INDUSTRY WATCH

The demand for data center services has never been higher . Today ’ s data center managers face skyrocketing data storage needs , increasing rack densities , more connected devices and massive energy consumption . The demand for data center services is driving more technology deployment and faster upgrades of outdated power-hungry systems and components , in turn driving more change within the data center .

To add to this complexity , modern data center environments are distributed across many remote sites such as colocation and Edge deployments .

Data center managers struggle to remotely track all their assets , plan and manage their infrastructure capacity , perform changes at speed , increase energy efficiency and safeguard their assets and data from physical and cybersecurity threats , all while minimizing risk and maximizing uptime .

To make matters more difficult , many data center managers are forced to work with a myriad of disparate tools that lack integration . They have siloed databases with no single source of truth and must sacrifice productivity and data accuracy by manually entering data into multiple systems .

Why – and how – should data center managers follow best practices to maintain uptime , increase efficiencies and improve productivity ?

Maintaining uptime , increasing efficiency and improving productivity are arguably the three most important objectives for any data center manager .

Downtime has been estimated to cost US $ 5,600 per minute on average and that figure can be much higher for larger organizations . Downtime leads to lost sales , damaged brand reputations and SLA payouts .

Driving efficiency is more important than ever as most data center managers now have corporate sustainability initiatives they must comply with . Increased efficiency also leads to reducing operating costs and extracting more value out of existing capacity .

By improving productivity , data center managers can devote more resources to strategic projects rather than manual , time-consuming tasks . This can result in getting services online faster and responding to outages quicker .

Over the years , there has been plenty of new insights and process innovation that are driving data center management best practices . While there are many , the ultimate data center management best practice is ‘ integration with automation ’ or stitching together the multitude of data center operations tools and having a centralized view of all resources and capacities from integration of CMDBs , DCIM , BMS , ticketing and other tools . This real-time sharing of salient data makes data center asset , capacity and change management easier and faster than ever before .

What trends have you seen regarding data center managers operating remotely and what challenges does this pose ?

Remote data center management is the new normal . Whether it ’ s due to COVID-19 and work from home , security requirements to minimize on-site staff , or the distributed nature of modern data center environments , most data center professionals are no longer working on-site .

When working remotely without the right tools , data center professionals struggle to achieve their core objectives of maintaining uptime , increasing the efficiency of capacity utilization and improving productivity . Maintaining uptime remotely is a challenge when you don ’ t have information on the real-time power and environmental conditions within the data center and you can ’ t go on-site to take manual readings . A circuit breaker might trip or hot spots might damage equipment before you even know an issue is developing . When there is a serious problem , it can take more time to troubleshoot and resolve it .

Capacity planning is difficult because there is no visibility into how much space , power , cooling and port capacity is available . Remote data center managers don ’ t know where the best place to deploy new equipment is and manual spreadsheets and diagrams are error-prone and difficult to manage .

Finally , improving the productivity of people is arduous because remote teams lack tools that drive collaboration and data sharing . This leads to data siloes , multiple manual data entries and inaccurate data that can cause larger problems like downtime and stranded capacity .

Herman Chan , President , Sunbird Software

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