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2021 was a pivotal year for the future of work, forcing businesses to not just evolve their digital transformation strategies, but to rethink them altogether. It’s not just that more people are working from home. They’re doing so in a rapidly changing IT environment in which trends like edge computing, automation and huge data pipelines have ushered in continuous change.
According to the latest analysis by Gartner, from January 2022, worldwide IT spending is expected to hit $4.5 trillion in 2022. The size of the spend understates the scale of the transformations taking place. Organizations that embrace the constant tech-enabled disruption, continuously adapt and evolve and perhaps even stay abreast with it, will be the dominant players in their respective industries in the decade ahead.
What are some of the biggest trends we can expect to see this year for the future of work technology? I’ll dive into three major areas where we can expect to see major growth.
Even More Data
Last year, every person generated 1.7 megabytes of data every second. There were 70.3 billion real-time payment transactions in 2020, up 41% from the year before. Worldwide, it’s estimated that we’ll generate 79 zetabytes of data in 2021, a sum that will more than double by 2025 when it is expected to grow to 181 zetabytes.
The challenge for many companies when it comes to data is twofold. First, companies rely on a vast array of legacy systems and hybrid environments, even as they integrate new assets into the enterprise. They need to orchestrate their data pipelines to obtain a holistic view of the entire organization. The second problem is analytics, which can only be solved when the first challenge is met.
Right now, it’s difficult for most of us to see exactly where this data will take any individual business or industry. It requires a significant amount of agility to adapt to changing conditions and challenges with new business approaches without disturbing the foundations of success. This is perhaps best exemplified through the adoption of DataOps, an agile approach to data management that allows data engineers, data scientists and analytics teams to accelerate the way data is collected, used and analyzed and where it gets applied.
Increased Reliance on Automation
Traditionally, workload automation has referred to enterprise job scheduling and batch processing. But automation is breaking free from these limited contexts, giving way to hyperautomation — automation of automation. According to Gartner, the worldwide market for technology that enables hyperautomation — robotic process automation (RPA), low-code application platforms (LCAP), artificial intelligence (AI) and virtual assistants — will reach $596.6 billion in 2022, a 23% increase over 2020. We certainly believe this trend will continue.
For businesses, automation applied properly yields lower costs, reduced errors, much faster execution, fewer mundane tasks for employees and ultimately better customer experiences. And for employees, automation means less time on mundane tasks. Roles will evolve to make strategic use of uniquely human abilities like judgment and decision-making to manage the autonomous systems that run the business.
Managing this transition at the enterprise level will require orchestration of business workflows from a centrally managed location and tight integration of IT service and operations through six layers of the enterprise IT system: business processes, data pipelines, business applications, DevOps toolchains, IT infrastructure and edge orchestration.
Increased Investment in Security as Hybrid Work Becomes a Mainstay
Hybrid work has forced significant changes in network IT architecture. The idea of the traditional perimeter, already on shaky ground through the adoption of hybrid cloud networks, has been completely obliterated by hybrid work.
The challenges of security will be exacerbated as the number of connected edge-enabled Internet of Things devices grows to an estimated 7.7 billion by 2030. For enterprises, the only solution to these challenges is the introduction of a zero-trust framework that extends all the way to the edge.
The Common Threads
As you can see, all of these trends are interdependent and build upon one another. Edge devices create more data, which create more opportunities to automate and lead to a greater need for orchestration.
The impact of edge computing deserves some additional attention in particular for its ability to combine with data evolution to open immense opportunity.
We have all been living the infrastructure journey from central compute to hybrid IT to all cloud all the time and now the edge. Edge computing allows the processing of time-sensitive data in remote locations with limited or no connectivity to a centralized location, resolving the latency issues of cloud and delivering services on time, every time.
For the foreseeable future, we’ll be living a hybrid IT approach — an approach that includes centralized computing and the edge as part of an even more complex hybrid IT landscape.
There remain several use cases in which it would be valuable to integrate edge data with central computing data, for instance, in combining operational technology with IT to monitor asset performance and product life cycles. Imagine, for example, if an IoT device used to react to road quality in autonomous vehicles could also generate data that would be used to schedule predictive maintenance across the entire fleet.
Work cultures have undergone a dramatic change in the past two years. As businesses invest in workflows and solutions to manage this change in the years ahead, they are likewise transforming the work experience for their employees, who want to be engaged and empowered in a data-driven, human-centered enterprise.
Companies that cannot or will not prepare for this new world will have difficulty retaining top talent. Data, automation and edge computing will transform everything, including employee expectations.
Photo by Marvin Meyer on Unsplash
This UrIoTNews article is syndicated fromGoogle News