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Building IoT Solutions with Favoriot (eBook)

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Building IoT Solutions with Favoriot

A practical guide for the connected world, created for builders who want to move from IoT theory to real systems, real dashboards, real data, and real action.

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IoT Architecture to action
Practical IoT Guide
Building IoT Solutions with Favoriot
A Practical Guide for the Connected World
Dr. Mazlan Abbas
CEO & Co-founder, FAVORIOT
About the eBook

From connected devices to meaningful decisions.

Building IoT Solutions with Favoriot is a practical guide designed for developers, engineers, students, lecturers, and decision-makers who want to build Internet of Things systems that work beyond classroom demos and isolated prototypes.

The book explains how complete IoT solutions are designed, built, and scaled using the Favoriot platform. It covers the full IoT flow, from understanding system architecture and connecting devices to managing data streams, building dashboards, and setting intelligent rules that trigger action.

Instead of staying at the surface level, this guide focuses on the real work behind IoT. It helps readers understand how data becomes visibility, how visibility supports decisions, and how decisions can improve operations in smart cities, agriculture, healthcare, industrial monitoring, and more.

What readers will learn

A clearer path to building IoT systems.

Understand IoT solution architecture Learn how devices, connectivity, platforms, dashboards, rules, and applications work together as one complete system.
Connect devices and manage data streams See how sensor data can be captured, sent, structured, stored, viewed, and prepared for better decision-making.
Create dashboards that support action Move beyond beautiful charts and learn how dashboards can guide response, monitoring, and operational improvement.
Explore real-world IoT use cases Apply the concepts to smart cities, agriculture, healthcare, industrial systems, education, and operational monitoring.
Who should read this

Built for people who want to build.

01

Students

For learners who want to create IoT projects with practical value and a stronger project story.

02

Developers

For builders who want a direct way to connect devices, send data, and create applications.

03

Lecturers

For educators who want to teach IoT with hands-on examples linked to real industry needs.

04

Decision-makers

For leaders who want to understand how IoT turns operational data into better visibility and action.

Start building IoT solutions with more confidence.

Get the eBook today and use it as your practical companion for understanding, designing, and deploying connected systems with Favoriot.

Get the eBook Now

I Used AI to Write My Latest eBook. Here’s What Actually Happened.

Let Me Be Honest With You

The eBook you are looking at right now, Mastering IoT and AIoT with Favoriot, was built with AI. Not just assisted by AI. Built by it. And I think that is worth talking about honestly, because the story of how this book came together tells you more about where we are with AI than any think piece I could write.

Here is the confession: most of the infographics inside were generated through ChatGPT. The eBook itself was assembled using Claude Cowork. I pointed it at my folder of infographics, and it categorised them, selected the best visual style, and structured everything into a coherent ladder from awareness to mastery. And the post you are reading right now? Claude is pushing it automatically to this blog.

I did not type all of this. AI did a significant part of the heavy lifting.

So Why Am I Telling You This?

Because I think the dishonest version of this story, the “I wrote a book” version with no footnotes, does everyone a disservice. We are at a moment in technology where the tools are genuinely extraordinary, and pretending otherwise is a form of intellectual cowardice.

But here is the thing I want you to sit with: the book is real. The frameworks inside it are real. The five rungs, Awareness, Foundations, Methodology, Production, and Mastery, those came from twenty-plus years of watching IoT projects succeed and fail. They came from UTM, CELCOM, MIMOS, from REDtone IoT, from every Favoriot deployment where a client came to us six months too late because they had skipped Rung 2.

The AI did not invent the Build-Readiness Ladder. I did. The AI helped me package it.

And I think that distinction matters enormously, not just for me, but for anyone trying to figure out how to use these tools without losing themselves in the process.

Every Buzzword Wave Brings the Same Temptation

I have been in IoT long enough to remember when “digitisation” was the buzzword that made executives nod without understanding. Then it was “big data.” Then “Industry 4.0.” Now it is AI everywhere. Each wave brings the same temptation: to let the tool become the story instead of the outcome.

What I tried to do with this eBook, and what I try to do with every piece of content I create, is stay anchored to the practitioner’s reality. Not theory. Not a showcase of what the technology can do in a lab. What actually ships. What actually fails. What the team on Rung 3 needs to hear at 11pm when their deployment is fighting them.

That is what I hope the five rungs give you. A ladder you can put your weight on.

Here Is Exactly How the AI Toolchain Worked

I want to be specific because I think the specifics are useful.

The infographics came first, from ChatGPT. I gave it the concepts, the frameworks, the key messages, and it generated visuals that I reviewed and curated. Some were brilliant on the first attempt. Some took five iterations. A few I threw out entirely because they were technically wrong, and that is the part that requires a human who actually knows IoT. The tool has no idea whether MQTT is correctly positioned in an architecture diagram. I do.

Claude Cowork then looked at the full collection, forty-three diagrams, and did something I genuinely did not expect it to do well: it read the logical progression across them. It understood that the Awareness diagrams should open the book, that the Production use cases belong after the methodology chapter, that the Data Storytelling infographic is the bridge between Rung 4 and Rung 5. It organised the ladder better than my first draft did.

Is that intelligence? I do not know. But it was useful, and I am not going to pretend it was not.

What AI Cannot Do

If you are an IoT practitioner reading this, the lesson is not “use AI to write your book.” The lesson is that your expertise, your hard-won, field-tested, scar-tissue knowledge, is the thing AI cannot generate. It can package. It can structure. It can format and distribute. But the rung-by-rung logic inside this eBook? That came from doing this work for two decades.

That is what I want to give you. Not a showcase. A ladder.

Now It Is Your Turn

Download the eBook. Start at the rung that makes you uncomfortable, that is always the right one. And if you want to talk through where you are standing, find me on LinkedIn or drop me a message through Favoriot.

The tools changed. The climb has not.

What rung are you on right now, and what is keeping you there?

When Writing Free eBooks Still Feels Like Shouting Into the Void

I did not expect this feeling to arrive so quietly.

No dramatic moment.
No emotional breakdown.
Just a soft question that kept returning while I stared at my screen.

Should I stop writing eBooks about IoT, startups, and entrepreneurship?

I have written several eBooks over the years. Some came from years of experience building platforms. Some from scars earned while running a startup. Some from observing founders struggle with the same blind spots again and again.

I made them free.
No paywall.
No upsell tricks.
Just knowledge, stories, and lessons shared openly.

Yet after my last three books (Hello IoT, The Favoriot Way: A Life Built on Curiosity and Courage, Favoriot : The Journey of an IoT Startup), something felt off.

Downloads slowed.
Shares dropped.
The quiet became louder.

At first, I blamed myself.

Maybe the topics are stale.
Maybe I am repeating myself.
Maybe people are tired of hearing from me.

Then another thought crept in.

Or maybe the world has changed.

The Moment I Could No Longer Ignore

I noticed something about my own habits before blaming anyone else.

I no longer Google as much.
I open ChatGPT.
I type a question.
I get an answer.

Direct.
Fast.
Clean.

And here is the uncomfortable truth.

I am guilty too.

I ask AI to summarise books.
I ask for key takeaways.
I skim instead of sitting with pages.

Who am I to complain when I do the same thing?

That realisation stung.

Because I used to love reading slowly. Highlighting sentences. Rereading paragraphs. Letting ideas sit for days.

Now, time feels compressed. Attention feels borrowed. Everything competes for mental space.

The Silent Shift No One Talks About

This is not about AI replacing writers.

It is about AI changing readers.

People no longer want to search.
They want answers.

They no longer want ten blog posts.
They want one response.

They no longer want to explore.
They want to arrive.

Why buy a book when a prompt gives you a clean summary?

Why spend hours reading when minutes feel enough?

That question hurts writers, but it is not wrong.

Books were once a journey.
Now they are treated like databases.

Tell me what matters. Skip the rest.

Short Attention Is Not a Moral Failure

I hear people complain about attention spans all the time.

But I do not think it is laziness.
I think it is survival.

We are flooded with inputs. Messages. Alerts. Updates. Noise.

Reading a 150-page eBook feels heavy when your mind is already full.

The new generation did not lose patience.
They adapted to overload.

They want clarity, not volume.
Direction, not depth.

At least not by default.

When Free Still Feels Expensive

Making my eBooks free was supposed to remove friction.

Yet free does not mean easy.

Reading still costs time.
Thinking still costs energy.

AI removed that cost.

One prompt feels cheaper than one chapter.

So why am I surprised?

The Hard Question I Keep Avoiding

I keep asking myself something uncomfortable.

Am I writing for impact, or am I writing out of habit?

In the past, writing eBooks felt like leaving a trail behind. Something lasting. Something searchable. Something meaningful.

Now it feels like throwing paper planes into a sky full of drones.

They fly faster.
They reach further.
They respond instantly.

Paper planes still matter.
But fewer people look up.

Books Versus Conversations

AI feels like a conversation.

Books feel like a lecture.

That difference matters.

People want interaction. They want follow-up questions. They want context tailored to their situation.

A book cannot ask back.

AI can.

And that changes expectations.

What Writing Used to Give Me

I did not write eBooks just for readers.

I wrote to think.

Writing forced clarity.
It slowed my thoughts.
It made experiences visible.

If I stop writing books, what replaces that?

Blogs?
Short posts?
Conversations?
Voice notes?

I do not know yet.

That uncertainty is unsettling.

Maybe Books Are No Longer the First Door

Here is a thought I am still wrestling with.

Books may no longer be entry points.
They may become reference points.

Not where people start, but where they return when they want depth.

AI gives direction.
Books give texture.

AI answers questions.
Books explain why the questions matter.

But fewer people reach that stage.

The Ego Check I Needed

Another truth I had to face.

I assumed free meant valuable.
I assumed experience meant relevance.

Neither guarantees attention.

The world does not owe writers readers.

Attention is earned every day.

Even by those who have written before.

Am I Really Stopping?

When I say I feel like stopping, I am not quitting writing.

I am questioning the format.

Maybe eBooks are not where my thoughts want to live anymore.

Maybe ideas want to breathe in smaller spaces.
Or in stories.
Or in conversations.

Or maybe fewer books, written slower, with deeper intent.

I am not sure yet.

What I Do Know

AI has changed how we read.
AI has changed why we read.
AI has changed when we read.

That shift is real. It is not a phase.

Fighting it feels pointless.

Understanding it feels necessary.

The Choice In Front of Me

I can keep writing eBooks and accept fewer readers.

I can stop writing books and find new ways to share ideas.

Or I can redefine what a book means in a world that no longer reads the same way.

Right now, I am sitting with the discomfort.

No dramatic announcement.
No final decision.

Just honesty.

A Quiet Ending With an Open Question

I still believe ideas matter.
I still believe stories shape thinking.
I still believe writing is worth doing.

But I no longer believe format guarantees relevance.

Maybe the real question is not whether I stop writing eBooks.

Maybe it is whether I am brave enough to write differently.

If you are a writer, a reader, or someone who quietly stopped reading books, I would love to hear your thoughts.

Have you felt this shift too?

Why is IoT Growing Now?

IOT NOTES

Lecture Notes

Based on 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]

Understanding Data Ownership and Big Data

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.

Based on eBook — IoT Notes by Mazlan Abbas

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]

Climbing the Wisdom Pyramid: Mastering the Stages of Data Transformation (DIKIW)

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]

Examples of IoT Applications and Their Benefits

Today, look at some real-world examples of IoT applications and their benefits.

This diagram focuses on two industries—transportation and logistics and Oil and gas —to illustrate how IoT solutions can reduce costs and improve efficiency. Let’s examine each example in detail.

Based on the eBook — IoT Notes by Mazlan Abbas

1. Transportation & Logistics

In this industry, IoT solutions are used to improve the management of vehicles and delivery systems.

IoT Solution:

  • Sensors are installed in vehicles to provide real-time data on their state and performance.
  • These sensors report on engine health, location, and delivery progress.

Benefits (Payback):

Cost Savings:

  • Reduces truck operation and delivery costs by identifying and addressing maintenance issues early.
  • Minimises fuel consumption by optimising delivery routes.

Improved Efficiency:

  • Reduces delivery disruptions and delays.
  • Ensures on-time deliveries and improves overall logistics system performance.

Example in Action: A logistics company uses IoT-enabled fleet management to track delivery vehicles and proactively schedule maintenance, avoiding breakdowns and saving costs.

2. Oil & Gas

The Oil & Gas industry faces unique challenges that IoT can address by enhancing safety and efficiency.

IoT Solution:

  • Sensors are deployed throughout an oil rig to monitor critical components.
  • These sensors detect potential issues and provide operators with real-time alerts.

Benefits (Payback):

Cost Savings:

  • Increases safety by providing early and reliable warnings of potential equipment failures.
  • Prevents costly shutdowns by identifying problems before they escalate.

Improved Efficiency:

  • Real-time alerts enable quicker responses, reducing downtime and improving time-to-revenue.
  • Enhances asset utilisation by ensuring equipment operates optimally (4x improvement).
  • Complete data analysis allows for near real-time decision-making and operational adjustments.

Example in Action: An oil rig uses IoT sensors to monitor drill performance and prevent malfunctions, ensuring smoother operations and faster oil extraction.

Key Takeaways

IoT in Transportation & Logistics:

  • Reduces costs and enhances delivery efficiency through real-time monitoring of vehicles.

IoT in Oil & Gas:

  • Improves safety, reduces downtime, and maximises asset utilisation with real-time alerts and analysis.

Final Thought:
IoT transforms industries by providing actionable data that saves money and improves efficiency. These examples show how IoT can adapt to specific industry needs and bring measurable benefits.

Discussion Question: Which other industries do you think can benefit from similar IoT solutions? Let’s brainstorm together!

[Download IoT Notes eBook for FREE]

Reasons to Consider IoT

IOT NOTES

Lecture Notes

Today, we’re going to discuss the key reasons why organisations should consider adopting IoT (Internet of Things).

The diagram highlights three main benefits: Gain, Retain, and Innovate. Let’s go through each of these in a simple and structured way.

1. Gain

The first reason to consider IoT is the opportunity to gain strategic advantages and stay competitive in the market.

Strategic Advantages: IoT allows businesses to reinvent their business models, offering smarter, data-driven services.

  • Example: A retail store using IoT to track customer preferences and improve in-store experiences.

Become Competitive: By adopting IoT, businesses can stay ahead of competitors who are slow to embrace technology.

  • Example: A logistics company using IoT sensors to optimise delivery routes.

Data-Driven Decisions: IoT provides real-time data, which helps in making better decisions faster.

  • Example: A manufacturer monitoring equipment performance to reduce downtime.

2. Retain

The second reason is about retaining the best employees and maintaining operational efficiency.

Attract and Retain Talent: Employees prefer organisations that use the latest tools and technology to make their work easier and more efficient.

  • Example: Providing engineers with IoT-enabled tools to monitor and control operations remotely.

Ease Workloads with Automation: IoT automates repetitive tasks, allowing employees to focus on higher-value activities.

  • Example: Smart systems in a factory automatically managing inventory levels.

When employees feel supported by modern technology, they are more likely to stay with the organisation.

3. Innovate

The third and perhaps most exciting reason is the ability to innovate with IoT.

Go-to-Market Strategies: IoT enables businesses to develop new strategies to attract and retain customers.

  • Example: A car rental company using IoT for keyless entry and real-time vehicle tracking.

New Products and Services: IoT opens doors to create entirely new offerings that improve the customer experience.

  • Example: Wearable health trackers offering personalised fitness insights.

Better Quality of Life: For individuals and society, IoT can enhance convenience, safety, and overall well-being.

  • Example: Smart homes with automated lighting and security systems.

Efficiency and Savings: IoT reduces costs by optimising resources and processes.

  • Example: Smart irrigation systems saving water in agriculture.

Key Takeaway

The main takeaway is simple: IoT is not just about adopting technology — it’s about creating value by:

  1. Gaining a competitive edge.
  2. Retaining top talent and improving efficiency.
  3. Innovating to improve products, services, and quality of life.

IoT is the key to staying relevant in today’s fast-changing world.

Let’s discuss: Which of these reasons resonates most with your business or personal goals? How can IoT help you achieve them? Let’s explore together!

[Download the FREE eBook— IoT Notes by Mazlan Abbas]

Building IoT Using the 3-Question Approach

Today, we will explore a structured way to implement IoT projects called the 3-Question Approach (W-W-H). This approach revolves around asking three key questions: What? Why? How?

Let’s review each step to understand how this framework ensures clarity and direction for IoT solutions.

Based on the eBook — IoT Notes by Mazlan Abbas

1. What? (Problem Statement)

The first question is What? which helps us identify the problem we’re trying to solve.

Purpose: To uncover the actual pain points or gaps in current processes.

Steps:

  • Look for inefficiencies or challenges in operations.
  • Identify areas where IoT can provide value.

Examples:

  • Low production rates in a factory.
  • Increasing operational costs.
  • Poor quality control leads to defects.
  • Safety incidents in a workplace.
  • Intermittent services in logistics or delivery.

Understanding the problem is the foundation of any successful IoT project.

2. Why? (Objective & End Goal)

Next, we ask Why?, which focuses on the objective and the end goal of the IoT implementation.

Purpose: Clearly define what you want to achieve and align everyone toward that goal.

Steps:

  • Get buy-in from stakeholders by explaining the benefits.
  • Ensure the outcome can be monitored and measured effectively.

Examples of Objectives:

  • Improve logistics and delivery efficiency.
  • Reduce operational costs.
  • Enhance asset utilisation and reduce errors.
  • Boost overall safety and performance.

Defining the “why” ensures a clear purpose behind the IoT deployment.

3. How? (Solutions)

The final question is How? which focuses on the solutions to address the problem and achieve the goals.

Steps:

Use the DNA approach:

  • D — Device: Identify the right sensors to collect the required data.
  • N — Network: Choose a suitable network for connectivity (e.g., Wi-Fi, LoRa, 5G).
  • A — Applications: Develop platforms (mobile or web) to process and display the data.
  • Decide on an implementation strategy:
  • Build in-house.
  • Operate internally.
  • Outsource to a third party.
  • Collaborate with partners.

Examples of Solutions:

  • Install sensors to monitor production lines in real time.
  • Ensure the network has sufficient coverage and can handle the amount of data.
  • Develop mobile or web applications to provide actionable insights.

The “how” ensures the project is feasible and actionable.

Key Takeaway

The 3-Question Approach provides a clear path for implementing IoT solutions:

  1. What? — Define the problem.
  2. Why? — Establish the objective.
  3. How? — Develop the solution.

This framework ensures every IoT project is focused, aligned, and effective. Think big, but start small.

Let’s discuss: Can you think of an example where this approach could be applied in your industry or daily life? What “What-Why-How” steps would you take? Share your thoughts!

[Download FREE IoT Notes by Mazlan Abbas]

Building IoT with the 3-Step Approach

Lecture Notes

Today, we will explore a simple and practical framework for implementing IoT projects: the 3-Step Approach.

This method ensures a smooth and effective rollout by focusing on small steps, integration, and innovation. Let’s break it down step by step.

Step 1: Think Big, Start Small

The first step is to start with a clear vision (think big) but begin with a small, focused project to gain momentum.

Develop a Small Application: Identify one specific problem and build a targeted solution.

  • Example: A smart thermostat to monitor energy usage in one building rather than an entire campus.

Deliver Immediate Impact: The solution should show quick results to build confidence in IoT’s value.

Mindset Transformation: This small success shifts how people perceive IoT and its potential.

Get Buy-In from Leadership: Demonstrating early results helps secure support from decision-makers like the C-suite executives for future projects.

Step 2: Integrate

Once the initial IoT solution proves its value, it is integrated into the broader system.

Connect to Legacy Systems: Ensure the new IoT solution works seamlessly with existing infrastructure, such as ERP systems or older databases.

  • Example: Integrating smart sensors into a factory’s traditional production line.

Seamless Workflow: Avoid disrupting operations by designing smooth processes between old and new systems.

Break Silos: Encourage collaboration between departments to maximise the benefits of IoT across the organisation.

This step ensures that IoT doesn’t operate in isolation but becomes a part of the larger ecosystem.

Step 3: Innovate

The final step is to use IoT to drive innovation and create new opportunities.

Create New Workflows: Leverage IoT to optimise or redesign how work is done.

  • Example: Using real-time data from IoT sensors to automate maintenance schedules.

Test New Business Models: Experiment with different ways to generate revenue using IoT solutions.

  • Example: Offering predictive maintenance as a subscription service for customers.

Better Analytics and AI: Use advanced analytics and AI to unlock deeper insights from IoT data and automate decision-making.

This stage transforms IoT from a problem-solving tool into a driver for long-term growth and innovation.

Key Takeaway: Think Big, Start Small

The overall lesson is simple:

  1. Start with a big vision, but focus on small, impactful projects to get started.
  2. Build on early successes by integrating solutions into larger systems.
  3. Use IoT to innovate and create new opportunities.

This approach minimises risk, builds momentum, and ensures sustainable growth.

Let’s discuss: What small IoT applications can you think of to start with? How would you scale and integrate them into a more extensive system? Let’s brainstorm together!

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