Data volumes are increasing at a phenomenal rate. With big data comes one particular challenge that can put the spanner in the works: data and network latency, which can slow down your networks and render your big data analysis inaccurate and useless.
June 28, 2017
AI lies at the heart of solving network latency issues
In his latest article in Information Age, Bridgeworks CEO, David Trossell discusses the extent that the analytics will increasingly have to be achieved with the help of artificial intelligence and machine learning.
“So being a true data leader also requires you to recognise when and where new technologies can help you to add value, and it necessitates understanding that without big data many organisations today wouldn’t be able to operate profitability.”
– David Trossell, CEO Bridgeworks, LTD
To be a true data leader requires the ability to recognise when you need data acceleration tools that use machine learning to mitigate the impact that latency has on networks and your ability to create a clearer and deeper analysis of whatever you need to analyse.
The end game is about creating value for your enterprise, for your customers and partners. With artificial intelligence and machine a deeper level of insight can be achieved, and this can help with the development of new products and services through the finding of new trends – as well as an overall improved ability to create a deeper level of customer and market insight.
The term ‘data leader’ may mean different things to different people, but essentially it’s about how well an individual or an organisation derives value from its data through proactive data analytics.
The problem is data volumes are increasing at a phenomenal rate, to the extent that the analytics will increasingly have to be achieved with the help of artificial intelligence and machine learning.
Daniel Gutierrez cites Massimiliano Versace, co-founder and CEO of Neurala, in his article ‘Big Data Industry Predictions for 2017’ for InsideBigData: “More and more companies are applying artificial intelligence and deep learning into their applications, but a unified, standardised engine to facilitate this process has lagged behind.
“Today, to insert AI into robots, drones, self-driving cars and other devices, each company needs to reinvent the wheel. In 2017, we will see the emergence of unified AI engines that will eliminate or greatly mitigate these inefficiencies and propel the formation of a mature AI tech supplier industry.”
>See also: The UK’s top 50 data leaders 2017
The end game is about creating value for your enterprise, for your customers and partners. With artificial intelligence and machine a deeper level of insight can be achieved, and this can help with the development of new products and services through the finding of new trends – as well as an overall improved ability to create a deeper level of customer and market insight.
But with big data comes one particular challenge that can put the spanner in the works: data and network latency, which can slow down your networks and render your big data analysis inaccurate and useless.
To be a true data leader therefore requires the ability to recognise when you need data acceleration tools that use machine learning to mitigate the impact that latency has on networks and your ability to create a clearer and deeper analysis of whatever you need to analyse.
Unlocking the Power of Big Data
So being a true data leader also requires you to recognise when and where new technologies can help you to add value, and it necessitates understanding that without big data many organisations today wouldn’t be able to operate profitability. You therefore have to protect your data.
