In simple terms
A friendly intro before the formal notes — no formulas yet.
The Digital Library
Data is like the raw words in a book, while a database is the entire library system that organises those books so you can find what you need. This system turns raw facts into useful, searchable information.
Imagine a vast library. Each individual word on every page is 'data'. A single book, organised into chapters and with a title, is 'information'. When you read several books on a topic and form your own understanding, that's 'knowledge'. The database is the entire library itself: the building, the shelves (tables), the card catalogue (queries/indexes), and the librarian (Database Management System) who helps you find, check out, and return books efficiently.
- 1
First, understand the hierarchy: raw 'data' (like a list of temperatures) is processed into meaningful 'information' (the average temperature last week).
- 2
Next, see how this information is structured in a database using tables, which are made up of rows (records) and columns (fields).
- 3
Then, explore the two main types of databases: relational (like a neat set of linked spreadsheets) and non-relational (like a flexible folder of documents).
- 4
Finally, consider the real-world impact of these databases, from how your social media feed is generated to how a business manages its customer list.
Explore the concept
Use the live diagram and synced steps — play it or tap a step card to walk through.
Full topic notes
Formal explanation with the rigour you need for the exam.
The DIKW Pyramid: From Raw Facts to Actionable Insight
To understand the value of data, we often use the Data-Information-Knowledge-Wisdom (DIKW) pyramid. It shows how raw data is transformed into something much more valuable. 'Data' are the raw, isolated facts. When we add context and structure, it becomes 'Information'. When we understand and interpret that information, it becomes 'Knowledge'. Finally, 'Wisdom' is the ability to apply that knowledge effectively.
Data: Raw facts. Example: 3,000.
Information: Data in context. Example: 3,000 steps taken today.
Knowledge: Understanding patterns. Example: Taking 3,000 steps is below my daily goal of 10,000 steps.
Wisdom: Applying knowledge. Example: I should go for a walk this evening to meet my fitness goal.
Organising Data: Databases and DBMS
A database is simply a structured way of storing data. Think of it as a highly organised digital filing cabinet. To interact with this filing cabinet, we use a Database Management System (DBMS). The DBMS is the software that allows us to create, read, update, and delete data in the database. It ensures data is secure, consistent, and accessible.
Types of Databases: Relational (SQL) vs. Non-Relational (NoSQL)
Databases are not one-size-fits-all. The two major categories are relational and non-relational. Relational databases (often called SQL databases) have been the standard for decades. They organise data into tables with strict schemas, like our Coding Club example. They are excellent for structured data where consistency and reliability are paramount, such as in banking or e-commerce inventory systems. Non-relational databases (NoSQL) are more modern and flexible. They can handle unstructured data like social media posts, images, and sensor data. They are designed for high speed and scalability, making them ideal for big data applications and dynamic websites.
Worked examples
See the formulas applied — reveal one step at a time, like the exam.
A new school 'Coding Club' needs a simple database to track its members. Design a single table for this database, identifying a suitable primary key and listing four appropriate fields (columns). Then, provide two example records (rows) for this table.
- 1
A suitable structure would be a table named 'Members'.
A developer is building a new mobile app for sharing short video clips, similar to TikTok. Users can upload videos, add text captions, use hashtags, and 'like' other users' videos. Would a relational (SQL) or non-relational (NoSQL) database be more appropriate? Justify your answer with two reasons.
- 1
Handling Unstructured and Varied Data: The app deals with highly varied data types: video files, user profiles, text captions, lists of hashtags, and a constantly changing number of 'likes'. A NoSQL database (like a document database) can store all this related but unstructured information together in a flexible format without a rigid, predefined schema. A relational database would struggle to efficiently store a variable number of hashtags or likes for each post.
How it all connects
The big idea sits in the middle — tap a linked idea to explore the link.
Tap a linked idea to see how it connects back to the main topic — that connection is what examiners reward.
Glossary
Try to recall each definition before you reveal it.
Quick check
Answer in your head first — then tap to check. No pressure.
Revision flashcards
Flip the card. Test yourself before the exam.
Data
Raw, unorganised facts, figures, and symbols that have no context or meaning on their own. For example, '19.4'.
Key takeaways
Review these before you close the topic — retrieval beats re-reading.
- ✓
Data: Raw facts. Example: 3,000.
- ✓
Information: Data in context. Example: 3,000 steps taken today.
- ✓
Knowledge: Understanding patterns. Example: Taking 3,000 steps is below my daily goal of 10,000 steps.
- ✓
Wisdom: Applying knowledge. Example: I should go for a walk this evening to meet my fitness goal.
Practice — then mark it
The whole point: a real Cambridge question, marked mark-by-mark.
Test Your Knowledge on Data and Databases
Test Your Knowledge on Data and Databases
Extra simulations & links
PhET, GeoGebra and other curated tools — open in a new tab.
Frequently asked
Checkpoint
One marked question is worth ten re-reads — close the loop before you move on.
Reading it isn’t knowing it — prove it.
Before you move on: do Test Your Knowledge on Data and Databases on paper, snap a photo, and get examiner-style feedback on exactly where you win and lose marks.