Increasingly, senior executives are more heavily scrutinizing the business value of their software deployments by looking for cost savings and operational efficiencies. At the heart of this evaluation process, they are looking at the data center estate and the actual data itself. This enables managers to validate whether previous software investments have had a positive effect on their facilities and their bottom line.

This requirement to provide clearer evidence of tangible improvements increases the pressure on data center managers. Most managers already use a myriad of software and reporting tools to maintain service availability and to make informed decisions about critical environments, but as other business units ask to leverage operational data, the more important it is becoming for data to be “normalized.”

This process of normalization involves converting data from its proprietary format so it can be used by any integrated solution, such as DCIM, BMS, ITSM, ERP and other applications that provide accountability data. 

Normalization for All

It’s no surprise that this level of integration and normalization can prompt significant IT challenges. However, if a data center manager is equipped with the proper software tools built around a solid middleware layer, the issues are much easier to address.

There are software platforms that support bi-lateral data transfer, interpretation and analysis, but many data center managers are not leveraging these options. Even though the technology is available, many facilities still rely on outdated collection processes and manual methods for managing data center operations. In fact, a 2015 survey by Intel found 43% of data centers still rely on manual methods for capacity planning and forecasting.

Take a single server as an example. Operators used to need to know whether a server was running and if possible, how efficiently. The complexity and power of modern software has changed this requirement. Managers now need precise knowledge of the status of the asset, its intended and actual location, power efficiency and utilization, and its cost throughout its lifespan. If data is being shared across the business, the granularity of information required quickly increases.

As a result, tools that present the data in a common, useable format will be more valuable in the long-term. And by standardizing the format in which data can be analyzed, businesses are able to gain greater clarity over the entire data center operation. Ultimately, decision making is drastically improved and assets can be maintained more efficiently.

Higher up the management chain, executives can take a more active role in strategic planning. Disparate operational departments can come together to analyze data from multiple sources and quantitatively prove that current software investments are delivering return on investment, providing a clear business case for other software deployments.

From a total cost of ownership perspective, it is simpler to understand where budget is being spent on a daily basis. This includes asset depreciation data, the cost of scheduled and unplanned maintenance, associated personnel expenditures, leasing information and warranty data, real-time workload utilization, and associated environmental and thermal costs such as power and air conditioning usage.

All of this helps to paint a more accurate, holistic overview of a data center’s performance; an outcome that simply would be impossible were it not for normalized data.