Consequence of diminished data integrity: poor decision-making

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Quick Questions

Shade in the A or B circle for each question.

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

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

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A Untimely data: it is out of date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

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

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

Match the Pairs

Draw a line connecting each item on the left to its match on the right.

Left column		Right column
1Wrong forecast		AResults that look reliable but are distorted because the data used was inaccurate or untimely
2Misleading analysis		BA prediction built on inaccurate or out-of-date data, so it does not match what really happens
3Untimely data can lead to a false conclusion, such as believing a product is still in stock when it has sold out		CThe data is untimely, so the decision may be wrong because customer needs have changed. Use current data.
4Inaccurate data produces a wrong forecast because errors flow through into the prediction		DThe analysis is misleading because the data was not checked for accuracy. Clean and verify the data before analysis.
5Using last year's customer survey to decide this year's product range without checking it is still current		Eforecast = 2000 * 1.1 # sales entered as 2000, real figure 200
6Average sales = 500 (calculated from data containing duplicate and mistyped records, then used for budgeting)		Fstock_report_date = 'last month'; decision = 'Do not reorder'

Definitions

Poor decision-making

Fill in the Blanks

Complete each line of code by filling in the missing parts.

__ dataAccurate = FALSE THEN decisionRisk ← HIGH ENDIF

sales[3] ← 9000 forecast ← _______(sales)

IF dataAgeDays _ 30 THEN refreshData() ENDIF

total ← SUM(sales) avgSale ← total / _____(sales)

IF dataValid ___ dataCurrent THEN conclusion ← TRUSTED ENDIF

Spot the Error

Shade in the letter of the line that contains an error.

Question 1

A sales = [120, 135, 150]

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

C forecast = avg * 1.1

D print(forecast)

Question 2

A age = 45

B limit = 7

C if age < limit:

D print("Data is too old")

Question 3

A marks = [70, 65, 80

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

C print(avg)

Question 4

A last_update = "2019-01-01

B today = "2025-06-01"

C if last_update < today:

D print("Refresh data")

Question 5

A stock = 50

B forecast == 200

C order = forecast - stock

D print(order)

Answer Key

Consequence of diminished data integrity: poor decision-making

Section A: Quick Questions

B — A wrong forecast, so it over-orders stock

A — They are likely to be wrong

A — Untimely data: it is out of date

B — Misleading analysis

A — False conclusion

Section B: 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.

Section C: Definitions

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

Section D: Fill in the Blanks

IF dataAccurate = FALSE THEN
    decisionRisk ← HIGH
ENDIF

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

IF dataAgeDays > 30 THEN
    refreshData()
ENDIF

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

IF dataValid AND dataCurrent THEN
    conclusion ← TRUSTED
ENDIF

Section E: Spot the Error

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).

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:.

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].

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".

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.