lesson 3-13-1 the data around us
Understanding the Scale and Variety of Big Data
Have you ever wondered how Netflix knows exactly what movie you want to watch next, or how a supermarket sends you vouchers for the things you actually buy? The secret is dataRaw facts and figures with no meaning.! Every time you use an app, watch a video, play a game, or even walk around with your phone, you are generating huge amounts of data. In this lesson, we're going to become Data Detectives and explore the massive, fast-moving, and varied world of Big Data. We'll discover how professionals like Data Scientists use this data to spot patterns and make predictions that shape our world.
Learning outcomes
The Building Blocks (Factual Knowledge)
The Connections and Theories (Conceptual Knowledge)
The Skills and Methods (Procedural Outcomes)
Recall that large amounts of data are generated and collected continuously by organisations and systems we use every day.
Describe how patterns found within large datasets can be used to gain insights or make decisions.
Describe the '3 Vs' of Big Data (Volume, Velocity, and Variety).
The Connections and Theories (Conceptual Knowledge)
Explain the difference between raw data, useful information, and the knowledge gained from it.
Digital Skill Focus:
The Skills and Methods (Procedural Outcomes)
Apply your curiosity to formulate simple questions that could be answered by analysing a large dataset.
What is Data, Anyway?
You hear the word 'data' all the time, but what does it actually mean? In computing, we can think of it in four stages:
DIKW - Click to enlarge
Professionals called Data Scientists are experts at turning massive piles of raw data into valuable knowledge that helps businesses, scientists, and governments make smart decisions. Let's investigate...
Task 1 My Data Footprint
After our class discussion, choose one app or online service that you use regularly (for example, YouTube, TikTok, Roblox, Spotify).
1
Prepare your workspace
You should create a blank word processed document for you to record your work. Save it somewhere suitable in your userspace and call it "My Digital Footprint".
2
Create your template
Create three headings in your document:
1
Name of the service
2
Three types of data it collects about me
3
Why does it collect this data?
Apply proper 'heading' styles to these. They are designed to apply styles which don't 'follow' to the line below.
3
Engage!
Under the first heading, write the name of the service you chose.
Under the second heading, list at least three specific types of data it collects. Think about what you click, what you type, what you watch, and where you go.
Under the third heading, write one sentence explaining why you think the company wants to collect this information about you. (Hint: Does it help them make money or improve the service?)
Outcome: I can identify specific data that is collected about me by a service I use and suggest a reason why that data is collected.
Welcome to the World of Big Data
The brainstorming task shows us that data is being generated everywhere, all the time. The term we use for these enormous, complex datasets is Big Data.
Big Data is defined by three key characteristics, known as The 3 Vs:
1
Volume
This refers to the incredible amount of data being stored. We're not talking about a few spreadsheets; we're talking about terabytes, petabytes, or even exabytes of data.
Analogy: A single book is data. A school library is a lot of data. Volume is like trying to store every book ever written in the world.
Example: All the photos and videos ever uploaded to Instagram.
2
Velocity
This is the amazing speed at which new data is generated and needs to be processed.
Analogy: A dripping tap is data. A river is a lot of data. Velocity is a tidal wave of data that never stops.
Example: The millions of tweets, messages, and sensor readings being generated around the world every second.
3
Variety
This refers to all the different forms that data can take. It's not just neat rows and columns in a table.
Analogy: A library with only text books has one type of data. A modern media library has books, films, music, video games, and maps. Variety is all of these things and more.
Example: A social media company collects a huge variety of data: text posts, photos, videos, audio messages, location data, and friendship connections.
So many V's! Let's consolidate...
Task 2 Sorting the 3 Vs
1
In small groups
1
Your teacher will give you a set of cards.
2
On each card is a description of a real-world data source.
3
With your partner, read each card carefully.
4
Decide which of the 3 Vs (Volume, Velocity, or Variety) best describes that data source.
5
Place the card under the correct heading.
6
Be prepared to explain your choices to the class. Some cards might fit into more than one category, so pick the one you think is the most important characteristic.
2
On your own
Continuing on from the word processed document you started in task 1, create a table with three columns, Volume, Velocity and Variety and three empty rows.
Copy two of the examples from the cardsort into the first two empty rows.
Come up with your own example for the last row.
Outcome: I can categorise different data sources using the 3 Vs of Big Data and come up with my own examples.
Asking the Right Questions
So, we have all this data. What's the point? The goal of a Data Scientist is to ask interesting questions that can be answered by the data. The answers provide the knowledge that helps organisations improve.
Task 3 What's the Question?
1
Read the following scenario.
2
Imagine you are a Data Scientist working for this supermarket.
3
On your whiteboard, write down three interesting questions you could answer using this data to help the supermarket improve its business.
4
You may be asked to share your ideas with the class.
Outcome: I can write simple questions that could be answered by a large dataset.
Out of Lesson Learning
Last modified: October 2nd, 2025
