← VCE Data Analytics U4O2

Consequence of diminished data integrity: poor decision-making

Decisions made from inaccurate or untimely data are likely to be wrong, causing wrong forecasts, misleading analysis and false conclusions.

Part of: VCE Data Analytics - U4O2 - Cyber Security: Data Security — Module 28, QCE Digital Solutions Unit 2 — Data-Driven Problems and Solution Requirements — Module 119, QCE Digital Solutions Unit 4 — Complex Digital Data Exchange Problems and Solution Requirements — Module 34

Understanding Consequence of diminished data integrity: poor decision-making in VCE Data Analytics U4O2

What it does: An old stock report no longer reflects reality, so the decision not to reorder rests on a false conclusion. A data entry error (2000 instead of 200) is carried into the forecast, making it ten times too high. Duplicated or inaccurate records inflate the numbers, so forecasts built on them are wrong and lead to poor decisions such as over-ordering. Careful analysis cannot fix bad inputs. If the data is inaccurate, the conclusions are likely to be wrong too. Errors in the data distort the results, so the analysis misleads the people who rely on it. Data with gaps or errors can lead to a false conclusion, and the school may then act on something that is not true.

Common mistakes: An average is the total divided by the count, so it needs / not *. Multiplying gives a huge wrong average, which leads to a wrong forecast. It should be avg = sum(sales) / len(sales). The warning should show when the data is older than the limit. Using < warns about fresh data and ignores stale data, so decisions would use untimely data. It should be if age > limit:.

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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 retailer forecasts next month's stock using sales figures that contain duplicate entries. What is the likely result?

A wrong forecast, so it over-orders stockA more accurate forecast, because there is more data

Matches

Using last year's customer survey to decide this year's product range without checking it is still current

↓

The data is untimely, so the decision may be wrong because customer needs have changed. Use current data.

Applying

Meanings

Term

Poor decision-making

Meaning

Decisions made from inaccurate or untimely data are likely to be wrong, causing wrong forecasts, misleading analysis and false conclusions.

Practice

=ROUND(AVERAGE(B2:B8),1)

Testing

Gaps

dataAccurate = FALSE THEN

Check Answer

Spots

1sales = [120, 135, 150]

2avg = sum(sales) * len(sales)

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Quick Questions

1. A retailer forecasts next month's stock using sales figures that contain duplicate entries. What is the likely result?

A. A more accurate forecast, because there is more data

B. A wrong forecast, so it over-orders stock ✓

Duplicated or inaccurate records inflate the numbers, so forecasts built on them are wrong and lead to poor decisions such as over-ordering.

2. Which statement about decisions made from inaccurate data is correct?

A. They are likely to be wrong ✓

B. They are right as long as the analysis is done carefully

Careful analysis cannot fix bad inputs. If the data is inaccurate, the conclusions are likely to be wrong too.

3. A manager uses last year's customer addresses to plan a local delivery service. Which integrity problem leads to a poor decision?

A. Untimely data: it is out of date ✓

B. Encrypted data: it is too secure to use

Outdated data no longer reflects reality, so decisions based on it, like where to deliver, are likely to be wrong.

4. An analyst's chart is built from data with many typing errors, and it shows a sales trend that is not real. This is an example of:

A. Effective data validation

B. Misleading analysis ✓

Errors in the data distort the results, so the analysis misleads the people who rely on it.

5. A school finds that its attendance data was incomplete and concludes that Year 9 students rarely miss class, when many do. This is a:

A. False conclusion ✓

B. Verified finding

Data with gaps or errors can lead to a false conclusion, and the school may then act on something that is not true.

Match the Pairs

Wrong forecast → A prediction built on inaccurate or out-of-date data, so it does not match what really happens

Misleading analysis → Results that look reliable but are distorted because the data used was inaccurate or untimely

Untimely data can lead to a false conclusion, such as believing a product is still in stock when it has sold out → stock_report_date = 'last month'; decision = 'Do not reorder'

Inaccurate data produces a wrong forecast because errors flow through into the prediction → forecast = 2000 * 1.1 # sales entered as 2000, real figure 200

Using last year's customer survey to decide this year's product range without checking it is still current → The data is untimely, so the decision may be wrong because customer needs have changed. Use current data.

Average sales = 500 (calculated from data containing duplicate and mistyped records, then used for budgeting) → The analysis is misleading because the data was not checked for accuracy. Clean and verify the data before analysis.

Key Term

Poor decision-making

Decisions made from inaccurate or untimely data are likely to be wrong, causing wrong forecasts, misleading analysis and false conclusions.

Fill in the Blanks

1. IF dataAccurate = FALSE THEN decisionRisk ← HIGH ENDIF

Hint: Type the keyword that starts a condition check before the risky-decision flag is set.

2. sales[3] ← 9000 forecast ← AVERAGE(sales)

Hint: Type the function that calculates the mean. One wrong value in the data will skew this forecast.

3. IF dataAgeDays > 30 THEN refreshData() ENDIF

Hint: Type the comparison operator that checks whether the data is older than 30 days (untimely).

4. total ← SUM(sales) avgSale ← total / COUNT(sales)

Hint: Type the function that tallies the records. Duplicate or wrong records would give a misleading analysis.

5. IF dataValid AND dataCurrent THEN conclusion ← TRUSTED ENDIF

Hint: Type the logical operator that requires both checks to be true before a conclusion is trusted.

Spot the Error

Question 1

A sales = [120, 135, 150]

B avg = sum(sales) * len(sales)

C forecast = avg * 1.1

D print(forecast)

Line B — An average is the total divided by the count, so it needs / not *. Multiplying gives a huge wrong average, which leads to a wrong forecast. It should be avg = sum(sales) / len(sales).

Question 2

A age = 45

B limit = 7

C if age < limit:

D print("Data is too old")

Line C — The warning should show when the data is older than the limit. Using < warns about fresh data and ignores stale data, so decisions would use untimely data. It should be if age > limit:.

Question 3

A marks = [70, 65, 80

B avg = sum(marks) / len(marks)

C print(avg)

Line A — The list is missing its closing square bracket, so the program cannot run and no analysis is produced. It should be marks = [70, 65, 80].

Question 4

A last_update = "2019-01-01

B today = "2025-06-01"

C if last_update < today:

D print("Refresh data")

Line A — The string is missing its closing quote, which is a syntax error. The date check that spots untimely data never runs. It should be last_update = "2019-01-01".

Question 5

A stock = 50

B forecast == 200

C order = forecast - stock

D print(order)

Line B — A single = assigns a value, but == compares two values. Here forecast is never created, so the next line fails and no order amount is worked out. It should be forecast = 200.

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