We speak to ITProPortal about why financial services shouldn’t need to spend on a new datacenter when machine learning technologies like PORTrockIT solve latency, security and storage issues.
Find a balance between what you already have and can still use new technologies that make it possible to reduce your expenditure while increasing network, data analytics and storage performance.
One large global banking firm wanted the ability to back up, restore and recover their data from their datacentres. It thought it had to build another datacentre – not too close, not too far away from its existing datacentre. The thing is this company probably has the facilities it needs on site already. It shouldn’t need to spend either £1, £100m or even £100bn on a new datacentre. That’s because none of its existing datacentres are operating at anywhere near to full capacity.
“Firms should focus on using the assets its already has, and it also needs to concentrate on its ability to allow workflow and collaboration with all of its key stakeholders.”
– David Trossell, CEO Bridgeworks, LTD
With digital transformation and Big Data in full flight in financial services, as well as the impact of the latest and greatest in information communications technology, there is always the temptation to buy new technology without considering how your organisation can improve the efficiency and utilisation of what it already has. On the other hand there are existing technologies such as WAN optimisation that once was the saviour of slow WAN can’t no longer fulfil the requirements of the industry with WAN speeds into the high Gigabit and the need to transfer rich, compressed and encrypted data.
So there should be some consideration about investing in technologies that permit you do more with what you already have. Data acceleration solutions such as Bridgeworks PORTrockIT do just that by using machine intelligence to mitigate data latency.
With accelerated data it’s possible, for example, to make the most of real-time data analysis – making big data analytics more timely and more accurate than if latency were allowed to continue to stall network performance.
Building another datacentre, or installing new network infrastructure, may sound good. But what will they really do? With regards to networks and latency there is only so much you can do within the limitations of the speed of light. Yet data acceleration supported by machine learning is an enabler, allowing encrypted data to be transmitted in ways that aren’t possible with WAN optimisation.