← VCE Data Analytics U4O2

Assessing data against the six integrity characteristics

How trustworthy data is. Test it against six characteristics, one at a time: accuracy, authenticity, correctness, reasonableness, relevance and timeliness. Failing any one means it can't be trusted.

Part of: VCE Data Analytics - U4O2 - Cyber Security: Data Security — Module 26, ACARA Digital Technologies Years 9–10 — Acquiring and Validating Data — Module 89, QCE Digital Solutions Unit 4 — Complex Digital Data Exchange Problems and Solution Requirements — Module 67

Understanding Assessing data against the six integrity characteristics in VCE Data Analytics U4O2

What it does: Bakery sales data is the wrong data for hardware stock planning, so it fails relevance. The source cannot be verified, so the data may not be genuine and fails authenticity. Reasonableness asks if a value is sensible. An age of 212 is impossible, so this data should not be trusted until it is fixed. Relevance means the data suits the purpose. Pizza preferences can't help choose fitness classes, even if the data is otherwise perfect. Correctness checks that data is in the right form and valid for its field. A postcode must be a number, not a suburb or city name.

Common mistakes: An age of 250 is not a believable value for a person, so it fails the reasonableness test. Line 2 should be 'test: reasonableness'. Timeliness is about how up to date data is. The record says 12 but the real shelf count is 9, so the data does not match the real world. It fails accuracy. Line 3 should say FAIL.

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This module's key terms are also in a printable crossword (Modules 26–43) and word search (Modules 1–43).

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Learning
Duo
A school database lists a Year 10 student's age as 212. Which characteristic has the data failed?
Timeliness, because the age is out of dateReasonableness, because 212 is not a sensible age
Matches
A student says: The spelling matches the form, so the data is accurate. It passes all six characteristics
↓
Passing one characteristic does not prove integrity. Each of the six must be tested one at a time using evidence from the scenario
Applying
Meanings
Term
data integrity
Meaning
How trustworthy data is. Test it against six characteristics, one at a time: accuracy, authenticity, correctness, reasonableness, relevance and timeliness. Failing any one means it can't be trusted.
Practice
Accuracy: does it match the real value?
Testing
Gaps
failed ← ""
Check Answer
Spots
1record: Age = 250
2test: timeliness

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Quick Questions

1. A school database lists a Year 10 student's age as 212. Which characteristic has the data failed?
A. Reasonableness, because 212 is not a sensible age ✓
B. Timeliness, because the age is out of date
Reasonableness asks if a value is sensible. An age of 212 is impossible, so this data should not be trusted until it is fixed.
2. A manager receives a sales spreadsheet from an email address that only looks like the supplier's. Which characteristic is most in doubt?
A. Authenticity, because the source can't be verified as genuine ✓
B. Relevance, because the file is a spreadsheet
Authenticity is about whether data comes from its genuine, trusted source. A spoofed sender is evidence the source may not be real.
3. A café sets this month's budget using supplier prices from 2019. Which characteristic has failed?
A. Timeliness, because the data is out of date for the decision ✓
B. Authenticity, because the prices came from a supplier
Timeliness means data is current enough for its purpose. Old prices lead to poor decisions, so the data can't be trusted for this use.
4. A gym analyses members' favourite pizza toppings to decide which new fitness classes to run. Which characteristic has failed?
A. Correctness, because pizza toppings are spelt wrongly
B. Relevance, because the data doesn't relate to the decision ✓
Relevance means the data suits the purpose. Pizza preferences can't help choose fitness classes, even if the data is otherwise perfect.
5. The postcode field in a customer record contains the word 'Melbourne'. Which characteristic has failed?
A. Timeliness, because the customer may have moved
B. Correctness, because the value is not in the valid postcode form ✓
Correctness checks that data is in the right form and valid for its field. A postcode must be a number, not a suburb or city name.

Match the Pairs

Student age recorded as 214 → Fails reasonableness: the value is not believable for a student, so the data should not be trusted
Customer address last updated in 2015 → Fails timeliness: the data is out of date, so it should not be used for current decisions
Testing relevance: does the data suit the purpose? A scenario using it for the wrong purpose fails this test → Using sales data from a bakery to plan stock for a hardware store
Testing authenticity: is the data from a genuine, trusted source? A scenario with an unverified source fails this test → Survey results emailed from an unknown sender with no way to verify them
A student says: The spelling matches the form, so the data is accurate. It passes all six characteristics → Passing one characteristic does not prove integrity. Each of the six must be tested one at a time using evidence from the scenario
An analyst finds data that fails timeliness and says: It fails one test, but we can still trust it → Failing any one characteristic means the data has low integrity and should not be trusted

Key Term

data integrity
How trustworthy data is. Test it against six characteristics, one at a time: accuracy, authenticity, correctness, reasonableness, relevance and timeliness. Failing any one means it can't be trusted.

Fill in the Blanks

1. IF age = 250 THEN failed ← "reasonableness"
Hint: Type the characteristic that fails when a value is impossible or unlikely for the real world.
2. IF lastUpdated < "2020-01-01" THEN failed ← "timeliness"
Hint: Type the characteristic that fails when data is out of date for its use.
3. IF sourceVerified = FALSE THEN failed ← "authenticity"
Hint: Type the characteristic that fails when the data's origin cannot be confirmed as genuine.
4. IF heightRecorded <> heightMeasured THEN failed ← "accuracy"
Hint: Type the characteristic that fails when a stored value does not match the true value.
5. failedCount ← 1 IF failedCount > 0 THEN trust ← FALSE
Hint: Type the Boolean value showing data failing any characteristic should not be trusted.

Spot the Error

Question 1
A record: Age = 250
B test: timeliness
C result: FAIL
D action: do not trust data
Line B — An age of 250 is not a believable value for a person, so it fails the reasonableness test. Line 2 should be 'test: reasonableness'. Timeliness is about how up to date data is.
Question 2
A record: stock count = 12
B shelf count = 9
C test: accuracy -> PASS
D action: do not trust data
Line C — The record says 12 but the real shelf count is 9, so the data does not match the real world. It fails accuracy. Line 3 should say FAIL.
Question 3
A record: price list dated 2019
B today: 2025
C test: authenticity -> FAIL
D action: do not trust data
Line C — A price list from 2019 used in 2025 is out of date. This is a timeliness failure, not an authenticity failure. Line 3 should be 'test: timeliness -> FAIL'.
Question 4
A task: plan canteen menu
B data: student lunch orders
C test: relevance -> FAIL
D action: do not trust data
Line B — Shoe sizes have nothing to do with planning a menu, so the data fails relevance. Lunch orders would be relevant, so line 2 would not match the FAIL result. Line 2 should be 'data: student shoe sizes'.
Question 5
A email: bank asks for password
B sender: bank-secure.xyz
C real domain: bank.com.au
D test: authenticity -> PASS
E action: do not trust data
Line D — The sender domain does not match the real bank's domain, so the email is not from the genuine source. It fails authenticity. Line 4 should say FAIL.

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