The big data movement is moving from an emerging technological trend to a transformative movement. Its path to prominence has been accelerated by business needs and its potential, leaving many IT leaders scrambling to keep up. A good service desk solution featuring change management capabilities could prove key in this area, especially as big data pushes businesses toward next-generation data center architectures.
According to a recent Network Computing report, big data is having a huge impact on data center infrastructure strategies and finding ways to manage such plans effectively could push many businesses to invest heavily in advanced data center infrastructure managers.
Big Data Transforming Infrastructure Models
The news source explained that the amount of data being generated and consumed in enterprise settings is absolutely staggering. At the same time, that information can prove almost useless if businesses are unable to successfully manage and analyze it. In most cases, cloud computing and WAN architectures are not going to provide the analytics and data transmission power organizations really need to deal with big data. Instead, new and emerging solutions are necessary to handle the scale and complexity of advanced analytics systems.
At this point, many businesses do have access to Hadoop and similar solutions aimed specifically at big data management. However, not many IT workers are able to fluently step into big data environments and manage everything, the report said. This is not a shortcoming of IT leaders, but a result of the fact that big data is so new, but also having such a wide reach on enterprise operations. This creates a functional climate where highly-specific management solutions alone may not be enough to support analytics. Furthermore, big data also puts an incredible strain on network, storage and server systems.
Big data is just one of the emerging trends impacting businesses today. Advanced cloud models, the consumerization of IT and other technological movements are all converging with big data to create major management challenges for IT teams, according to Network Computing. Overcoming these difficulties depends on taking meaningful steps forward toward innovation. This means not just putting one or two management solutions in place, but looking holistically at the behind-the-scenes systems that make IT tick.
Data center infrastructure is a critical component of supporting big data. The report said that high-density infrastructure models, sophisticated network setups and advanced management platforms will all play critical parts in enabling organizations to support big data. These next-generation data center architectures offer the combination of performance and control that is so integral to turning big data into a revenue generation source.
However, transitioning to a next-generation data center model is disruptive on many levels and requires an incredibly complex series of changes to be made throughout the entire configuration. These changes have a major effect on end users and require robust change management capabilities to get the job done
Understanding the Importance of Change Management for Next-Generation Data Centers
Migrating to a new data center model depends on effectively moving data, applications and other services to new machines and possibly new locations without experiencing a significant disruption in service. Furthermore, these advanced data center solutions also create more complexity once they are in place, adding incident, change and problem management challenges. Progressing from a legacy infrastructure model to a next-generation architecture is heavily dependent on an IT department's ability to support efficient change processes.
Investing in a robust change management platform is critical as businesses prepare for the future of a big data-driven data center. A good solution can coordinate and schedule change to streamline collaboration, remove risk and position organizations to maximize the revenue benefits of new IT investments by ensuring a smooth transition period.