IB Business Management HLTopic 5 — Operations ManagementPaper 1 & 2HL only~11 min read
Storing, Using and Losing Data
Businesses now hold more information about their customers and their own staff than ever before. That data is genuinely valuable — and it is also a liability, because everything a firm collects is something it can lose, misuse or be sued over. This page covers both halves of that bargain.
📚 What you need to know
Big data is the very large volume of data a business gathers day to day, from sales, apps, devices and social media.
Loyalty programmes are a deliberate trade: discounts for the customer, detailed spending data for the business.
Data mining extracts patterns from large data sets and turns them into decisions.
Digital Taylorism uses technology to monitor how employees work, often linking pay to measured performance.
Cybercrime ranges from phishing and ransomware to identity theft and denial-of-service attacks.
Concerns include privacy, data breaches, bias, data quality, dependency and environmental impact.
Big data and where it comes from
Big data is not just “a lot of data”. It is data arriving continuously, in large volumes, from many different sources at once, which is why it needs specialist tools to handle.
Source
What the business learns
E-commerce
What was bought, what was searched for and abandoned, what customers browse but never buy
Social media
What people say about the brand, which products are talked about, where complaints cluster
Connected devices
How products are actually used, and when equipment is about to fail
Logistics tracking
Where delays happen in delivery, and which routes cost most
Loyalty schemes
Individual spending habits over years, linked to a real identity
Loyalty programmes: the exchange
A loyalty card looks like a discount scheme. Commercially, it is a data collection scheme that pays customers to identify themselves at the till. In return the business gets financial data (what was bought, how it was paid for), interaction data (survey responses, in-store behaviour) and marketing data (which emails were opened, which adverts led to a purchase).
The benefits are real on both sides. Customers get discounts and rewards and feel recognised. The business gets repeat purchases, cheaper promotion because it no longer has to chase new customers so hard, and personalisation that competitors without the data cannot match.
The drawbacks are equally real. Running a scheme costs money, especially for a small firm. Customers can come to expect the discount and feel cheated without it. Too many schemes and people stop caring. And every record stored is a record that can be leaked.
Data mining
The last line is the whole ethical problem in one sentence. Joining data sets makes the analysis more powerful and the privacy risk far larger at the same time.
Businesses use mined data to plan marketing and target the right segments, forecast sales and set budgets, profile customers by demographics, tailor loyalty rewards, spot which products sell together, guide research spending and plan production around real demand patterns.
The criticisms
Invasion of privacy. Large-scale collection of personal data makes people uncomfortable, particularly when they never clearly agreed to it.
Data breaches. The more you store, the more there is to lose. Financial and health details are especially damaging when exposed.
Discrimination. Decisions based on mined data can unintentionally disadvantage particular groups, because the model reflects patterns in past data that may themselves be unfair.
Digital Taylorism
Digital Taylorism is the use of technology to monitor closely how employees work — tracking keystrokes, timing tasks, recording calls, following delivery drivers by GPS — and often linking pay or discipline to what is measured.
The business case is straightforward. Managers can staff shifts accurately, spot poor performance quickly, identify training gaps from real recorded interactions, reward the strongest performers with evidence rather than impressions, and spend less time directly supervising.
The human cost is equally straightforward. Constant surveillance raises stress and lowers trust. Workers judged by metrics start optimising the metric rather than the job. And if the measure is crude, it punishes people for things outside their control — a driver stuck in traffic looks identical to a driver who is slow.
This is the topic where your own opinion is welcome. Ask yourself how you would work if every keystroke were logged. Under Herzberg, close monitoring attacks recognition and responsibility, which are the motivators — so the same system that raises measured output can lower real effort.
Cybersecurity and cybercrime
Cybersecurity means the systems that protect networks and data from theft or unauthorised access. Cybercrime is illegal activity carried out using computers or networks, usually for financial gain.
Form
What happens
Phishing
Someone poses as a trusted organisation to trick a person into handing over passwords or card details
Malware and ransomware
Harmful software damages or takes over systems; ransomware locks the firm’s own files until a payment is made
Identity theft
Personal details are stolen and used to commit fraud in someone else’s name
Online fraud
Fake shops, fake invoices and fake investments designed to take money directly
Intellectual property theft
Designs, code or copyrighted material are copied and used without permission
Denial-of-service attacks
A site is flooded with traffic until it becomes slow or unavailable to real customers
Notice the bottom layer. A tested backup is what turns a ransomware attack from a catastrophe into an expensive weekend.
Weighing up technology overall
Positive impacts
Legal, ethical and practical concerns
Decisions based on real-time evidence rather than guesswork
Breaches expose sensitive data and cause financial and reputational damage
Automation raises efficiency and reduces human error
Algorithms can carry hidden bias and produce discriminatory outcomes
Faster communication and collaboration across sites
Rapid change creates skill gaps and resistance among staff
Personalised service and quicker customer support
Poor quality data produces confident but wrong decisions
Supply chains tracked in real time, so problems are caught early
Heavy dependence on systems and third-party providers that can fail
New products and faster adaptation to market changes
Energy use of data centres and disposal of old equipment harm the environment
The phrase to remember. “Garbage in, garbage out.” A sophisticated system fed inaccurate data does not produce cautious answers — it produces confident wrong ones, which is more dangerous than no system at all.
Worked examples
WORKED EXAMPLE 1
A supermarket chain is launching a loyalty app. Explain one benefit to the business and one concern for customers. [4]
Benefit to the business
Every purchase becomes linked to an identified shopper, so the chain can see which offers actually change behaviour and stop spending on the ones that do not.
Concern for customers
A detailed record of what an individual buys, when and where is stored indefinitely, and a single breach exposes all of it at once.
The exchange is cheaper, better-targeted marketing for the firm against a permanent privacy risk for the shopperFraming it as an exchange, rather than as good or bad, is what earns the analysis marks.
WORKED EXAMPLE 2
A delivery firm plans to track drivers’ routes, speed and break times, and link bonuses to the data. Evaluate this proposal. [10]
Step 1: the operational case
Route data identifies slow sections and cuts fuel and time. Objective figures make bonuses fairer than a manager’s impression, and drivers who are genuinely struggling can be given training.
Step 2: the human cost
Constant monitoring lowers trust and raises stress. Linking pay to speed creates a direct incentive to drive dangerously and skip breaks, which is a safety and legal risk for the firm.
Step 3: the measurement problem
The metric cannot separate a slow driver from heavy traffic, so some drivers will be punished for something they did not control.
Recommend collecting the route data to improve planning, but not linking individual bonuses to speedJudgement
Use the data on the process, not on the person — that keeps the efficiency gain without creating the unsafe incentive.
Separating “monitor the system” from “monitor the individual” is the strongest distinction available in this topic.
💡 Exam tip
Treat data as an asset and a liability. Every answer here has two sides, and saying so is the fastest route to evaluation marks.
Name the stakeholder. Customers, employees and shareholders experience the same technology completely differently.
Use Unit 2 motivation theory when discussing digital Taylorism. It gives the human argument some structure.
Say that cybersecurity is mostly about people. Training is cheaper and more effective than most software.
Do not forget the environment. Data centres use a lot of energy, and old equipment has to go somewhere.
⚠️ Common mix-up
Cybersecurity is not cybercrime. One is the defence, the other is the attack.
Big data is not just a large spreadsheet. It is the volume, speed and variety together that make it different.
Data mining is not data collection. Mining is finding the patterns in data already held.
Monitoring is not motivating. It may raise measured output while lowering genuine effort.
Following the law is not the same as being ethical. A firm can comply fully and still lose customers’ trust.
That completes Operations Management. Go back to What Operations Management Is For and work forwards: if you can explain the input-output model, the production methods, break-even and stock control without notes, you are ready for the paper.
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