People often refer to data centers as “the backbone” of modern enterprises.
But, when you really think about it, they’re more akin to the spinal cord — a complex network of nerves and tissues that carry messages from the brain to the body, enabling movement, autonomic functions, sensations, and reflexes. Humans rely on their central nervous systems in the same way businesses rely on data centers: to function.

Data centers have evolved from their “backbone” days of simple server farms into highly sophisticated mission-critical facilities — the nerve centers of the digital world. They store and process vast amounts of data, provide critical computing infrastructure, and maintain the continuous availability of essential services that both organizations and individuals rely on every day. And, the key to ensuring these operations run securely, efficiently, and without disruption lies in achieving and maintaining operational readiness at every level. That means being prepared for everything, including routine operations and unexpected interruptions. But, that goes beyond just keeping the lights on — it’s about aligning people, processes, technology, and governance to optimize resources, anticipate issues before they arise, and respond quickly when necessary.
Preparing data centers to operate at peak performance requires strategic planning, continuous monitoring, and ongoing improvements. In this article, we’ll explore the top 10 best practices to optimize operational readiness with a combination of people, operate, design and automation strategies. The conclusion of the article highlights how Salute is adding value and making the change through its Salute Technology Enabled Platform (STEP) AI program.
1. Design and Infrastructure: Establish clear capacity planning
Capacity planning is the foundation of operational readiness. It involves forecasting the future needs of your data center, including computing power, storage, network bandwidth, and cooling requirements. By anticipating demand, you can ensure your infrastructure is adequately sized to meet future growth while avoiding overprovisioning, which can lead to unnecessary costs.
Proactive capacity management allows you to scale systems in response to demand fluctuations, avoiding performance bottlenecks. There are some excellent AI-driven predictive tools on the market that can enhance capacity planning by forecasting energy and cooling demands based on historical data and AI workload patterns, ensuring that resources are available when needed. Using AI insights to regularly review and update to your capacity plans allows infrastructure to evolve alongside your business needs.
2. Operate: Implement robust redundancy and high availability
Redundancy is crucial to maintaining uptime and operational continuity, particularly in the face of failures or unexpected disruptions. This means duplicating critical systems and components so that if one fails, the other can take over without interrupting service. Key areas for redundancy include power systems, network infrastructure, and cooling mechanisms. For example, dual UPS systems and backup generators enable continuous power supply, while redundant networking equipment and pathways provide resilience against network failures. Backup cooling systems, such as additional air conditioning units or water-cooling solutions, are also necessary to prevent overheating.
AI-based predictive analytics and real-time diagnostics ensure that redundancy systems are continuously optimized, providing better foresight into potential points of failure and enabling proactive interventions before disruptions occur. AI tools help identify which components are at risk, allowing teams to focus maintenance efforts on the most vulnerable systems.
3. Energy Consumption: Prioritize energy efficiency
As data centers are some of the most energy-intensive facilities, prioritizing energy efficiency is essential for reducing operational costs and minimizing environmental impact. This can be achieved through a variety of strategies, such as adopting the appropriate energy-efficient hardware, optimizing cooling systems depending on geographic location and density of facility, and exploring renewable energy sources. Using low-power servers and networking equipment can reduce energy consumption while still meeting performance requirements. AI can play a key role in energy efficiency by predicting energy demand fluctuations in real-time, allowing for fast-response energy systems that adjust to changing workloads and environmental conditions. Additionally, sourcing energy from renewable sources, such as solar or wind, can further reduce a data center’s carbon footprint. Not only do these strategies help cut costs, but they also contribute to sustainability goals.
4. Operate and Manage: Leverage automation for operational consistency
Automation is a powerful tool for maintaining consistency and efficiency in data center operations. By automating routine tasks, such as system health checks, software updates, and backups, you can reduce human error and ensure that these critical functions are performed consistently. Automation also plays a key role in disaster recovery, as automated failover systems can switch to backup resources in real-time, minimizing downtime and accelerating recovery. Beyond operational efficiency, automation can incorporate AI-driven systems to monitor infrastructure health, predict failures, and initiate corrective actions before they escalate into significant problems. This helps improve operational resilience by enabling proactive management and reducing the need for manual intervention.
5. Develop a comprehensive disaster recovery and business continuity plan
Disaster recovery and business continuity planning are critical in the event of an unexpected disruption, such as a power failure, cyberattack, or natural disaster. From the outset of retrofitting or designing a new AI ready facility, a solid plan should include strategies for geographical redundancy, meaning that critical data is backed up across multiple locations to protect against regional disasters. Automated failover systems can switch operations to backup systems in real-time, allowing for a seamless transition during system outages.
AI enhances disaster recovery planning by predicting where failures may occur and optimizing the failover processes. AI’s ability to analyze patterns and anticipate potential issues can reduce the risk of unexpected disruptions and shorten recovery times.
6. People: Invest in employee training and skill development
The readiness of your data center is directly linked to the skills and preparedness of your workforce. Continuous training and development are necessary to ensure staff members are well-equipped to handle the challenges that arise in data center environments. Technical training ensures that staff are proficient in managing hardware, networking, and software systems, while soft skills, such as problem-solving and effective communication, are essential for handling high-pressure situations.
To prepare for AI-driven operations, data center staff must undergo continuous training in AI-specific tools and technologies. Practicing ongoing skill development through regular disaster recovery drills and certifications fosters a knowledgeable and adaptable team, which results in improved response times and reduced human error during critical incidents.
7. Utilize cloud services for enhanced flexibility
Cloud services offer scalability and flexibility, which are key to supporting dynamic workloads and future growth. By leveraging hybrid or multi-cloud environments, data centers can avoid the limits of physical infrastructure and scale resources quickly to meet fluctuating demand. Cloud services allow data centers to offload noncritical workloads to the cloud while retaining sensitive or high-performance applications on-premises. The cloud also provides advanced monitoring, security, and analytics capabilities, helping data centers stay ahead of potential issues before they impact performance. Integrating AI with cloud services can provide greater flexibility in resource management, enabling data centers to scale efficiently while maintaining operational continuity and optimizing workloads.
8. Monitor: Implement comprehensive monitoring and reporting systems
Continuous monitoring is a crucial aspect of operational readiness. By tracking power consumption, temperature, hardware health, and other system performance metrics in real time, data centers can identify potential issues before they escalate into critical failures. Centralized monitoring systems provide a holistic view of infrastructure health and performance, aggregating data from various tools and sensors to offer a comprehensive overview. AI-powered monitoring tools can analyze data in real time, detecting anomalies and predicting potential system failures before they occur. Real-time alerts can notify staff of emerging problems, allowing them to take corrective action immediately. Regular reporting helps analyze trends, track resource usage, and optimize operational efficiency. By maintaining robust monitoring and reporting systems, data centers can improve their ability to anticipate issues, enhance performance, and make data-driven decisions.
9. Security: Enhance security and compliance frameworks
Security and compliance are critical to operational readiness, especially as data centers manage sensitive business data. A strong security posture involves regular security audits to identify vulnerabilities and mitigate risks. Implementing encryption for data both at rest and in transit protects against unauthorized access and keeps sensitive information secure. In addition to these technical measures, comprehensive employee training on security best practices helps reduce the risk of human error and insider threats. Data centers must also comply with industry regulations, such as GDPR, HIPAA, and SOC 2, which require specific data protection measures. AI can be used to enhance security by identifying patterns of abnormal behavior and responding to potential threats in real time. By integrating a robust security and compliance framework, data centers can safeguard sensitive information, maintain customer trust, and meet regulatory requirements.
10. Embrace advanced technologies for proactive management
The future of data center management lies in adopting advanced technologies that enable more proactive and efficient operations. AI, machine learning (ML), and the Internet of Things (IoT) are becoming increasingly integral to operational readiness. AI and ML can predict hardware failures before they occur, allowing for timely maintenance and reducing the risk of unexpected downtime. IoT sensors can monitor environmental conditions, equipment status, and system performance in real time, providing valuable data for decision-making. These technologies also support automation, enabling data centers to optimize energy consumption, manage cooling systems, and improve overall efficiency. By embracing these emerging technologies, data centers can stay ahead of the curve, improve operational resilience, and reduce the likelihood of human error.
Conclusion
Adopting these best practices and leveraging cutting-edge technologies, like AI, results in data centers that are agile, resilient, and capable of meeting the dynamic demands of the digital age. The Salute Technology Enabled Program (STEP) AI offers a strategic approach to managing the challenges faced by data centers adapting to AI’s rapidly growing demands. As AI workloads push the boundaries of traditional infrastructure, STEP AI provides a comprehensive framework to mitigate risks, optimize operations, and enhance sustainability. By focusing on design, infrastructure, operations, and talent development, STEP AI empowers data center operators to address inefficiencies, minimize environmental impact, and meet the rising energy and cooling demands of AI-driven systems.
Our program emphasizes proactive measures, such as retrofitting legacy systems, deploying advanced cooling and energy forecasting technologies, and fostering cross-disciplinary collaboration among teams. Real-world case studies illustrate the impact of STEP AI in transforming facilities with scalable, efficient solutions that ensure uptime and operational resilience. Whether through phased infrastructure upgrades, tailored cooling strategies, or AI-powered predictive maintenance, Salute’s expertise helps data centers navigate uncertainty while enabling seamless integration of advanced computing technologies.