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]

Let’s target for the year 2024 with a new skill or up-skill your IoT knowledge with AI.

Favoriot will be offering this training on May 6–9, 2024. Mark this in your calendar and register for an early bird price!

Unveiling the Mastering AIoT Course: A Synergy of AI & IoT

Today, we are seeing the fusion of Artificial Intelligence (AI) and the Internet of Things (IoT), giving rise to the next wave of the technological revolution — the Artificial Intelligence of Things (AIoT).

Recognizing this, we have meticulously designed a 4-day intensive course, “ Mastering AIoT — From Theory to Practice, “to provide a robust understanding of AIoT integration.

Course Objectives: Bridging Theory and Practice

The course is strategically developed to meet the following objectives:

• Integrating AI with IoT: We start by familiarizing participants with the convergence of AI and IoT, a critical skill set for tomorrow’s innovators.
• Exploring the IoT Ecosystem: Participants delve into the architecture and components of IoT, gaining comprehensive insights into its functioning.
• Mastering Edge Computing with Jetson Nano: Focusing on the Jetson Nano hardware (or similar platforms) equips learners with hands-on expertise in IoT device management and data processing.
• Interfacing and Data Transmission: We train you to interface sensors with Jetson Nano, ensuring seamless data flow to platforms like Favoriot.
• Unlocking IoT Data Analytics: Specialized techniques are taught to decipher vast amounts of IoT data, turning information into actionable insights.
• Empowering Creativity: The course culminates with designing and constructing intelligent IoT applications using AI, Python, and the Favoriot IoT Platform.

Target Participants: Who Will Benefit?

This course is tailored for:

• Aspiring IoT Developers: Those looking to jumpstart their career in IoT development.
• Data Scientists and Analysts: Professionals aiming to specialize in IoT data analytics.
• AI Enthusiasts: Individuals keen on exploring the application of AI in the IoT space.
• Product Managers: Decision-makers overseeing IoT product development.
• Academics and Researchers: Those involved in AIoT research and education.
• Entrepreneurs: Visionaries seeking to innovate with AIoT solutions.

Learning Outcomes: What You’ll Achieve

By the end of this course, participants will:

1. Comprehend AI and IoT integration fundamentals.
2. Develop IoT applications utilizing Jetson Nano, Python, and the Favoriot IoT Platform.
3. Interface sensors with hardware like Jetson Nano for IoT data collection.
4. Implement IoT-specific data analytics to derive significant insights.
5. Apply machine learning algorithms for intelligent data interpretation.

Training Modules: Your Roadmap to AIoT Mastery

Module 1: The Foundations of IoT and AI

We begin with the basics, exploring IoT’s architecture and ecosystem and an introduction to AI, covering various learning types and their IoT applications.

Module 2: Data Acquisition on the Favoriot Platform

Diving into data acquisition and analysis for IoT, we provide insights into the Favoriot platform’s structure and data handling capabilities.

Module 3: AI Hardware Essentials

A comprehensive overview of Jetson Nano’s hardware, setting the stage for IoT development with practical Linux command training.

Module 4: Machine Learning Foundations

Exploring data analytics and machine learning types, introducing tools like TensorFlow and PyTorch, and hands-on model building.

Module 5: Delving into Neural Networks

An in-depth look at deep learning, including CNNs and RNNs, and practical exercises in building neural network models.

Module 6: IoT Integration Techniques

Connecting Jetson Nano to IoT devices and implementing edge computing for real-time data processing.

Module 7: IoT Data Analysis with Machine Learning

Preparing and analyzing IoT data with machine learning, visualizing results for unambiguous interpretation.

Module 8: AI Model Deployment

Finalizing the course with AI model deployment on hardware and optimization for peak performance.

Hardware Utilized in the Course

Participants will get hands-on experience with cutting-edge technology, including the Hibiscus Sense, Computer Jetson Nano 4G Dev Kits, and WiFi dongles, ensuring a practical learning environment.

Conclusion: Your Path to AIoT Expertise

This 4-day course is more than just a training program — it’s a transformative experience that will equip you with the knowledge and skills to innovate in the AIoT domain.

We invite all interested individuals to embark on this journey to learn and master the intricacies of AIoT.

For those ready to step into the future of intelligent technology and harness the full potential of AIoT, “ Mastering AIoT — From Theory to Practice” is your pivotal training ground. Join us and redefine what’s possible in the world of smart technology.

Contact Favoriot for the IoT Training.