IB Business Management HLTopic 5 — Operations ManagementPaper 1 & 2Core idea~10 min read
The Language of Information Systems
This part of the course is mostly vocabulary. The examiner is not testing whether you can build a database — only whether you can define these terms correctly and say what a business actually gains from each one. Learn the definitions precisely and this becomes one of the easiest topics in the unit.
📚 What you need to know
Data analytics turns raw data into useful information by finding patterns, trends and relationships.
There are four types: descriptive, diagnostic, predictive and prescriptive.
A database is a structure for storing data so it can be entered, protected and retrieved quickly.
Critical infrastructure means the IT systems a business depends on: data centres, cloud computing and artificial neural networks.
Virtual reality, the Internet of Things and artificial intelligence each have specific business uses you should be able to name.
Every one of these costs money and creates dependency, so none of them is automatically a good idea.
Data analytics
Data analytics is the process of turning raw data into information a manager can act on. A supermarket till produces millions of numbers a week; on their own they are useless. Analytics is what turns them into “sales of a product fall 30% when it is moved off the end of the aisle”.
The reason businesses invest in it is simple: decisions based on evidence are less risky than decisions based on a hunch. That does not make them right, but it makes them defensible and repeatable.
Exam questions usually give you an example and ask you to name the type. Match it to the question being answered: what, why, what next, or what should we do.
Why data on its own is worth nothing
This ladder is why hiring analysts often matters more than buying another system. Most firms already hold more data than they use.
Databases
A database gives stored data a structure, so it can be entered, kept secure and pulled back out quickly. A university, for example, runs one system for student records, another for timetables, another for library loans, another for staff and payroll — and increasingly links them so a single change updates everywhere.
The catch is cost. Databases need setting up, securing and maintaining, often by specialist staff. For a small business that is a real expense, and a badly maintained database is worse than a spreadsheet because everyone assumes it is correct.
Critical infrastructure
Critical infrastructure means the IT systems and facilities a business genuinely cannot operate without. Three appear in the syllabus.
Data centres are physical buildings holding a business’s servers, storage and networking equipment. They provide backup, security, email, file sharing and database services.
Cloud computing does the same job but the applications and data sit on remote servers accessed over the internet, rather than in a building the firm owns. It converts a large fixed cost into a running cost and scales up easily — at the price of depending on someone else’s uptime.
Artificial neural networks are computer systems loosely modelled on the brain, which learn patterns from data. Businesses use them to spot unusual patterns that suggest fraud, to predict when a machine is about to fail so it can be serviced first, and to power chatbots that answer routine customer questions.
The phrase “critical infrastructure” is a warning as much as a description. If the business cannot run without it, then a failure of it is not an inconvenience — it is the crisis you read about two pages ago.
Virtual reality, the Internet of Things and AI
Technology
What it is
How businesses use it
Virtual reality
A realistic three-dimensional environment created in software that users can explore
Training staff in risky situations safely; letting customers view products before buying; testing prototype designs before anything is built
Internet of Things
Everyday objects connected to the internet, sending and receiving data with little human involvement
Sensors that report when a machine needs servicing; connected fridges monitoring temperature; tracking goods through a supply chain
Artificial intelligence
Machines completing tasks that would normally need human intelligence or judgement
Link every technology to a business benefit. “The firm uses VR” scores nothing. “The firm uses VR to train warehouse staff on the forklift without risking an accident, which cuts training costs and injury claims” scores properly.
Worked examples
WORKED EXAMPLE 1
A retailer analyses last year’s sales to see which weeks were busiest, then models what staffing it will need next December. Identify the two types of analytics being used. [2]
Step 1: the first activity
Looking at what already happened, with no explanation and no forecast.
Descriptive analyticsStep 2: the second activity
Estimating a future requirement from past patterns.
Predictive analyticsIf the system had also recommended a specific number of staff to hire, that final step would be prescriptive.
WORKED EXAMPLE 2
A furniture retailer is considering moving its systems to the cloud. Explain one advantage and one drawback. [4]
Advantage: cost and flexibility
It avoids buying and housing its own servers, turning a large upfront investment into a monthly cost, and capacity can be increased quickly during busy sale periods.
Drawback: dependency
The retailer no longer controls its own systems. An outage at the provider, or a price increase at renewal, is something the business must simply absorb.
The trade is lower cost and easier scaling against reduced control“Reduced control” is the phrase to reach for whenever a firm hands any system to an outside provider.
💡 Exam tip
Define precisely. Many marks here are for a clean definition, so learn the wording rather than paraphrasing loosely.
Match the analytics type to its question. What, why, what next, what should we do.
Give every technology a purpose. Name the business problem it solves, not just the technology.
Mention the cost side. Setup, training, maintenance and dependency all belong in an evaluation.
Use up-to-date examples if you have them. This topic moves fast and a current example reads well.
⚠️ Common mix-up
Data is not information. Data is raw; information has been organised and given meaning.
Descriptive is not diagnostic. One says what happened, the other says why.
Cloud computing is not a data centre. The cloud runs on data centres, but somebody else’s.
The Internet of Things is not just phones and laptops. It means everyday objects that were never computers.
More data does not mean better decisions. Poor quality data produces confident, wrong answers.
Up next: Storing, Using and Losing Data — what businesses actually do with all this information, and what happens when it falls into the wrong hands.
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