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

Are they one and the same thing?
Terry Critchley

Part 1 of this blog dealt briefly with the differences between CS and IT in their applicability to computing needs in today's workplace. This was the reason I developed a list of topic names, which represent the modern IT, for comparison with practically any CS course curriculum worldwide.

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

This I expanded into a Glossary, aimed at introducing topics and indicating where simple further reading can be found, aiming it much more than a simple expansion of acronyms or 1-line description. Thus, I feel it is useful as a learning tool and a reference document.

View a sample list of Glossary topics at the Glossary link(173 pages):

I have added a small additional selection here dealing with more than just the letter "a" to show its scope and depth:

bi-modal IT

Bimodal IT is the state where organizations need two speeds of IT — often called traditional IT (business as usual) and agile IT (Gartner calls them mode-1 and mode-2). Traditional IT is focused on "doing IT right", with a strong emphasis on efficiency and safety, approval-based governance and price-for-performance.

Agile IT is focused on accelerated development and implementation, supporting prototyping and iterative development, rapid delivery, continuous and process-based methodology. Its role is to support and add value to business initiatives to generate value and/or revenue earlier that would otherwise be the case.

business continuity planning (BCP)

Sometimes used in place of BCM although some people may interpret them differently. In essence they both mean "keeping the business show on the road" when disaster strikes the IT setup and is much broader than IT disaster recovery. It includes, for example, temporary premises for displaced staff, transport activities and other non-IT considerations after the event.

Related links:
Business continuity planning
Business continuity planning steps to keep your organization running
High Availability IT Services

converged infrastructure (CI)

"Converged infrastructure, sometimes known as converged architecture, is an approach to data centre management that packages compute, networking, servers, storage and virtualization tools on a prequalified turnkey appliance. Converged systems include a toolkit of management software."

Converged infrastructure is an approach to data centre management that seeks to minimize compatibility issues between servers, storage systems and network devices while also reducing costs for cabling, cooling, power and floor space. A converged infrastructure can be implemented with a CI reference architecture, with standalone appliances or with a software driven hyper-converged approach.

A CI is the IT equivalent of an off-the-peg suit from a tailor, that is, a premade suit but not made to measure for a particular person. It is unlikely that the vendor will supply such a ready-made suit with a 50 in. chest coat and trousers of 22 in. length, unless there is big demand for such a very odd requirement or he wants to go out of business. Vendor longevity and trustworthiness are required here.

Related links:
Converged Infrastructure

DevOps

DevOps is a term emerged from the combination of Development and Operations. The role of a DevOps engineer is to automate all the operational work in the way that a developer would do. The idea is to encourage frequent releases to increase quality and get early feedback. See the entries "continuous delivery" and "continuous testing" (q.v.).

DevOps is not a technology but a way of doing things, or methodology, according to Webtorials.

"Hence, according to me [author of the article below; Nilesh Kanawade], the main two objectives of DevOps are increasing the speed and quality of the deliveries."

Related links:
DevOps Management: New Teams, New Processes
What is DevOps?
10 Things Your CIO Should Know About DevOps
DevOps Glossary

encryption algorithms

There are lots of encryption algorithms and NIST has approved some of them: Advanced Encryption Standard (AES), TripleDES, and Skipjack. On the asymmetric side, approved algorithms are DSA, RSA and ECDSA [NIST, 2013].

Related links:
AES
Triple-DES
Triple Data Encryption Algorithm (TDEA)
DSA [digital signature]
RSA
ECDSA
Skipjack
NIST Encryption Algorithm Approval Source

erasure codes

"Erasure coding (EC) is a method of data protection in which data is broken into fragments, expanded and encoded with redundant data pieces and stored across a set of different locations or storage media." [see link below]

One key point about erase coding is its ability to recover from corrupted storage faster than RAID configurations, especially involving very large data sets (databases etc.) and storage volumes.

Related links:
Erasure Coding
Erasure Codes for Storage Systems
Erasure Coding vs. RAID
Erasure Coding vs. RAID

intent based networking (IBN)

Although not a totally new concept, IBN has been pushed by Cisco as a way forward in function-effective and cost-effective networking. It is a subset of automated or even autonomic (q.v.) computing where intelligence is used to monitor and manage resources.

Related links:
What is intent based networking

There are also articles by Cisco and Gartner, the latter with risk and other cautionary factors about IBN.
Cisco DNA
FAQ

I hope I have made my "CS is not IT" point without malice and introduced the discerning reader to a source on information on "modern computing" aka "workplace IT." Until this nettle is grasped, bodies will continue to imagine that boosting CS teaching will solve the IT Skills shortage. It will not.

The UK Government has recently shelled out £ 100 m. to increase the UK "digital skills" … to academia, in the form of five Universities' CS faculties! I have asked each one of the five what their planned topics are and received one reply – they are doing 2 very specific, very narrow topics which I believe will solve little of the problem.

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

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 2

Are they one and the same thing?
Terry Critchley

Part 1 of this blog dealt briefly with the differences between CS and IT in their applicability to computing needs in today's workplace. This was the reason I developed a list of topic names, which represent the modern IT, for comparison with practically any CS course curriculum worldwide.

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

This I expanded into a Glossary, aimed at introducing topics and indicating where simple further reading can be found, aiming it much more than a simple expansion of acronyms or 1-line description. Thus, I feel it is useful as a learning tool and a reference document.

View a sample list of Glossary topics at the Glossary link(173 pages):

I have added a small additional selection here dealing with more than just the letter "a" to show its scope and depth:

bi-modal IT

Bimodal IT is the state where organizations need two speeds of IT — often called traditional IT (business as usual) and agile IT (Gartner calls them mode-1 and mode-2). Traditional IT is focused on "doing IT right", with a strong emphasis on efficiency and safety, approval-based governance and price-for-performance.

Agile IT is focused on accelerated development and implementation, supporting prototyping and iterative development, rapid delivery, continuous and process-based methodology. Its role is to support and add value to business initiatives to generate value and/or revenue earlier that would otherwise be the case.

business continuity planning (BCP)

Sometimes used in place of BCM although some people may interpret them differently. In essence they both mean "keeping the business show on the road" when disaster strikes the IT setup and is much broader than IT disaster recovery. It includes, for example, temporary premises for displaced staff, transport activities and other non-IT considerations after the event.

Related links:
Business continuity planning
Business continuity planning steps to keep your organization running
High Availability IT Services

converged infrastructure (CI)

"Converged infrastructure, sometimes known as converged architecture, is an approach to data centre management that packages compute, networking, servers, storage and virtualization tools on a prequalified turnkey appliance. Converged systems include a toolkit of management software."

Converged infrastructure is an approach to data centre management that seeks to minimize compatibility issues between servers, storage systems and network devices while also reducing costs for cabling, cooling, power and floor space. A converged infrastructure can be implemented with a CI reference architecture, with standalone appliances or with a software driven hyper-converged approach.

A CI is the IT equivalent of an off-the-peg suit from a tailor, that is, a premade suit but not made to measure for a particular person. It is unlikely that the vendor will supply such a ready-made suit with a 50 in. chest coat and trousers of 22 in. length, unless there is big demand for such a very odd requirement or he wants to go out of business. Vendor longevity and trustworthiness are required here.

Related links:
Converged Infrastructure

DevOps

DevOps is a term emerged from the combination of Development and Operations. The role of a DevOps engineer is to automate all the operational work in the way that a developer would do. The idea is to encourage frequent releases to increase quality and get early feedback. See the entries "continuous delivery" and "continuous testing" (q.v.).

DevOps is not a technology but a way of doing things, or methodology, according to Webtorials.

"Hence, according to me [author of the article below; Nilesh Kanawade], the main two objectives of DevOps are increasing the speed and quality of the deliveries."

Related links:
DevOps Management: New Teams, New Processes
What is DevOps?
10 Things Your CIO Should Know About DevOps
DevOps Glossary

encryption algorithms

There are lots of encryption algorithms and NIST has approved some of them: Advanced Encryption Standard (AES), TripleDES, and Skipjack. On the asymmetric side, approved algorithms are DSA, RSA and ECDSA [NIST, 2013].

Related links:
AES
Triple-DES
Triple Data Encryption Algorithm (TDEA)
DSA [digital signature]
RSA
ECDSA
Skipjack
NIST Encryption Algorithm Approval Source

erasure codes

"Erasure coding (EC) is a method of data protection in which data is broken into fragments, expanded and encoded with redundant data pieces and stored across a set of different locations or storage media." [see link below]

One key point about erase coding is its ability to recover from corrupted storage faster than RAID configurations, especially involving very large data sets (databases etc.) and storage volumes.

Related links:
Erasure Coding
Erasure Codes for Storage Systems
Erasure Coding vs. RAID
Erasure Coding vs. RAID

intent based networking (IBN)

Although not a totally new concept, IBN has been pushed by Cisco as a way forward in function-effective and cost-effective networking. It is a subset of automated or even autonomic (q.v.) computing where intelligence is used to monitor and manage resources.

Related links:
What is intent based networking

There are also articles by Cisco and Gartner, the latter with risk and other cautionary factors about IBN.
Cisco DNA
FAQ

I hope I have made my "CS is not IT" point without malice and introduced the discerning reader to a source on information on "modern computing" aka "workplace IT." Until this nettle is grasped, bodies will continue to imagine that boosting CS teaching will solve the IT Skills shortage. It will not.

The UK Government has recently shelled out £ 100 m. to increase the UK "digital skills" … to academia, in the form of five Universities' CS faculties! I have asked each one of the five what their planned topics are and received one reply – they are doing 2 very specific, very narrow topics which I believe will solve little of the problem.

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

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 ...