Computer Science · Big Data

A library with ten million books isn't big data. Here's what actually is.

Big data questions turn up across every one of NZQA's past 91908 papers, built around real platforms and real datasets — Google's COVID-19 mobility reports, TikTok, X. These five topics work through the ideas those papers actually examine, each ending with a real past-paper question so you can see exactly how the ideas get tested.

AS91908 · 3 credits · external digital exam·5 topics·Past-paper practice throughout

Executive summary

Start here. Read the topics in order — later topics build on vocabulary from earlier ones. Attempt every past-paper question yourself before revealing the "how to answer well" notes, and keep a running glossary as you go.

Topic	What it covers
1. Characteristics of Big Data	Volume, variety, velocity — and why "big" isn't just "a lot"
2. Generation, Processing & Formats	Where data comes from, structured vs unstructured, and preparing it for analysis
3. Interpretation, Bias & Display	Reading data critically and choosing how to show it
4. Tools, Technologies & AI	How systems actually store, process and find patterns in huge datasets
5. Privacy, Ethics & Governance	Who benefits, who is at risk, and who decides

What's assessed

AS91908 is an online digital examination with a printed resource booklet. You write short and/or extended answers totalling 800–1500 words on ONE area you choose in the exam room — this unit is that preparation for the Big Data option specifically. Per the specification, questions may cover:

• Characteristics of big data (volume, variety, velocity, etc.)
• Generation, processing and analysing data in different formats
• Interpretation and representation (bias and display)
• Tools and technologies used in big data
• Considerations: privacy, ethics, and data governance

You'll also be expected to apply this understanding to an unfamiliar context in the exam, and to spot where big data overlaps with other computer science areas — most often artificial intelligence.

Don't confuse this with AS91909

The specification document covering this unit (91909-spc-2026.pdf) covers both 91908 and 91909 on the same pages. AS91909 ("Present a reflective analysis of developing a digital outcome") is a different standard about your own project work — it is not part of this unit and is not covered here.

How to use these notes

Each topic explains a mechanism or concept, then works through a past-paper-style question so you can see how the ideas are actually examined. NZQA hasn't released marking schedules for these papers, so treat the worked examples as guidance on structure and depth — not a guaranteed mark scheme.

A strong 91908 answer usually does three things:

1. Explains a mechanism accurately — in your own words, using the correct vocabulary
2. Connects it to a specific effect on people — not "society" in the abstract, but a named group
3. Reaches a justified conclusion or comparison — especially for Merit/Excellence-level questions ("discuss", "critically analyse")

Sources used in this unit

91909-spc-2026.pdf (the 2026 assessment specification), 91908-exm-2021.pdf and its resource booklet 91908-res-2021.pdf (Question Three, Big Data, including Google's COVID-19 Community Mobility Report), 91908-cat-A-2023.pdf (Question Two, Big Data, using TikTok) and 91908-cat-B-2023.pdf (Question Two, Big Data, using X). No official marking schedules were available for these papers, and the 2023 resource booklet (referenced but not supplied) is not reproduced here — only the question text and what it implies.

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