The automotive industry must understand how to make the most of cloud investments through the use of machine learning and automation, writes Christian Ott
Adopting cloud is supposed to bring big efficiency benefits. So why do businesses often end up losing control of their cloud costs, spending as much as or more than they did before transition to the cloud?
The simple answer is that embracing cloud infrastructure without proper monitoring and analysis makes optimising costs a serious challenge for IT teams, which have to make sure that duplication of effort and over-provisioning don’t become too complex to keep track of.
In the automotive industry, change is the new constant and being agile is key to survival. The cloud comes to mind quickly when thinking about change, agility and speed, especially in software development. Infrastructure is available at your fingertips at any scale and that’s what developers love. However building a reliable and scalable ecosystem can become complex very quickly and a certain level of enterprise quality and scale is required, especially when managing data and scaling up tons of containers, for example with automated testing.
Waste not, want not
According to a Forbes report last year, around 80% of enterprises consider managing cloud spend a challenge and waste an average of about 35% of their cloud spend. The manufacturing wings of automotive businesses are especially vulnerable to the problem of waste, because production processes are supported by significant digital infrastructure. This means that the process of building and running cloud-native software infrastructure causes many automotive businesses to experience cloud sprawl in the area of costs.
As data volumes continue to grow, managing compute requirements in cloud ecosystems is increasingly challenging. This rising complexity means that not every company has the time or the ability to understand how to optimise their cloud infrastructure costs and keep them under control. In fact, according to Capgemini, the technology evolution is driving massive demand for the next generation of software and data solutions that the industry cannot currently deliver.
With the global automotive industry adopting to the new normal, it’s never been more important for the sector to get cloud right
Unfortunately, this lack of visibility into cloud computing resources and the struggle to manage them has direct cost implications for the rest of business. To deliver maximum value for cloud dollars spent, automotive companies need to rely on—and invest in—tools that can dynamically match their ongoing needs to the optimal cloud resources.
How automotive businesses can solve this
In order to combat these challenges, the automotive industry needs to understand how to make the most of cloud investments through the use of machine learning and automation. This means collecting the data at the edge, transporting it to the right place at the right time with the right attributes. This also includes controlling the cost in the cloud through algorithms to choose the correct cloud service, subscription plan and automation.
Working with the right technology partners will help automotive businesses take advantage of this technology to improve the user experience, enable faster innovation, ensure quality throughout the manufacturing process, increase privacy and data security, as well as providing actionable insights for improvement. By doing this, automotive organisations can simplify and automate their cloud infrastructures, whilst ensuring peak performance at the lowest possible cost no matter where workloads and applications are run.
With the global automotive industry adopting to the new normal, it’s never been more important for the sector to get cloud right. By tapping into machine learning and automation, automotive businesses can optimise cloud costs based on business needs to drive efficiencies in the future.
The opinions expressed here are those of the author and do not necessarily reflect the positions of Automotive World Ltd.
Christian Ott is Director, Solution Engineering, Global Automotive at NetApp
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