F201 TA1: The Scope of Managing Big Data

1 State three characteristics of big data. 3

One mark for each of the following, to a maximum of 3.

• One valid characteristic
• A second, different characteristic
• A third, different characteristic

Model answer. Volume, velocity and variety.

2 Explain why high velocity data requires a different approach from traditional batch processing. 4

One mark for each of the following, to a maximum of 4.

• Velocity means data arrives continuously
• Batch processing runs on a schedule, so results are late
• Stream processing handles each event as it arrives
• A consequence or example where the delay matters

Model answer. High velocity means the data arrives continuously rather than as a file at a fixed time, so there is no natural point at which a complete set exists to be processed. Traditional batch processing runs on a schedule, typically overnight, which means every result is as old as the last run; for data arriving every second that delay is the whole problem rather than an inconvenience. Stream processing handles each event as it arrives, applying rules or a model in flight and acting within milliseconds. The difference matters wherever the value of the answer decays: a fraudulent card transaction has already been authorised and settled by the time a nightly batch notices it, and a vibration pattern indicating a bearing failure is useful before the bearing fails, not the next morning.

3 A supermarket chain is using big data from loyalty cards, till transactions, weather feeds and its supply chain. Explain three benefits this could bring and one risk it creates. 8

One mark for each of the following, to a maximum of 8.

• A benefit relating to demand forecasting or stock
• It is explained with the mechanism
• A benefit relating to personalisation or marketing
• It is explained with the mechanism
• A third benefit, such as pricing, layout or efficiency
• It is explained with the mechanism
• A genuine risk is identified
• The risk is explained and a mitigation or judgement offered

Model answer. The first benefit is demand forecasting. Joining till data to weather feeds lets the chain predict, store by store, that a forecast of 24 degrees on a Friday raises barbecue and salad sales by a known proportion, so orders are placed before demand arrives rather than after; that cuts both empty shelves and waste on short-life products, which is where a supermarket's margin actually sits. The second is personalisation. Loyalty data shows what each household buys and how often, so offers can be targeted at products a customer already buys or is likely to switch to, which gives a far higher redemption rate than a uniform discount and costs less in lost margin. The third is operational: combining transaction data with supply chain data identifies which lines sell in which stores at which times, which informs range decisions, shelf layout and staffing, and lets distribution be planned against real rather than assumed demand. The risk that comes with all of this is privacy, and it is concentrated precisely where the value is. Individually, a weather feed and a basket are innocuous; joined to a loyalty card they produce a detailed and sensitive record of a household, capable of revealing pregnancy, illness, religious observance and financial difficulty as inferences the customer never disclosed. That creates both a legal exposure, since inferred health information is special category data and the processing must be transparent and minimised, and a trust exposure, because customers who feel watched stop using the card and the data disappears. The chain should therefore be explicit in its privacy notice about what it infers and why, hold the minimum it needs with a real retention period, keep identified data tightly access controlled, and avoid using inferences in ways a customer would find intrusive even where doing so would be lawful.