Normalising data

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10.2 · Chapter 10 · AS Level IT · Hodder Education

Normalising Data

A step-by-step technique to remove redundant data and structure a database efficiently — from un-normalised to Third Normal Form, with a full worked example.

📋 0NF (UNF)

1️⃣ 1NF

2️⃣ 2NF

3️⃣ 3NF

The Big Idea: Normalisation removes duplicated data and organises a database so that each piece of information is stored in exactly one place. Each Normal Form adds a rule that the data must follow — and each step removes a different type of problem.

10.2What is Normalisation?

Normalisation is a multi-step technique used to reduce data duplication in a relational database. It helps to:

→ Organise data efficiently
→ Remove redundant (duplicated) data
→ Ensure only related data is stored in each table

The rules are called Normal Forms, numbered 0–5. This course covers 0NF through to 3NF. (4NF and 5NF are beyond scope.)

0NF

Un-Normalised Form (0NF / UNF)

Also called: un-normalised database

A database that has NOT been normalised. Typically a flat-file (single table) with:

Characteristics of 0NF data

Redundant data: the same data stored multiple times (e.g. teacher name repeated for every student they teach)
Non-atomic data: multiple items of data stored in a single field (e.g. "Computing, Mr Brown, Room 53, Maths, Miss White, Room 104" all in one field)
Repeating groups: the same type of data repeated in multiple columns or rows

Example 0NF — raw student/course data (non-atomic course field)

Student name	House	Course (non-atomic!)
Jane Smith	York	Computing, Mr Brown, Room 53, Maths, Miss White, Room 104, Statistics, Miss White, Room 104
Ruksana Patel	Lancaster	Business, Mrs Grey, Room 3, Maths, Miss White, Room 104, Science, Mr Green, Room 24

1NF

First Normal Form (1NF)

Eliminates non-atomic data and repeating groups

Rules for 1NF (ALL must be true)

All data is stored in a database table
A unique key must exist (primary key or compound key)
Only atomic data is stored — data is at its lowest level and cannot be broken down further (e.g. Forename and Surname as separate fields, not "Jane Smith")
Each field has a unique name
Each record is unique (no repeated rows)
No repeating groups of columns

To convert to 1NF: Split non-atomic fields (e.g. course data split into Subject, Teacher, Room). Add a unique ID field (St_ID) since two students share the same name. Add Course_ID for each course. Use a compound key (St_ID + Course_ID) since neither alone is unique.

1NF result — all data now atomic, compound key = St_ID + Course_ID

St_ID (PK)	Forename	Surname	House	Course_ID (PK)	Subject	Teacher	Room
0001	Jane	Smith	York	1042	Computing	Mr Brown	53
0001	Jane	Smith	York	1400	Maths	Miss White	104

Why this is NOT yet 2NF

Subject, Teacher, and Room are NOT dependent on St_ID — they relate only to the Course_ID. And Forename, Surname, House are NOT dependent on Course_ID — they relate only to St_ID. When non-key fields depend on only part of a compound key, the data is not in 2NF.

2NF

Second Normal Form (2NF)

Eliminates partial key dependency

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🧠 What to Memorise
🎯 Exam Tips

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Normalising data