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Upskilling the "Digital" Workforce

Terry Critchley

"Upskilling" is a popular phrase, used liberally by educators, vendor trainers and other organizations like Microsoft (Global Skills Initiative) and LinkedIn (Skills Path) with their own programs of numerous courses covering many aspects of IT. This sounds a great idea for bolstering IT skills for the changing world of computing.

I have my reservations though, based on 50+ years of IT experience, both at the coal face across a wide set of industries and latterly as author and researcher. I have examined many, many computing curricula across schools, universities and other organizations offering various forms of "IT" education and, like Sir Thomas Beecham, I have "formed a very poor opinion of it." Why?

Download the full paper

What is the Problem?

That is difficult to put into a few sentences as there are many aspects of this current computing education which make me uncomfortable.

■ One is the lack of a common idea of what constitutes IT or "digital" knowledge, from Mrs. Jones using the internet for supermarket shopping to people at the forefront of technology.

■ The variation in topic coverage, going from the nitty gritty to the sublime in the same course but for different topics — it feels bitty and cobbled together, possibly developed by a lot of people working separately.

■ The lack of pragmatic content, particularly in computer science (CS) courses, for example, "... this technique is used in the X industry to estimate the depth of a depletion layer in semi-conductors and also in Y industry" never appears. In other words, it lacks real world context.

Let me give an illustrative example. In network courses or modules, one is taken on a journey through the bowels of networks — routers, MAC, 7-layer models, TCP/IP and much more. There is no discussion of what the pragmatic aspects of networks are — congestion, compression, data loss, high availability, cybersecurity forensics, network performance management, network simulation tools — and a host of other day-to-day aspects of designing and running a network. In short, much of the computer teaching fails to answer the "so what?" question.

■ There is a plethora of courses offering a specialism at the end of it and cybersecurity is a case in question. I have looked at many of these and none, apart from a single IBM course, ask for any previous training (prerequisites). This attempt to develop a specialist from a standing start is akin to trying to become a cardiac or brain specialist without going through general medical school.

■ The overall impression I was left with was that most courses resembled a fairly random set of specific topics with no feeling a synergy and what IT was all about overall. This left me with the impression that I might do all the topics in a course but still feel uneasy about whether I could give a one-hour presentation on what IT was all about.

I have two analogies which might illustrate what I have said above more clearly.

A nautical analogy. I join a course on sailing and am trained on yachts, speedboats and a few larger vessels and graduate feeling very pleased with myself. However, I meet an old salt who had sailed the seas for decades and he asked me what I'd been taught about navigation — with instruments or by the stars — and about trade winds, dangerous waters, different ports and assessing weather conditions in the absence of a forecast. I pleaded a headache and politely bid him farewell and a fair wind on the seas of life.

Picture a hypothetical course on the motor car which I undertook. It covers the components parts — engine, brakes, transmission etc. — dealing with equations of coefficients of friction, the Carnot Cycle, adiabatic expansion of gases, hydraulic transmission and a host of other topics. Great!

Figure 1: Parts of an Automobile Engine

However, when I re-joined the real world after three year's study, I realized I couldn't drive a car, read a map, plan a journey, assess what kind of vehicle I needed for my purposes, had little idea of service intervals and other day-to-day things necessary in possessing a car.

Need I go further???

What Is the/a Solution?

I have thought long and hard about this and came to the conclusion that all IT education should fit into a framework and what takes place within it reflect the activity flow of nearly all IT projects, large and small. When people understand this, they see the value in taking this view from 10,000 feet and then home in on topics, rather like one does with views in Google Earth. It is also key that people recognize that they are moving into IT as a career and not just a job paying X £ or $.

A key factor which tells why the career mentality is superior to the job mindset is the volatility of change in IT. Many people have written about this and point to the idea that IT jobs, like the Covid virus, mutate over time until its boundaries change or, in the worst case, become a different subject or, worse still, become obsolete. The half-life of an IT job in a particular form is estimated to about 18-24 months, hence the need for rapidly changeable training.

Summary

■ The field of education is like a patchwork quilt of difference shades and depths and often comprises a set of topics with little connectivity or "where used" context. It is also limited in scope and ignores major topics such as mainframes, enterprise computing, cloud, service levels and high performance computing (HPC). These omissions limit the students' career scope considerably.

■ Much IT education is based on computer science (CS) which is very limited in its scope and rarely offers workplace context.

■ There is a prevailing idea that an IT specialization can be taught from scratch without solid prerequisite knowledge.

■ There is also the mistaken notion that "coding and algorithms" are the "be all and end all" of IT work. This activity is just one in a chain of activities.

■ An IT project is a chain of activities which depend on each other for the success of the project.

■ There is a need for a broad underpinning IT education track as a springboard to promotion or specialization.

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Upskilling the "Digital" Workforce

Terry Critchley

"Upskilling" is a popular phrase, used liberally by educators, vendor trainers and other organizations like Microsoft (Global Skills Initiative) and LinkedIn (Skills Path) with their own programs of numerous courses covering many aspects of IT. This sounds a great idea for bolstering IT skills for the changing world of computing.

I have my reservations though, based on 50+ years of IT experience, both at the coal face across a wide set of industries and latterly as author and researcher. I have examined many, many computing curricula across schools, universities and other organizations offering various forms of "IT" education and, like Sir Thomas Beecham, I have "formed a very poor opinion of it." Why?

Download the full paper

What is the Problem?

That is difficult to put into a few sentences as there are many aspects of this current computing education which make me uncomfortable.

■ One is the lack of a common idea of what constitutes IT or "digital" knowledge, from Mrs. Jones using the internet for supermarket shopping to people at the forefront of technology.

■ The variation in topic coverage, going from the nitty gritty to the sublime in the same course but for different topics — it feels bitty and cobbled together, possibly developed by a lot of people working separately.

■ The lack of pragmatic content, particularly in computer science (CS) courses, for example, "... this technique is used in the X industry to estimate the depth of a depletion layer in semi-conductors and also in Y industry" never appears. In other words, it lacks real world context.

Let me give an illustrative example. In network courses or modules, one is taken on a journey through the bowels of networks — routers, MAC, 7-layer models, TCP/IP and much more. There is no discussion of what the pragmatic aspects of networks are — congestion, compression, data loss, high availability, cybersecurity forensics, network performance management, network simulation tools — and a host of other day-to-day aspects of designing and running a network. In short, much of the computer teaching fails to answer the "so what?" question.

■ There is a plethora of courses offering a specialism at the end of it and cybersecurity is a case in question. I have looked at many of these and none, apart from a single IBM course, ask for any previous training (prerequisites). This attempt to develop a specialist from a standing start is akin to trying to become a cardiac or brain specialist without going through general medical school.

■ The overall impression I was left with was that most courses resembled a fairly random set of specific topics with no feeling a synergy and what IT was all about overall. This left me with the impression that I might do all the topics in a course but still feel uneasy about whether I could give a one-hour presentation on what IT was all about.

I have two analogies which might illustrate what I have said above more clearly.

A nautical analogy. I join a course on sailing and am trained on yachts, speedboats and a few larger vessels and graduate feeling very pleased with myself. However, I meet an old salt who had sailed the seas for decades and he asked me what I'd been taught about navigation — with instruments or by the stars — and about trade winds, dangerous waters, different ports and assessing weather conditions in the absence of a forecast. I pleaded a headache and politely bid him farewell and a fair wind on the seas of life.

Picture a hypothetical course on the motor car which I undertook. It covers the components parts — engine, brakes, transmission etc. — dealing with equations of coefficients of friction, the Carnot Cycle, adiabatic expansion of gases, hydraulic transmission and a host of other topics. Great!

Figure 1: Parts of an Automobile Engine

However, when I re-joined the real world after three year's study, I realized I couldn't drive a car, read a map, plan a journey, assess what kind of vehicle I needed for my purposes, had little idea of service intervals and other day-to-day things necessary in possessing a car.

Need I go further???

What Is the/a Solution?

I have thought long and hard about this and came to the conclusion that all IT education should fit into a framework and what takes place within it reflect the activity flow of nearly all IT projects, large and small. When people understand this, they see the value in taking this view from 10,000 feet and then home in on topics, rather like one does with views in Google Earth. It is also key that people recognize that they are moving into IT as a career and not just a job paying X £ or $.

A key factor which tells why the career mentality is superior to the job mindset is the volatility of change in IT. Many people have written about this and point to the idea that IT jobs, like the Covid virus, mutate over time until its boundaries change or, in the worst case, become a different subject or, worse still, become obsolete. The half-life of an IT job in a particular form is estimated to about 18-24 months, hence the need for rapidly changeable training.

Summary

■ The field of education is like a patchwork quilt of difference shades and depths and often comprises a set of topics with little connectivity or "where used" context. It is also limited in scope and ignores major topics such as mainframes, enterprise computing, cloud, service levels and high performance computing (HPC). These omissions limit the students' career scope considerably.

■ Much IT education is based on computer science (CS) which is very limited in its scope and rarely offers workplace context.

■ There is a prevailing idea that an IT specialization can be taught from scratch without solid prerequisite knowledge.

■ There is also the mistaken notion that "coding and algorithms" are the "be all and end all" of IT work. This activity is just one in a chain of activities.

■ An IT project is a chain of activities which depend on each other for the success of the project.

■ There is a need for a broad underpinning IT education track as a springboard to promotion or specialization.

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...