Today, we’re going to explore a framework called the DIKIW Model. It helps us understand how raw data transforms into valuable wisdom.

The diagram here breaks this journey into five stages: Data, Information, Knowledge, Insight, and Wisdom (DIKIW). Let’s dive into each stage step by step.

1. Data

Data is at the base of the model.

• Data is like raw material — a series of random dots or unprocessed facts.
• By itself, it has no meaning. It’s just numbers, words, or measurements.
• Example: Imagine you have a list of temperatures recorded throughout the day. Without context, it doesn’t tell you much.

Data is “block oil” — it’s valuable, but only when refined.

2. Information

When meaning or relationships are applied to raw data, it becomes information.

• At this stage, we start to see patterns or groupings.
• Example: If you organise the temperature readings by time, you’ll see when it’s hottest and coolest during the day.
• Information provides context and is often visualised using charts, tables, or colour coding.

This is like colouring the dots in the diagram to highlight differences or relationships.

3. Knowledge

Knowledge comes when we make sense of the information and see connections.

• At this stage, we begin to understand why things happen.
• Example: Analysing the temperature data might reveal that it’s hottest at noon and coolest at dawn.
• Knowledge connects the dots and helps us understand patterns or causes.

This is where we start to see the bigger picture, as the diagram shows interconnected lines.

4. Insight

Insight is where things get seriously useful.

• It’s synthesising knowledge and gaining a deeper understanding of a problem.
• Example: From the temperature data, you might infer that noon is the best time for solar energy collection, while early morning is ideal for outdoor activities.
• Insights are actionable. They guide decisions and strategies.

In the diagram, the highlighted paths represent key insights that stand out from the broader connections.

5. Wisdom

At the top of the model is wisdom, the most refined stage.

• Wisdom is using insights to make informed decisions and act purposefully.
• Example: Based on your insights, you decide to schedule outdoor activities early in the morning and optimise solar panels to maximise energy collection at noon.
• Wisdom combines all the previous stages to guide strategic, long-term thinking.

In the diagram, wisdom is depicted as a clear path that guides decision-making.

Why is This Important?

• In today’s world, data is everywhere, but it’s useless unless transformed into actionable wisdom.
• The DIKW model helps us understand step-by-step how to extract value from data.

Final Thoughts

Data is the new oil, but it’s only valuable when refined into wisdom. Following the DIKW model, we can move from collecting raw data to making intelligent, informed decisions.

Let’s discuss: How can you apply this model in your work or personal life? Share an example of how you’ve turned data into actionable insights!

[Download eBook IoT Notes to complement these lecture notes]

Today, we’ll discuss two critical topics in the digital age: data ownership and the 4 V’s of Big Data. 

This diagram simplifies these concepts, so let’s break them down for better understanding.

1. Data Ownership

Data ownership refers to who has the rights and responsibilities over data. There are four main categories:

1. Personal/Household

• This includes data generated from your personal devices, like your smartphone, fitness tracker, or smart home systems.
• Example: Steps tracked by your smartwatch, or usage data from your smart TV.
• You, as the owner of the device, own this data and can decide how it is used or shared.

2. Private

• This is data collected and owned by companies or enterprises.
• Example: A company’s internal data about its operations, such as sales performance or employee attendance.
• Organisations use this data to improve their services, products, or strategies.

3. Public

• Public data is owned by the government and shared for the benefit of society.
• Example: Data from weather sensors, air quality monitors, or river level gauges.
• This data is often accessible to the public for research, awareness, or planning purposes.

4. Commercial Sensor Provider

• These are entities that deploy, own, and sell data collected from their sensors.
• Example: A telecommunications company selling location data collected from its network.
• They monetise the data by providing it to third parties, such as businesses or governments.

2. The 4 V’s of Big Data

Big Data refers to the massive volumes of data generated by digital devices and systems. It is characterised by the 4 V’s:

Volume

• This is the amount of data, which can be massive in scale.
• Example: Social media platforms generate terabytes of data every day from user interactions.

Velocity

• This refers to how fast or slow data is generated and processed.
• Example: Real-time data from stock markets or traffic monitoring systems must be processed quickly to be useful.

Variety

• Data comes in different formats, such as text, audio, video, or images.
• Example: An IoT platform may process data from sensors (numeric values), surveillance cameras (video), and voice commands (audio).

Veracity

• This addresses the uncertainty or trustworthiness of the data.
• Example: Ensuring the accuracy of user-generated reviews on e-commerce platforms can be challenging.

Why is This Important?

Understanding data ownership and the nature of Big Data is essential for:

• Privacy and Security: Knowing who owns and controls your data helps protect your rights.
• Decision-Making: Leveraging the 4 V’s effectively enables organisations to make informed decisions.
• Innovation: Big Data drives advancements in fields like healthcare, transportation, and smart cities.

Final Thoughts

Data is the fuel of the digital economy, but with it comes the responsibility to manage it ethically and effectively. Whether it’s your personal data or public data shared by governments, understanding ownership and the dynamics of Big Data is crucial.

Let’s discuss: How can individuals and organisations ensure ethical data usage while maximising its potential? Share your thoughts!

[Note: Download the full eBook IoT Notes by Mazlan Abbas]

Today, let’s explore why the Internet of Things (IoT) is experiencing a surge in awareness and adoption in recent years. The diagram provides five apparent factors driving this growth, so let’s walk through each step.

1. Hardware Advancements

The first driver of IoT adoption is the rapid development of hardware. Devices are now:

• Cheaper: The cost of sensors, processors, and connectivity modules has dropped significantly.
• More Powerful: Today’s smartphones, for instance, are as powerful as the computers that send astronauts to the moon.
• Smaller: Miniaturisation has made it easier to embed technology into all sorts of devices, from wearable health trackers to smart home appliances.

These advancements make IoT devices accessible to more people and industries.

2. Network Expansion

IoT depends on connectivity, and networks have become more pervasive and diverse:

• We now have Wi-Fi, 4G/5G, LoRa, NFC, and even satellite networks connecting devices across the globe.
• This widespread coverage ensures that IoT devices can communicate, no matter where they are located.

Imagine this: You can monitor a sensor in a remote farm or track a shipping container in the middle of the ocean because of this pervasive network infrastructure.

3. Easier and Faster Software Development

Creating IoT solutions has become simpler because:

• Software tools and platforms are now more user-friendly.
• Developers can build and deploy solutions quickly with pre-built frameworks, cloud computing, and open-source libraries.

What used to take months or years to program can now be done in days or weeks, speeding up innovation in IoT.

4. Moore’s Law: The Power of Computation

You may have heard of Moore’s Law, which states that the number of transistors on a chip doubles approximately every two years. This leads to:

• Higher computational power: Devices can handle more complex tasks, such as AI and data processing, on smaller chips.
• Lower costs over time: IoT solutions can scale quickly with more powerful chips becoming affordable.

This exponential growth in computing power has made IoT a reality.

5. The Network Effect

The network effect explains how IoT becomes more valuable as more connected devices. Here’s why:

• Everything is connected: Sensors, devices, and systems communicate and generate data.
• Data generation: The more devices there are, the more data we have. This data can be analysed to gain insights, optimise processes, and improve decision-making.

For example, a smart city network with connected traffic lights, sensors, and cameras can reduce congestion and improve safety by analysing real-time data.

Historical Context

• The term “IoT” was first coined by Kevin Ashton in 1999.
• It took years of technological progress for IoT to become mainstream. By 2020, IoT reached a tipping point, integrating with industries worldwide.

Why Now?

The convergence of cheaper hardware, pervasive networks, faster software development, computational power (thanks to Moore’s Law), and the network effect have created the perfect environment for IoT to flourish.

IoT is no longer a futuristic concept — it’s a reality shaping industries like agriculture, healthcare, and manufacturing.

Let’s discuss: Which of these factors do you think has had the biggest impact on IoT adoption? How can we use these advancements to innovate further in our fields?

[Note: Download full IoT Notes eBook]