The impact of advances in technology: formula sheet

Key formulas

The 3 Vs of big data

Big data = Volume + Velocity + Variety

Volume is amount, velocity is speed, variety is different forms (structured and unstructured). Veracity and value are sometimes added as extra Vs.

Types of data analytics

Descriptive (what happened) → Diagnostic (why) → Predictive (what will happen) → Prescriptive (what to do)

Each type moves from looking back to guiding action. Use the question's wording to choose the right one.

Data to information

Data + processing and context = Information

Analytics turns data into information that supports decisions.

IaaS

Provider supplies infrastructure; you manage OS and applications

Think: you rent the hardware. Examples: virtual servers and storage.

PaaS

Provider supplies infrastructure and platform; you manage your applications

Think: you rent a development and hosting environment.

SaaS

Provider supplies the complete application; you just use it

Think: you rent the finished software, usually through a browser.

Blockchain features

Distributed + linked blocks + consensus + very hard to alter

Use these four ideas to describe it in any answer.

Cloud deployment models

Public (shared) | Private (one organisation) | Hybrid (mix)

Choose by balancing cost against control and security.

Answer structure

Define → Benefit → Risk → Apply to scenario

A reliable pattern for written-style and multiple response items.

Risk-control matching rule

Threat → Preventive control + Detective control + Corrective control

For any threat, give at least one control of each type if the question allows.

Data protection principles (typical)

Lawful and fair → Specific purpose → Adequate and accurate → Kept no longer than needed → Secure

Based on common laws such as GDPR. Wording varies by country.

Information security aims (CIA)

Confidentiality + Integrity + Availability

Confidentiality: only authorised people see data. Integrity: data is accurate and unaltered. Availability: data is accessible when needed.

Data security vs data protection

Data security = protecting data; Data protection = lawful handling of personal data

Security is a means. Protection is a legal and rights-based duty.

Quick revision

• Automation uses technology to carry out routine tasks with less human input, usually improving speed, consistency and cost.
• Automation can reduce errors in repeated tasks but needs set-up cost, maintenance and suitable controls.
• Big data is described by high volume, velocity and variety of data, and many sources also add veracity and value.
• Data analytics means examining data to find patterns that support decisions.
• Cloud computing delivers storage, software or computing over the internet instead of from the organisation's own equipment.
• Cloud brings flexibility and lower upfront cost but creates reliance on the provider and raises data location and security questions.
• Match each emerging technology to its main purpose before looking at the answer options.
• Cyber security protects systems, networks and data from unauthorised access, damage or theft.
• Data protection rules focus on how personal data is collected, used, stored and kept accurate.
• Typical controls include passwords, access limits, encryption, firewalls, backups and staff training.
• In scenario questions, name the technology, the benefit, then the risk or control the question asks for.

Common mistakes

• Treating RPA and AI as the same thing. Fix: Remember: RPA follows fixed rules; AI learns from data and can adapt.
• Saying automation removes all errors. Fix: Say it reduces human error. A bot with wrong rules or poor data repeats the same mistake at speed.
• Treating big data and data analytics as the same thing. Fix: Remember: big data is the data itself; analytics is the examination of data to support decisions.
• Confusing velocity with volume. Fix: Volume is how much. Velocity is how fast it arrives and must be processed.
• Mixing up IaaS, PaaS and SaaS. Fix: Remember the customer's view: hardware (IaaS), build platform (PaaS), finished software (SaaS).
• Saying blockchain guarantees that transactions are true. Fix: Say it makes records tamper-resistant. Wrong or fraudulent data entered at the start still stays recorded.
• Treating data protection and data security as the same thing. Fix: Link data protection to law and personal data rights. Link data security to technical and organisational safeguards.
• Choosing antivirus software for every threat. Fix: Match the control to the threat. Phishing needs training and filtering. Ransomware recovery needs backups. Insider misuse needs access rights and logs.

Exam tips

• Always link benefits and risks to the scenario given; generic lists earn fewer marks.
• Know the one-line difference between RPA (rules) and AI (learning) for quick objective test answers.
• In multiple response questions, read the stated number of answers to select and avoid absolute words like 'always' or 'eliminates'.
• Cover the people side: jobs, skills and morale are commonly tested.
• Link technology to the finance role: less data entry, more analysis, but controls still needed.
• Learn the 3 Vs as a set and be able to give a one-line example of each; objective tests often ask you to match a scenario to a V.
• Always separate big data (the data) from data analytics (the use of it) when choosing between similar options.
• For finance-use questions, tie analytics to a named task such as forecasting, fraud detection or customer profitability.