The Most Common Data Center Design Missteps

Introduction

Data center design aims to provide IT equipment with a high-quality, standard, safe, and reliable operating environment, fully meeting the requirements for stable and reliable IT device operation and prolonging the service life of computer systems. Successful data center design directly impacts long-term planning, requiring it to be professional, advanced, integral, flexible, safe, reliable, and practical. Leveraging an automated error detection platform can significantly enhance these design attributes. For example, Entangl provides an AI-driven platform designed to enhance data center design by automatically detecting errors and streamlining processes.

9 Missteps in Data Center Design

Data center design is one of the most effective solutions to overcrowded or outdated data centers, while poor design leads to obstacles for growing enterprises. Missteps can result in wasted funds and operational issues, raising expenses. Here are 9 mistakes to avoid when designing a data center.

1. Miscalculation of Total Cost

Data center operation expenses consist of maintenance costs and operating costs. Maintenance costs cover critical facility support infrastructure, such as OEM equipment maintenance and cleaning fees. Operating costs include daily operational activities, field personnel, and site-specific documentation. Automating the creation of such operational procedures through an AI-based system can help ensure a streamlined, accurate approach, reducing human effort and minimizing discrepancies. The best approach for data center budgeting involves considering capital expenditures, operational expenses, and energy costs in alignment with an organization's risk and business spending profiles.

2. Unspecified Planning and Infrastructure Assessment

Clear planning and a thorough infrastructure assessment are essential in data center construction. For instance, a successful construction project must have a chain of command that defines responsibilities. Advanced AI-driven planning tools can automatically assess infrastructure, identify connectivity needs, and avoid missteps from incomplete or unclear information—saving time and reducing rework.

3. Inappropriate Design Criteria

Misaligned design criteria can lead to overspending. Establishing the right design criteria helps avoid a mismatch between business needs and infrastructure capabilities. Leveraging an AI platform that autonomously evaluates design criteria helps ensure data center facilities are neither overbuilt nor underprepared, aligning capital expenditures and operational needs with the intended performance characteristics.

4. Unsuitable Data Center Site

A proper site selection can make or break a data center project. Premature or unreasonable site selection often fails to meet design requirements, especially in terms of power and connectivity. Automated assessments of geographical suitability can aid in decision-making by highlighting site-critical information and preventing costly site errors.

5. Pre-design Space Planning

Effective planning of space capacity inside the data center—from raised floors to electrical equipment—is crucial. Mistakes in planning space capacity can lead to costly re-evaluations or component repurchases. AI solutions that autonomously assess space requirements against future capacity can support architects and engineers in ensuring that the design suits the available site space without the need for rework.

6. Mismatched Business Goals

A clear understanding of business goals is needed for effective data center debugging and planning. AI-based platforms can help align data center architecture with specific business goals by evaluating ongoing infrastructure suitability and identifying mismatches early on.

7. Design Limitations

Modular and flexible designs are well-publicized within the data center industry. Leveraging an autonomous platform that identifies design limitations before construction is crucial for long-term operational stability.

8. Improper Data Center Power Equipment

Selecting suitable power equipment that supports projected capacity is crucial to ensuring uptime. AI-driven solutions can optimize the configuration of power systems to reduce waste while maintaining the required redundancy, minimizing excess costs.

9. Over-complicated Design

Introducing excessive redundancy or overcomplicating a modular system can increase both costs and risks. Simplifying the design while maintaining reliability is an art. Automated design review platforms can provide insights into system vulnerabilities introduced by redundant components, preventing over-complexity that could compromise the data center's stability.

Conclusion

Entangl's platform is a powerful tool for ensuring that data centers are designed efficiently, minimizing costly errors, and supporting long-term stability.

Avoid the nine missteps above to establish a stable, efficient data center IT infrastructure that aligns with your business needs. Automated platforms that help assess design, operational procedures, and infrastructure play a significant role in reducing the risks of costly errors, ensuring a well-calculated, meticulously planned, and optimized data center design that supports your business long-term.