QCE Digital Solutions Unit 4: Adjusting data for completeness, consistency and integrity
Students assess data against integrity characteristics, clean and validate it, and use checksums and hashes to confirm it arrives intact.
One content description from the full QCE Digital Solutions Unit 4 — Complex Digital Data Exchange Problems and Solution Requirements journey. All 25 modules below are in that journey too, with the same content — add just these to a class, or the whole strand.
Modules (25)
1
Data completeness
2
Data consistency
3
Data integrity characteristic: correctness
4
Data integrity characteristic: reasonableness
5
Data integrity characteristic: authenticity
6
Data integrity characteristic: timeliness
7
Assessing data against the six integrity characteristics
8
Keeping an untouched copy of the raw data
9
Handling missing values
10
Flagging duplicates with a COUNTIF formula
11
Standardising spelling variations with Find and Replace
12
Converting text to numbers and fixing date formats
13
Cleaning text with TRIM, UPPER and PROPER
14
Keeping a cleaning log
15
Validation: why data is checked on entry
16
Types of validation check
17
Existence checking in code
18
Type checking in code
19
Range checking in code
20
Combining validation checks in a validation function
21
What data integrity means in transmission
22
Checksum
23
Hashing functions
24
Using hashes to check data integrity
25
Checking end-to-end data integrity from collection to visualisation
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