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The hybrid cloud
2020 saw an astounding increase in the number of businesses that had to adapt their technological infrastructure to accommodate an overwhelmingly remote infrastructure and settle in for long-term uncertainty.
The key to survival (and thrive in many cases) has been a business’s ability to adapt its IT infrastructure to maintain the same customer service levels and rapidly shifting market grounds. However, not all business models can reap benefits from a fully cloud-native architecture. Common ground is the use of a hybrid cloud. Yet, many realize its advantages and have moved their additional workloads to the cloud using a hybrid cloud model.
Simply put, it’s a computing environment that shares data and applications between a private and public cloud. As demand fluctuates, non-sensitive and less critical workloads can move to the public cloud to handle any overflow without giving the public cloud access to the entirety of its data. Many define a hybrid cloud as the “best of both worlds.”
Many businesses see short-term spikes in demand. For example, an eCommerce brand may see a massive spike in demand during significant shopping seasons. It does not make sense for the brand to invest in infrastructure to handle such far and few surges. A hybrid cloud allows such applications to scale their infrastructure between a private and public cloud when the demand is high. This allows companies to pay for only the services they use and use them without spending on dedicated infrastructure.
Increasing progression of technologies such as quantum computing will make the cloud faster – with risks.
New computing technologies such as quantum computing will help make the cloud’s underlying infrastructure better and faster and, at scale, cheaper. However, this enhanced computational power can also be misused, such as breaking encryption. This means that the world of security and cryptography will also see breakthroughs in releasing quantum-safe algorithms.
An increase in AI-driven automation and tools will drive large scale migration.
AI-driven automation has gained a rapid foothold in the last two years. The pandemic further accelerated its adoption in automation as it is a natural fit. This AI-driven automation can help analyze and simplify repetitive code for deploying infrastructure.
AI is already being used in natural language processing to decipher and ‘learn’ human intent. These kinds of models can be used to go beyond containerization and automate the creation of microservices. Currently, AI training is an intensive process that requires intensive resources. With rapidly increasing hardware efficiency and reducing cost, the time taken to train AI-models will reduce by a significant order of magnitude.
As migrations continue to grow, the natural tendency to automate will grow as well. Hence tools, both open-source, and proprietary will increase in 2021. These tools will also help developers shorten the learning curve to develop and deploy applications for the hybrid cloud.
Interoperability between providers will pick up slowly, paving the way for startups to innovate
Today’s big cloud providers tend to keep you within their ecosystem of services, and rightfully so. They’ve spent fortunes on creating the infrastructure and will look to make returns on their investment. However, as many enterprises take the hybrid cloud approach, there is a growing call for bridging applications between multi-vendor platforms. Many find that specific applications with different providers are better than being locked in with one provider.
While this is contrary to their business models that look to upsell services from within their portfolio, bridging methods would help develop standards for sharing access and data across platforms. Until the big platform providers release their applications, expect to see startups come with their services to achieve the same.
The growth of RPA and AI-driven automation
RPA or robotic process automation occupies a sweet spot in the automation technology space. Any process that is repeatable in a consistent manner can be automated using RPA. As machine learning and artificial intelligence algorithms become more sophisticated, they will become more enmeshed with fundamental process design creating intelligent process automation. A continually learning automation system can result in genuinely substantial savings and efficiency.
Use cases beyond IT
RPA and intelligent automation are not likely to stay just in the IT-sphere and see use cases in more non-traditional environments. RPA truly shined in 2020, where automation bots played a critical role in industries working to find a Covid cure. Healthcare companies such as Takeda employed RPA to fully automate its clinical trials’ administrative side, freeing up time for its researchers to focus on the more critical high-level tasks at hand.
Security and privacy concerns
While the advantages of RPA are apparent, there are a few concerns as well. In typical enterprise automation, systems generated log files that served as the primary data source. RPA captures data from the user layer, it’s richer and allows for better insights. Organizations must ensure that data is protected and anything that the process uses is safeguarded so that leakages are prevented. In 2021, as RPA makes accelerated progress, there will be an increasing focus on privacy and security.
Transformational impact
Throughout 2021, we’ll see bridging between RPA islands (isolated automation projects). With this, we’ll start seeing the more transformational impact as automation becomes more autonomous. With digital transformation taking high precedence in 2021, RPA will play a key role in accelerating its pace throughout the enterprise.
Artificial intelligence and machine learning will become mainstream
AI and ML have been buzzwords for a while now. The pandemic gave these technologies the real push to be used in scenarios that saw their real value. As healthcare companies rush to deliver a viable long-effecting vaccine, ML and AI helped scale and accelerate several parts of the drug discovery and clinical trials process.
Catalysts for faster time-to-value
Apart from the vaccine, machine learning has enabled enterprises to act as catalysts in synthesizing large, disparate, and often messy data-sets to cleaner standardized data. AI algorithms then use this data to derive actionable insights and predict future courses of action.
As of last year, the adoption of AI and ML tools increased, but a sizable majority of projects were still at the pilot stage. This will change in 2021. Fueled by the need to recoup losses from 2021 and reach the markets faster, more projects will come out of cold storage and mainstream production.
Augmenting human skills
As the second wave of the pandemic hits businesses that are yet to recover from the first crash, AI and ML tools are no longer the new shiny tech bauble but an imminently needed tool. E.g., as human workforces spread thin, AI and ML can dramatically enhance contact centers that answer customer service queries.
While concerns about AI and ML tools making human workforces redundant persist, the real trend points to an augmentation of nuanced skills that humans bring augmented with these tools’ raw, intelligent automation.
Stay tuned for Part II, where we cover more trends and predictions for 2021!
