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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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AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...