Finance transformation: formula sheet

Key formulas

The Vs of big data

Volume, Velocity, Variety, Veracity (sometimes Value)

Use them to describe the data in the scenario, not just to list them.

Four types of analytics

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

Each type adds more value but needs more skill and better data.

Data quality attributes

Accuracy, completeness, timeliness, consistency, validity, uniqueness

Use these to explain why a given data set may be unreliable.

Information security aims

Confidentiality, integrity, availability

Useful structure for security risks and controls.

Lewin's change model

Unfreeze → Change → Refreeze

Unfreeze by showing why change is needed, make the change, then embed it in new routines, controls and rewards.

Force field analysis

Driving forces vs Restraining forces

Strengthen drivers and, more usefully, reduce restrainers. Name forces that fit the case.

Kotter's eight steps (headline stages)

Urgency → Coalition → Vision → Communicate → Empower → Short-term wins → Consolidate → Anchor in culture

Use it as a checklist for a rollout plan, not as a list to recite.

Kotter and Schlesinger's approaches to resistance

Education, Participation, Facilitation and support, Negotiation, Manipulation, Coercion

Match the approach to the cause and the time available. Coercion and manipulation carry ethical and morale risks.

Benefits realisation test

Benefit = Owner + Measure + Baseline + Target + Date

A benefit without an owner and a measure is a hope, not a plan.

Project constraints

Scope, Time, Cost, Quality

Changing one usually affects the others. Use this to discuss trade-offs and delays.

Quick revision

• Finance transformation moves finance from processing and reporting toward decision support and business partnering.
• A transformed function spends less time on transactions and more on analysis, forecasting and advice.
• A shared service centre brings routine finance work from many units into one location to gain consistency and lower cost.
• Shared services can hurt local knowledge, flexibility and staff morale if poorly managed.
• Outsourcing transfers work to a third party; check cost, control, data security and dependence on the supplier.
• Automation suits high-volume, rule-based tasks, and frees staff for higher-value work.
• New technology still needs controls, security and reliable underlying data.
• Analytics helps forecasting, performance measurement and risk detection, but only if data quality is good.
• Big data is often described by volume, velocity and variety; question its accuracy and ethical use.
• Resistance to change is common; plan communication, training and involvement of key stakeholders.
• Link every recommendation to the scenario's strategy, costs, risks and people.
• Always give a clear conclusion; professional skills marks reward judgement and clear communication.

Common mistakes

• Treating finance transformation as only buying new software. Fix: Always cover people and process as well. Say that systems alone do not change behaviour or skills.
• Writing a generic list of benefits with no link to the scenario. Fix: Quote scenario facts, such as late reports or duplicated systems, and tie each benefit to them.
• Treating outsourcing, offshoring and shared services as the same thing. Fix: Define them in one line each. Outsourcing is about who does the work, offshoring is about where, and an SSC is an internal centralised unit.
• Listing generic benefits and risks with no link to the scenario. Fix: Tie every point to a fact, such as the number of business units, the current error rate or the location. Explain the effect on this business.
• Writing a textbook definition of cloud, RPA or AI with no link to the scenario. Fix: After every definition, add a sentence using a named process, figure or fact from the case.
• Treating all technologies as the same. Fix: Remember that RPA follows fixed rules, while AI learns and predicts. Say which one suits which task.
• Treating big data and data analytics as the same thing. Fix: State that big data is the data and analytics is the technique used to extract insight from it.
• Listing the Vs without applying them. Fix: Pick the Vs that matter in the scenario and show how each affects the decision or the risk.
• Writing a generic essay on Lewin or Kotter without using the case. Fix: Use the model as a lens. State a case fact, then the model point, then the action it supports.
• Treating finance transformation as only a technology project. Fix: Always cover people, process and governance. Ask who must change their behaviour for the benefit to appear.

Exam tips

• Always tie your answer to scenario facts. Generic lists earn few marks in SBL.
• Use the people, process and technology headings to structure points quickly.
• Include a balanced view of benefits and risks, since evaluation questions reward it.
• Show professional skills: write in the format asked, such as a briefing note, and address the named reader.
• Link finance changes to strategy, governance and stakeholder needs, not just efficiency.
• Show you can separate transactional, specialist and advisory work. This is what the examiner wants from a finance operating model answer.
• Define SSC, CoE, outsourcing and offshoring briefly, then spend your time applying them to the case.
• Balance benefits and risks, and always end with a justified recommendation. Marks go to judgement, not lists.