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Computer Science (CS) and Information Technology (IT): Part 1

Are they one and the same thing?
Terry Critchley

I believe that in the UK and US there is a lack, nay absence, of pragmatic computing education which matches the needs of the current business world of information technology (IT). Current computer education, school and university, appears to me to be computer science based, very theoretical and does not follow the logical sequence of activity in the development, use and management of business applications that I observed in my long IT career spanning many industries.

(In this blog I use "business" in its broadest sense to mean the "world of work," be it commercial, scientific, medical or industrial.)

In fact, the curricula appear to me to be a collection of topics with little synergy and no end-to-end flow which IT projects have. As an analogy, consider the following scenario which I believe is a parallel to this.

A technical course on the motor car is run at Knowalot College, covering the internals of the car; Carnot cycle, adiabatic expansion, electronic ignition etc.; very detailed and demanding. At the end of the course, the student will probably have no concept of the motor car as vehicle, might not know how to drive, read a map or plan a journey from A to B. It is almost certain that he/she will not know how to decide on which car, van or lorry to recommend for the business he works for. In short, he/she is doomed to be a head-under-the-bonnet techie forever. That job of course is necessary but it cannot be classed as covering "motor transport," simply a technical corner of it.

Not only that, but the words "business" or "requirements" do not even appear anywhere in CS curricula I have searched. Only under the title "problem solving" could one guess that it refers to business. This is not to say CS education per se is bad; it just isn't a comfortable fit to the current computing world although it is gradually finding a niche in various areas of computing. These areas include big data, data science, cognitive and similar computing, and cybersecurity.

However, a broader knowledge across key IT concepts and architectures is needed since no person in IT is an island and anyone totally specialized will find it difficult to cross-communicate where his/her field overlaps with another, particularly in meetings or presenting to the business.

What Are the Differences?

In this part of blog, I will try to demonstrate this CS vs. IT dichotomy but first some outside view of the differences between CS and IT:

The proposition I put to CS people as to what modern IT is goes roughly as follows:

■ IT needs to be presented as sequence of related activities within a framework, not a simple collection of topics.

The flow of IT projects can be represented as:
- Business idea/need
- Specification of business flow
- IT Architecture (product-free)
- Populate the design with Technology
- Code/Buy software
- Implement
- Manage
- Update
- Retire systems and Start again

(There will of course be reviews and the like throughout this sequence of activity.)

You can see "coding" in context here; students and teachers cannot see this far.

■ There should be a pragmatic, contextual "wrapping" around major topics, for example, "this is used in the oil industry to map the subsea strata in the search for oil deposits." – the "so what?" test.

■ Emphasize important aspects of IT as a framework in which to teach topics. Over the years I have decided that FUMPAS represent the key elements (others can be found within these):

FUNCTIONALITY
USABILITY
MANAGEABILITY
PERFORMANCE
AVAILABILITY
SECURITY.

These are the criteria to map onto any business IT project to whatever degree of detail (reflecting its importance) the business decides.

■ Two large topics totally absent from CS curricula are mainframes, their operating systems and high performance computing (HPC). Much of the world's financial work is done on mainframes and its influence is growing, believe it or not. HPC computing is now a big field and is expanding beyond pure science into medicine, financial modelling, AI and other power hungry areas. Not to even mention them is dereliction of IT teaching duty, whatever the syllabus mandates. This sort of add-on could be done by selection of a suitable reading list, even if it is not in the syllabus.

CS school and university syllabuses I have studied do not fit the "real world" IT scene in breadth, depth or velocity of change and I therefore generated a keyword list to demonstrate this dichotomy. The list then developed into a learning Glossary, now on Amazon Kindle (check tomorrow for Part 2 of this blog), to show where IT fits in the business world and the topics which make it tick. The CS world can then see if their output matches these requirements.

So what? The world has gone mad on the "digital revolution" impacting nearly all business. I believe this issue needs to be addressed vigorously and quickly to tackle the much discussed "IT skills shortage." The current computer education, at least in the UK, will not achieve this aim, still less cater for the skills needs post-Brexit. I see no difference between UK CS and US CS, ergo much of what I say also applies the US.

Finally, I cannot find a syllabus anywhere I have looked that remotely covers IT as demonstrated by the list and subsequently the Glossary. I see this as a start in resolving the "IT Skills issue," a mantra that has been trotted out since the year 2000, if not earlier.

As Mark Twain said; "Everybody is talking about the weather, nobody is doing anything about it." I hope the Glossary is a beginning.

Read Computer Science (CS) and Information Technology (IT): Part 2

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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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Computer Science (CS) and Information Technology (IT): Part 1

Are they one and the same thing?
Terry Critchley

I believe that in the UK and US there is a lack, nay absence, of pragmatic computing education which matches the needs of the current business world of information technology (IT). Current computer education, school and university, appears to me to be computer science based, very theoretical and does not follow the logical sequence of activity in the development, use and management of business applications that I observed in my long IT career spanning many industries.

(In this blog I use "business" in its broadest sense to mean the "world of work," be it commercial, scientific, medical or industrial.)

In fact, the curricula appear to me to be a collection of topics with little synergy and no end-to-end flow which IT projects have. As an analogy, consider the following scenario which I believe is a parallel to this.

A technical course on the motor car is run at Knowalot College, covering the internals of the car; Carnot cycle, adiabatic expansion, electronic ignition etc.; very detailed and demanding. At the end of the course, the student will probably have no concept of the motor car as vehicle, might not know how to drive, read a map or plan a journey from A to B. It is almost certain that he/she will not know how to decide on which car, van or lorry to recommend for the business he works for. In short, he/she is doomed to be a head-under-the-bonnet techie forever. That job of course is necessary but it cannot be classed as covering "motor transport," simply a technical corner of it.

Not only that, but the words "business" or "requirements" do not even appear anywhere in CS curricula I have searched. Only under the title "problem solving" could one guess that it refers to business. This is not to say CS education per se is bad; it just isn't a comfortable fit to the current computing world although it is gradually finding a niche in various areas of computing. These areas include big data, data science, cognitive and similar computing, and cybersecurity.

However, a broader knowledge across key IT concepts and architectures is needed since no person in IT is an island and anyone totally specialized will find it difficult to cross-communicate where his/her field overlaps with another, particularly in meetings or presenting to the business.

What Are the Differences?

In this part of blog, I will try to demonstrate this CS vs. IT dichotomy but first some outside view of the differences between CS and IT:

The proposition I put to CS people as to what modern IT is goes roughly as follows:

■ IT needs to be presented as sequence of related activities within a framework, not a simple collection of topics.

The flow of IT projects can be represented as:
- Business idea/need
- Specification of business flow
- IT Architecture (product-free)
- Populate the design with Technology
- Code/Buy software
- Implement
- Manage
- Update
- Retire systems and Start again

(There will of course be reviews and the like throughout this sequence of activity.)

You can see "coding" in context here; students and teachers cannot see this far.

■ There should be a pragmatic, contextual "wrapping" around major topics, for example, "this is used in the oil industry to map the subsea strata in the search for oil deposits." – the "so what?" test.

■ Emphasize important aspects of IT as a framework in which to teach topics. Over the years I have decided that FUMPAS represent the key elements (others can be found within these):

FUNCTIONALITY
USABILITY
MANAGEABILITY
PERFORMANCE
AVAILABILITY
SECURITY.

These are the criteria to map onto any business IT project to whatever degree of detail (reflecting its importance) the business decides.

■ Two large topics totally absent from CS curricula are mainframes, their operating systems and high performance computing (HPC). Much of the world's financial work is done on mainframes and its influence is growing, believe it or not. HPC computing is now a big field and is expanding beyond pure science into medicine, financial modelling, AI and other power hungry areas. Not to even mention them is dereliction of IT teaching duty, whatever the syllabus mandates. This sort of add-on could be done by selection of a suitable reading list, even if it is not in the syllabus.

CS school and university syllabuses I have studied do not fit the "real world" IT scene in breadth, depth or velocity of change and I therefore generated a keyword list to demonstrate this dichotomy. The list then developed into a learning Glossary, now on Amazon Kindle (check tomorrow for Part 2 of this blog), to show where IT fits in the business world and the topics which make it tick. The CS world can then see if their output matches these requirements.

So what? The world has gone mad on the "digital revolution" impacting nearly all business. I believe this issue needs to be addressed vigorously and quickly to tackle the much discussed "IT skills shortage." The current computer education, at least in the UK, will not achieve this aim, still less cater for the skills needs post-Brexit. I see no difference between UK CS and US CS, ergo much of what I say also applies the US.

Finally, I cannot find a syllabus anywhere I have looked that remotely covers IT as demonstrated by the list and subsequently the Glossary. I see this as a start in resolving the "IT Skills issue," a mantra that has been trotted out since the year 2000, if not earlier.

As Mark Twain said; "Everybody is talking about the weather, nobody is doing anything about it." I hope the Glossary is a beginning.

Read Computer Science (CS) and Information Technology (IT): Part 2

Hot Topics

The Latest

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...