Selangor, Malaysia – February 3, 2025 — Favoriot, a leading IoT platform provider, is proud to announce its latest Favoriot Partner Network (FPN) initiative to transform the landscape of IoT education through strategic collaborations with educational institutions. This collaboration bridges the gap between academic knowledge and industry demands by equipping students with practical IoT skills and industry-recognised certifications.

Empowering Students Through Two Key Approaches:

1. Embedding Favoriot IoT Platform into IoT Courses and Labs
   Educational institutions can directly integrate Favoriot’s IoT content into their courses, syllabi, and laboratory environments. This approach enables students to gain hands-on experience with real-world IoT platforms, fostering practical skills in device management, data analysis, and system integration. Upon completing these IoT courses, students will be awarded the Favoriot Certificate, co-endorsed by both Favoriot and the respective institution, enhancing their employability in the IoT industry.
2. Short-Term IoT Training Conducted by Certified Lecturers
   In addition to curriculum integration, Favoriot offers specialised 2-3 day IoT training programmes conducted by university lecturers. To ensure high-quality training, these lecturers must pass the Favoriot Certificate Examination to become certified trainers. This certification process guarantees that students receive instruction from knowledgeable educators who are well-versed in the latest IoT technologies. Students who complete these intensive training sessions will also receive the Favoriot Certificate, recognised by industry players.

Quality Assurance Through Certified Educators
Favoriot maintains stringent quality control measures by requiring that only lecturers who have successfully obtained the Favoriot Professional Certification are eligible to teach IoT courses or conduct training sessions. This ensures consistent, high-quality instruction across all partner institutions.

A Commitment to Industry-Ready Talent Development
“Our collaboration with educational institutions is part of Favoriot’s commitment to nurturing the next generation of IoT professionals,” said Dr. Mazlan Abbas, CEO of Favoriot. “By embedding our platform into academic environments and empowering educators through certification, we are creating a robust pipeline of talent equipped to meet the evolving demands of the IoT industry.”

About Favoriot:
Favoriot is a leading IoT platform company dedicated to simplifying the development of IoT applications through secure, scalable, and user-friendly solutions. With a strong focus on education, smart cities, and industrial IoT, Favoriot is at the forefront of driving digital transformation across various sectors.

For more information about this initiative or to explore partnership opportunities, please visit www.favoriot.com or contact info@favoriot.com.

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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]