The distinction between data, information and knowledge, data types, and the validation and verification techniques that protect data quality.

What this dot point is asking

CCEA wants you to distinguish data, information and knowledge, to recognise common data types, and to describe the validation and verification techniques that protect data quality when it is entered. Poor-quality data produces poor information, so these checks underpin every system that stores data.

Data, information and knowledge

The number 37 is data; "the noon temperature was 37 degrees Celsius" is information; "37 degrees is dangerously hot, so the greenhouse vents should open" is knowledge applied to act. Data becomes information through processing: selecting, sorting, summarising, calculating, or placing it in context.

Data types

A data type defines the kind of value a field holds and what operations are valid on it. Common types are integer (whole numbers), real or float (numbers with a fractional part), text or string (characters), Boolean (true or false) and date/time. Choosing the right type saves storage, enables the correct operations (you can add integers but not text), and supports validation.

Validation and verification

Common validation checks include:

• Range check. The value lies within set limits (a month is 1 to 12).
• Format (picture) check. The value matches a pattern (a date as DD/MM/YYYY).
• Presence check. A required field is not left blank.
• Type check. The data is the right kind (a number where a number is expected).
• Length check. The value has an allowed number of characters (a PIN is exactly four digits).
• Check digit. An extra digit calculated from the others detects entry errors (as on barcodes and bank numbers).

Verification is done by double entry (the same data entered twice and compared) or a visual check (proofreading) against the source document.

Why data quality matters

Information is only as good as the data behind it ("garbage in, garbage out"). Validation stops obviously wrong data at the point of entry, and verification catches accurate-looking but mistyped data, together protecting the quality of the information the system produces.

Try this

Q1. State which check ensures a required field is not left blank. [1 mark]

• Cue. A presence check.

Q2. Give one example each of an integer and a Boolean data value. [2 marks]

• Cue. Integer: a whole number such as 42. Boolean: true or false (for example, "subscribed = true").

Q3. Explain why a value can pass a validation check but still be incorrect. [2 marks]

• Cue. Validation only tests whether the data is reasonable against rules, so a wrong value that still falls within the rules (for example a valid but mistyped date) is accepted; only verification against the source catches it.