offer more tailored products and solutions.

Actual data center operations often differ from how they were conceived

As an experienced data center industry practitioner, I have witnessed that data center operations often deviate from the conceived design. To better understand the journey of a data center operator, the following maturity model (Figure 1) can be used to describe the evolutionary path of process maturity. This concept was originally tailored to software, but it can be and has been used to analyze any service or industry. By using the maturity model, data center operators can visualize the maturity level of their operations and how to move toward their desired goals. Numerous facilities can be found for each maturity level across the data center industry.

Figure 1: Data center operational maturity model 

Figure 1_ Data center operational maturity model.png

Source: Omdia

Tailoring the GTM strategy to operational maturity levels

By understanding a data center’s operation maturity levels, vendors can optimize their GTM strategy. Figure 2 shows a simplified matrix of power and cooling equipment characteristics matched to data center operational maturity levels. This summary is a simplified view that each vendor may adapt to their specific products or solutions.

Data centers that are at the initial stages of setting up thorough operational processes are unlikely to maximize the benefits of advanced equipment features and bleeding edge technologies. Such data centers often rely on human glue to ensure they are running smoothly and would not be able to maximize the value of purchasing top performing equipment with automation and interoperability features. They are likely prioritizing price when making their purchasing decisions.

Conversely, data centers with highly mature and integrated operational processes would be good targets for new technologies, advanced analytics, monitoring, and optimization tools. Omdia believes they are more likely to prioritize performance over price when making purchasing decisions.

Figure 2: Decision matrix for products/solutions based on data center operational maturity levels 


Source: Omdia

Below is a brief explanation of the 10 characteristics considered:

  • Equipment price: Ranges from low to high.
  • Equipment performance: Good or excellent; measured through different key indicators.
  • Monitoring capabilities: Basic or advanced; based on the number of parameters, metrics, and visualization tools.
  • Real-time data collection capabilities: Enabled through the equipment itself and/or sensing devices.
  • Connectivity to the data center infrastructure management (DCIM) systems: Basic or advanced (real-time data collection).
  • Analytics tools: Basic, advanced (predictive analytics), or highly advanced (actionable recommendations). In advanced or highly advanced, artificial intelligence (AI)-enabled analytics tools contribute to improving the decision-making process, supporting end-to-end resource management.
  • New technologies: The consideration of technologies with improved performance are highly encouraged. Examples include highly efficient and smaller footprint equipment, lithium-based batteries, grid-interactive uninterruptible power supplies (UPS), and liquid cooling.
  • Sustainability considerations: Environmental profiles are considered to measure the impact throughout the life of the equipment.
  • Integration with existing systems: Equipment needs to be adequately integrated when considering a comprehensive approach.
  • Optimization tools: Data center operations are continuously optimized, establishing an integrated approach toward performance improvement and risk mitigation. Data is collected to be leveraged by data science and AI-enabled tools to improve data center behavior, anticipate potential complications, and detect anomalies or vulnerabilities.

The data center industry is adopting new technologies and processes to improve the design, construction, and operations of data centers while being more sustainable. Understanding the operational maturity levels of a data center can help the operator and their vendors target the low hanging fruit.

Moises Levy, PhD, leads the data center physical infrastructure, thermal management, and sustainability research practice. A seasoned engineer and data center subject matter expert, Moises capitalizes on his knowledge to analyze relevant market data and produce reports that add value for Omdia’s clients.

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