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IT Professionals Experiencing Substantial Shift in Responsibilities

The roles and activities performed by IT professionals have evolved dramatically over the past ten years due to the convergence of modern development technologies, cloud platforms, and as-a-service offerings that can significantly improve overall productivity.

Today, many of these professionals find themselves in hybrid roles that combine traditional development activities with activities that formerly were associated with operations professionals who historically had few or no development-oriented responsibilities. A new International Data Corporation (IDC) report provides an extended census and forecast with detail for both traditional IT operations roles and these new hybrid roles.

"The census data shows that a dramatic, once-in-a-generation shift in the composition of the IT workforce is underway. This shift is akin to what took place during the years from 1997 to 2002 when the emergence of the commercial internet and the .com era turned priorities upside down for much of corporate IT and led to the hiring of vast numbers of web developers and networking experts," said Al Gillen, Group VP, Software Development and Open Source, IDC. "The increased adoption of cloud computing is driving similar transitions today in IT teams supporting this modern deployment model."

In developing this data set, IDC used the following definitions to describe the roles broken out in the study:

DataOps uses a combination of technologies and methods with a focus on quality for consistent and continuous delivery of data value, combining integrated and process-oriented perspectives on data with automation and methods analogous to agile software engineering.

DevOps uses collaborative, agile approaches paired with extensive automation development pipelines, testing, infrastructure configuration, provisioning, security controls, and life-cycle continuous integration (CI) for continuous development and continuous delivery (CD).

DevSecOps uses a methodology that asserts that security needs to be prioritized at the beginning of the DevOps delivery pipeline. It enables DevOps teams, collaborating with security, to act as key stakeholders in defining and implementing security policies.

ITOps uses technology and methods to provide routine, scheduled tasks and unscheduled support activities related to IT systems. ITOps professionals may spend as much as 50% of their time engaged with business users in support, the elicitation of requirements, and performing contingent or secondary business tasks.

MLOps uses technology and processes to streamline and automate the entire machine learning (ML) life cycle. The key capabilities include managing and automating ML data and pipelines, ML code, and ML models from data ingestion to model deployment, tracking, and monitoring. MLOps uses similar principles to DevOps practices, applied to machine learning processes.

Platform engineering is a discipline of designing and building toolchains and workflows that enable self-service capabilities focused on managing and optimizing the software delivery process to deploy applications and services to cloud platforms.

Site reliability engineering (SRE) includes software engineers who build scripts to automate IT operations tasks such as maintenance and support. To enable efficiency and reliability, SRE teams fix operational bugs and remove manual work in rote tasks.

Systems administrators configure, maintain, and support computer systems and systems of systems using a variety of tools and methods appropriate to the system or systems of systems in use. They may spend as much as 50% of their time engaged with business users in defining key requirements, business goals, and adaptations needed to maintain fit for use and fit for purpose.

At a macro level, the study shows that a substantial shift in the responsibilities of IT professionals will occur over the next five years. The data indicates that IT professionals in the most purely operational roles are facing a transition to a more technical or focused role that very often may involve some level of software development work. Accordingly, the roles of IT operations and system administrators, respectively, are projected to decline at compound annual growth rates (CAGR) of -8.2% and -7.8% over the 2022–2027 forecast period. By comparison, the recently emerging roles of DataOps and MLOps are projected to have CAGRs of 17.9% and 20.1% respectively, although the growth is starting from comparatively small numbers.

DevOps and DevSecOps roles are also forecast to continue growing with DevSecOps roles showing a double-digit CAGR over the forecast period. DevSecOps roles will benefit from the growing application threat landscape and the dependence that organizations have on their software capabilities to be competitive, combined with the recognition that incorporating security as early as possible in the software development life cycle reduces costs and increases quality. DevOps growth will be muted somewhat by the growth in platform engineering roles, which will absorb some of these same functions.

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IT Professionals Experiencing Substantial Shift in Responsibilities

The roles and activities performed by IT professionals have evolved dramatically over the past ten years due to the convergence of modern development technologies, cloud platforms, and as-a-service offerings that can significantly improve overall productivity.

Today, many of these professionals find themselves in hybrid roles that combine traditional development activities with activities that formerly were associated with operations professionals who historically had few or no development-oriented responsibilities. A new International Data Corporation (IDC) report provides an extended census and forecast with detail for both traditional IT operations roles and these new hybrid roles.

"The census data shows that a dramatic, once-in-a-generation shift in the composition of the IT workforce is underway. This shift is akin to what took place during the years from 1997 to 2002 when the emergence of the commercial internet and the .com era turned priorities upside down for much of corporate IT and led to the hiring of vast numbers of web developers and networking experts," said Al Gillen, Group VP, Software Development and Open Source, IDC. "The increased adoption of cloud computing is driving similar transitions today in IT teams supporting this modern deployment model."

In developing this data set, IDC used the following definitions to describe the roles broken out in the study:

DataOps uses a combination of technologies and methods with a focus on quality for consistent and continuous delivery of data value, combining integrated and process-oriented perspectives on data with automation and methods analogous to agile software engineering.

DevOps uses collaborative, agile approaches paired with extensive automation development pipelines, testing, infrastructure configuration, provisioning, security controls, and life-cycle continuous integration (CI) for continuous development and continuous delivery (CD).

DevSecOps uses a methodology that asserts that security needs to be prioritized at the beginning of the DevOps delivery pipeline. It enables DevOps teams, collaborating with security, to act as key stakeholders in defining and implementing security policies.

ITOps uses technology and methods to provide routine, scheduled tasks and unscheduled support activities related to IT systems. ITOps professionals may spend as much as 50% of their time engaged with business users in support, the elicitation of requirements, and performing contingent or secondary business tasks.

MLOps uses technology and processes to streamline and automate the entire machine learning (ML) life cycle. The key capabilities include managing and automating ML data and pipelines, ML code, and ML models from data ingestion to model deployment, tracking, and monitoring. MLOps uses similar principles to DevOps practices, applied to machine learning processes.

Platform engineering is a discipline of designing and building toolchains and workflows that enable self-service capabilities focused on managing and optimizing the software delivery process to deploy applications and services to cloud platforms.

Site reliability engineering (SRE) includes software engineers who build scripts to automate IT operations tasks such as maintenance and support. To enable efficiency and reliability, SRE teams fix operational bugs and remove manual work in rote tasks.

Systems administrators configure, maintain, and support computer systems and systems of systems using a variety of tools and methods appropriate to the system or systems of systems in use. They may spend as much as 50% of their time engaged with business users in defining key requirements, business goals, and adaptations needed to maintain fit for use and fit for purpose.

At a macro level, the study shows that a substantial shift in the responsibilities of IT professionals will occur over the next five years. The data indicates that IT professionals in the most purely operational roles are facing a transition to a more technical or focused role that very often may involve some level of software development work. Accordingly, the roles of IT operations and system administrators, respectively, are projected to decline at compound annual growth rates (CAGR) of -8.2% and -7.8% over the 2022–2027 forecast period. By comparison, the recently emerging roles of DataOps and MLOps are projected to have CAGRs of 17.9% and 20.1% respectively, although the growth is starting from comparatively small numbers.

DevOps and DevSecOps roles are also forecast to continue growing with DevSecOps roles showing a double-digit CAGR over the forecast period. DevSecOps roles will benefit from the growing application threat landscape and the dependence that organizations have on their software capabilities to be competitive, combined with the recognition that incorporating security as early as possible in the software development life cycle reduces costs and increases quality. DevOps growth will be muted somewhat by the growth in platform engineering roles, which will absorb some of these same functions.

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Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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