Maximizing ROI: Strategies For Data Center Cost Optimization

Data centers are significant investments. It’s therefore vital to do everything possible to ensure that you make the best return on that investment. With that in mind, here is an overview of what you need to know about data center cost optimization.

Understanding data center costs

When people talk about data center cost optimization, they are generally talking about data center operational costs. Although the implementation costs definitely matter, these are, by definition, a one-off expense that will be depreciated over time.

In the context of data centers, the core operational costs are typically power, water, network connectivity, and labor. To these must be added the need to replace both hardware and software. This may be on a like-for-like basis or as part of an upgrade program.

Major upgrades are generally treated as new implementations. These naturally come with a cost but this will probably be viewed as a capital expenditure rather than an operational cost.

Managing data center costs

As in most areas of business, the end goal of data center cost management is to achieve maximum value for minimal expense. This does not, however, necessarily mean always choosing the lowest-cost option. Instead, it means finding the option that best fits the organization’s overall goals.

For example, over the long term, building an in-house data center may be more economical than using colocation. The problem is that it ties up a lot of capital (and/or requires businesses to take on debt). Colocation, by contrast, is much easier on an organization’s cash. It is therefore a popular option even with businesses that could build their own data centers.

In addition to thinking about high-level goals and objectives, data center managers also have to keep a close grip on everyday costs. This means they need tools that allow them to view their finances at both macro and micro levels and track key metrics.

Strategies for data center cost optimization

Here are 10 useful strategies for data center cost optimization.

Server virtualization: Server virtualization involves running multiple virtual servers on a single physical server. It therefore maximizes hardware utilization and reduces the number of physical servers required. By consolidating workloads onto fewer servers, organizations can achieve significant cost savings in terms of hardware procurement, energy consumption, and data center space requirements.

Consolidation and decommissioning: Consolidating underutilized servers and decommissioning obsolete hardware can help streamline data center operations and reduce unnecessary expenses. By rationalizing hardware resources and eliminating redundant systems, organizations can lower maintenance costs, free up valuable floor space, and simplify management tasks.

Energy-efficient hardware: Buying energy-efficient hardware like servers, storage, and networking gear can cut energy costs significantly in data centers. These components use power more efficiently without sacrificing performance. The result is lower electricity bills and a smaller environmental footprint.

Dynamic cooling optimization: Using dynamic cooling strategies such as cold aisle containment, hot aisle containment, and variable-speed fans boosts cooling system efficiency. By aligning cooling efforts with the actual heat produced by servers, data centers can reduce energy waste and improve cooling, lowering operational costs.

Cloud integration: Smart use of the cloud can save money by handling fluctuating workloads without the need for excessive data center capacity. Cloud services often come with flexible pricing, including long-term fixed rates and short-term variable rates. This versatility helps manage costs effectively.

Automation of routine tasks: Automating common tasks like provisioning, configuration, and resource management can streamline operations and cut labor costs in data centers. Automation tools and scripts increase efficiency, reduce errors, and allow IT staff to concentrate on more strategic work.

Predictive analytics for maintenance: Organizations can use predictive analytics tools to spot potential hardware issues before they happen. This minimizes downtime and cuts maintenance costs. By studying past performance data and spotting failure patterns, organizations can plan maintenance and replace faulty parts before problems arise.

Optimized workload placement: Placing workloads smartly across servers and data centers helps use resources fully and lowers costs. Organizations adjust workloads dynamically based on performance needs, resource availability, and cost. This strategy reduces waste and unnecessary expenses.

Software license optimization: Proper management of software licenses prevents overspending. Organizations use license management tools to track how software is used. This helps find licenses that are unused or underused, secure better terms, and match software use to actual needs.

Continuous performance monitoring: Continuous monitoring helps find data center inefficiencies and areas to improve. By tracking metrics like CPU use, memory use, and network traffic, organizations can pinpoint issues, adjust resources, and boost system performance. This keeps costs down while ensuring reliability.