Big Data and Analytics – SS2 Digital Technologies Lesson Note (Second Term)

Big Data is data that is so massive, fast and messy that a regular computer cannot handle it, so special tools are needed to analyse it. It is described by the 5 Vs: Volume, Velocity, Variety, Veracity and Value. Data analytics is the process of asking questions of that data to find answers and patterns.

Behavioural objectives

By the end of the lesson, students should be able to:

• explain what Big Data is and give examples of where it comes from;
• list and describe the 5 Vs of Big Data;
• give applications of Big Data in healthcare, entertainment, banking and agriculture;
• state what Hadoop, NoSQL databases and Apache Spark are used for;
• distinguish between descriptive, predictive and prescriptive analytics.

What is Big Data?

In the old days, a business could keep its records in a single notebook or a simple Excel sheet. Today, think about Jumia, Facebook or MTN.

• Every second, thousands of people are clicking links.
• Thousands are uploading photos.
• Thousands are making calls.

This mountain of information is too big, too fast and too messy for a regular computer to handle. This is what we call Big Data. It is data so massive that it needs special “super-tools” to understand it.

What are the 5 Vs of Big Data?

Data scientists use the 5 Vs to describe how we know if data is “Big”.

1. Volume (Size): We are talking about Terabytes and Petabytes. If you tried to print Big Data on paper, the stack would reach the moon!
2. Velocity (Speed): The data comes in like a rushing river. Think of Twitter (X): thousands of tweets appear every single minute.
3. Variety (Types): Big Data includes videos, voice notes, emojis, GPS locations and photos, as well as numbers in a table.
4. Veracity (Quality): The data may be messy or “fake”. Big Data often has a lot of “noise” or rumours that need to be filtered out.
5. Value (Purpose): This is the most important. There is no point in having Big Data if you cannot use it to make money or save lives.

What are the applications of Big Data?

Big Data affects our daily lives in Nigeria and across the world.

• Healthcare: Doctors use Big Data to predict where a disease (like malaria or COVID-19) might spread next by looking at hospital records and weather patterns.
• Entertainment: YouTube and Netflix analyse your “Big Data” (everything you have ever watched) to recommend films and videos you may like.
• Banking: Banks use it to spot fraud. If your ATM card is suddenly used in another country while your phone is still in Lagos, the Big Data system flags it as possible fraud immediately.
• Agriculture: Farmers use satellite data and soil sensors to know the best day to plant seeds to get the best harvest.

What tools are used to handle Big Data?

You cannot cut down a giant Iroko tree with a kitchen knife. Similarly, you cannot analyse Big Data with basic software. We use specialised tools, the “heavy machinery” of Big Data:

• Hadoop: Think of this as a team of 1,000 computers working together. If a task is too big for one computer, Hadoop breaks it into small pieces and gives a piece to each computer in the team.
• NoSQL databases (like MongoDB): Regular databases need neat rows and columns. NoSQL databases can easily store messy data like photos and long paragraphs.
• Apache Spark: A tool used for “real-time” analytics. It is what helps Uber calculate your fare and find a driver in 5 seconds.

What is data analytics?

Analytics is the process of asking the data questions, so that data turns into answers. There are three main levels:

1. Descriptive (What happened?): “Last month, we sold 500 bags of rice.”
2. Predictive (What will happen?): “Based on the data, we will probably sell 800 bags next month because of the festive season.”
3. Prescriptive (What should we do?): “We should buy 900 bags now while the price is low to make more profit.”

Key concepts at a glance

Evaluation

1. What is Big Data?
2. List the 5 Vs of Big Data and explain what each one means.
3. Give two applications of Big Data and explain how each one uses it.
4. What does Hadoop do when a task is too big for one computer?
5. Say whether each statement is descriptive, predictive or prescriptive analytics:
   • “Last month, we sold 500 bags of rice.”
   • “We will probably sell 800 bags next month.”
   • “We should buy 900 bags now while the price is low.”

Assignment

1. Apart from YouTube, give another example of an app that uses your “history” to recommend things to you.
2. Which of the 5 Vs do you think is the most challenging for a small business? Why?

Scenario: If you were the Principal of this school, how could Big Data help you improve student grades? (Think about attendance, test scores and library usage.)