By Marco Vieira, Country Manager: Hybrid IT at HPE SA
Businesses that have had set up remote working environments for their employees to work from home amid the lockdown are in many cases seeing a surge in hyperactivity, as people are more productive, and many tend to work longer hours.
Couple this with sectors that have seen an exponential increase in activity as more people reach out to essential services, whether it be financial institutions for financial relief or e-Commerce for goods.
As a result, the organisations’ backend systems having to work much harder.
Data analytics proves key
What’s more, many organisations are placing more emphasis that organisations are placing on data analytics, realising that data can provide them with alternative sources of information for positioning their products or reaching customers with whom they no longer have direct contact.
All of this has led to an increase in workloads, which demands greater efficiencies from backend systems, and has also placed a greater demand on storage due to the large number of employees working from home that have to be supported.
Under ordinary business conditions, storage requirements increase naturally and have to be managed consistently, but this is no longer the case under lockdown, as many companies have seen an unprecedented surge in storage demand, driven by data growth.
It is also important to note that storage is not just a single dimension, there are elements such as backup and Disaster Recovery (DR) to take into consideration, which are also impacted by the expansion of remote working environments and data growth, and in turn amplify the demands on storage.
Organisations are likely to see this situation continuing post lockdown to a greater or lesser degree, as enterprises will have to learn to deal with the ‘new normal’, which will see many people remaining at home and working remotely.
Additionally, organisations will increasingly focus on big data analytics to drive greater business growth as they come under pressure to recover. However, this will place storage systems under pressure to deliver critical data in the quickest and most efficient way possible.
Efficiency will be high on the priority list for many, with some capabilities becoming critical as organisations strive to redefine how business is done post the pandemic.
For many there will be a need for greater efficiency within their own business operations and some will look to new solutions that will allow them to scale their environments up or down.
Accelerating the uptake of AI-driven storage
Hence, companies will be looking for environments that are easy to manage and deploy, and solutions that are far less complex, application-aware and intuitive. This is where it will be key for enterprises to deploy Artificial Intelligence-drive storage arrays that can self-analyse and self-optimise for performance and efficiency.
Modern storage solutions are intelligent and understand the workloads that are being hosted within, while constantly analysing themselves to optimise the delivery of these workloads.
They are able to report back into a vendor’s machine learning engine which processes this information to further improve and optimise performance, allowing environments to learn from the collective and fix or prevent issues before they even occur.
However, it has gone beyond just storage – server platforms operate and run within the same analytics environment – and will continue to drive greater efficiency, users to be more productive. More than ever, organisations want to process data in real time, which will allow them to stay with the curve. They will require serious performance and capabilities within their environments to churn their information out as fast as possible.
The reality is that most businesses will have to redefine the way they operate. The current lockdown and post lockdown environment will see a greater push towards digital transformation, with many organisations embracing more digital platforms and capabilities to enhance business and end user work environments.
This article was published in partnership with HPE Nimble.