According to 451 Research, by 2019, 69% of companies will operate in hybrid cloud environments, and 60% of workloads will be running in some form of hosted cloud service. The reasons aren’t too far-fetched. The hybrid cloud offers better storage capabilities, seamlessness, improved security and agility. But that’s not all!
With the growing need in information technology to figure out how quickly data can move within systems to enable real-time, time-sensitive decisions, the hybrid infrastructure has evolved by constituting elements of Internet of Things (IoT), Artificial Intelligence (AI), blockchain, and the others. It is now viewed as a revenue generator and enabler of innovation, rather than an added cost to the business.
Reengineering Cloud Infrastructure
Cloud system IaaS (Infrastructure as a service) is the fastest growing segment in the public cloud services market. Gartner forecasts that IaaS will reach $39.5 billion in 2019, up 27.6% from $31 billion in 2018. Enterprises today are witnessing a shift away from data center build-outs and fulfilling their infrastructure needs through the public cloud, which has resulted in infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) being the strongest and fastest growing segments.
In 2017, the growth in IaaS cloud system was 38.6% and is expected at a CAGR of 29.7% by 2021. Not quite far behind, platform-as-a-service (PaaS) is expected to grow by 23.5% and is forecast to grow with a CAGR of 19.9% by 2021 to a total market size of $14.80 billion. This massive growth of public cloud services has been further facilitated by enterprise companies such as Amazon, Microsoft and Google who are working hard to promote and innovate the “as a service” business model. And though IaaS and PaaS remain the two most commonly used service offerings, businesses are slowly but surely looking forward to adopting machine learning as a service (MLaaS) into their technology stacks.
For instance, let’s talk about IBM, one of the biggest cloud companies in the world. IBM states that the blend of AI and cloud computing “promises to be both a source of innovation and a means to accelerate change.” With the help of the cloud, smart systems can receive information which they need to learn, and in exchange, AI can provide information which can give a cloud more data. This symbiotic relationship can transform the development of AI.
To find effective options within the cloud, it is crucial for enterprises to first develop the infrastructure by going through the consultancy, design, and implementation process of an IaaS cloud, followed by the development of their PaaS and SaaS options for application deployment and ongoing management. But the stalwarts in the cloud like Amazon Web Services and Microsoft Azure have taken the IaaS and PaaS business model and applied it to all the technologies needed to scale a business. AWS currently has a vast catalog of microservices that companies can purchase to create their own digital platforms. AWS, along with Azure and Google Cloud Services, provides a complete set of services right from cloud computing, storage and database management to augmented and virtual reality, business productivity applications, and tools for the IoT.
Building an Advanced Data Management Architecture
The two important tenets of artificial intelligence and machine learning are data and compute. Machine learning models are generated to tackle massive amounts of data when applied to statistical algorithms. These models learn from the existing patterns of data and the amount of data available is directly proportional to the predictive accuracy. According to the consortium for network and computing infrastructure of automotive big data, “it is estimated that the data volume between vehicles and the cloud will reach 10 exabytes per month around 2025, approximately 10,000 times larger than the present volume. Such an increase will trigger the need for new architectures of network and computing infrastructure to support distributed resources and topology-aware storage capacity.”
Currently, for a significant number of enterprises, building a higher-value hybrid cloud that incorporates cognitive computing and data analytics is the best foot forward. Hybrid allows agility and provides a choice with consistency that enables enterprises to move workloads to where it makes sense. And even as the data stacks increase with time, hybrid clouds provide the option to combine and analyze data streams from various locations, seamlessly connecting all of an organization’s clouds, as well as outside data resources to function as a single entity.
Why Businesses Need the Flexibility of Cloud?
Combining cloud with automation technologies have proven to reduce costs, drive agility and deliver innovation in the ‘as-a-service’ business model. It conveniently provides a dynamic, intelligent architecture layer that facilitates decision-making and real-time action.
A report by Gartner suggests that cloud computing is approaching the highest level on its disruption scale and will act as a necessary enabler for future disruptions. It projects the worldwide public cloud service market grew to $246.8 billion last year. Driven by the growing demands of the businesses, hybrid cloud platforms that combine data analytics, AI and cognitive computing will be increasingly imperative for enabling innovation in the digital world. Enterprise leaders need to prepare to operate in the new era of IT because let’s face it – companies don’t control the marketplace, consumers do. Therefore, the incremental shifts are required to set the companies on the right trajectory if they aim to grow and prosper in the market. Cloud has become a catalyst for digital transformation – to transform tech ecosystems from collections of working parts into high-performance engines that deliver speed, impact, and value.
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