Today, we’ll explore how the Internet of Things (IoT) transforms the 7 M’s of business — key elements that drive an organisation’s operations and strategy.

These 7 M’s are Manpower, Material, Method, Machine, Market, Money, and Management. Let’s break down each one and see how IoT impacts them.

1. Manpower

IoT helps businesses optimise human resources by reducing costs, improving safety, and increasing productivity.

Impact of IoT:

• Cost Reduction: Automating repetitive tasks reduces the need for manual labour.
• Worker Safety: IoT devices, such as wearables, can monitor health and alert workers to potential hazards.
• Productivity: By enabling remote work and real-time communication, IoT allows employees to focus on high-value tasks.

Example: A construction company using wearables to monitor worker fatigue and ensure safety.

2. Material

IoT ensures better management of materials, improving supply chain efficiency and reducing waste.

Impact of IoT:

• Just-In-Time Delivery: Sensors track inventory levels and automatically reorder materials when needed.
• Asset Condition Monitoring: IoT devices monitor the condition of materials, ensuring quality and preventing spoilage.

Example: A warehouse using IoT sensors to track stock levels and ensure optimal storage conditions.

3. Method

IoT makes business processes more agile and efficient by simplifying methods.

Impact of IoT:

• Reduce Red Tape: Automating workflows eliminates unnecessary administrative steps.
• Agility: IoT enables businesses to respond quickly to changing conditions.
• Efficiency: Processes become faster and more streamlined with IoT integration.

Example: A manufacturing plant automating quality checks with IoT sensors to speed up production.

4. Machine

IoT maximises the performance of machines, ensuring reliability and reducing downtime.

Impact of IoT:

• Uptime: Predictive maintenance ensures machines are operational when needed.
• Predictive Maintenance: IoT sensors detect issues before they become critical, preventing failures.
• Error Reduction: Machines can self-correct or alert operators when errors occur.

Example: A factory using IoT-enabled machinery to monitor performance and schedule maintenance.

5. Market

IoT helps businesses expand into new markets and improve their customer reach.

Impact of IoT:

• New Market Segments: IoT enables innovative products and services, opening new revenue streams.
• Global Reach: Businesses can monitor and manage operations worldwide through IoT platforms.

Example: An IoT-enabled home security company entering international markets with smart security systems.

6. Money

IoT creates new revenue opportunities and reduces costs.

Impact of IoT:

• New Revenue Streams: IoT drives innovation, leading to new services and products.
• Cost Savings: Automating processes and improving efficiency reduces expenses.

Example: A logistics company saving fuel costs by using IoT to optimise delivery routes.

7. Management

IoT improves decision-making through data-driven insights.

• Impact of IoT:
• Data-Driven Decisions: Real-time data helps managers make informed choices.
• Transparency: IoT provides visibility into all areas of the business.
• Better Decision-Making: Analytics from IoT systems offer actionable insights.

Example: A retail chain using IoT to monitor sales trends and optimise inventory.

Key Takeaways

IoT has a transformative impact on the 7 M’s of business:

1. Manpower: Reduces costs and improves safety.
2. Material: Ensures quality and efficiency.
3. Method: Simplifies workflows and increases agility.
4. Machine: Enhances reliability and performance.
5. Market: Expands opportunities globally.
6. Money: Generates new revenue and reduces costs.
7. Management: Improves decisions with real-time insights.

Discussion Question: Which of the 7 M’s most benefits from IoT in your industry? Let’s share ideas and examples!

{You can download the FREE eBook IoT Notes by Mazlan Abbas]

Today, we’ll discuss types of analytics and their importance in turning raw data into actionable insights.

This diagram shows four types of analytics, ranked by their difficulty level and the value they provide. Let’s go through them step by step.

1. Descriptive Analytics: What Happened?

At the base of the analytics hierarchy is descriptive analytics. This is the simplest form of analytics and helps us understand what happened by interpreting historical data.

• Purpose: To summarise past events and identify patterns.
• Example: A smart thermostat that shows last week’s energy usage patterns.
• Methods: Charts, graphs, and dashboards that clearly show past performance.

This type of analytics is great for reviewing the past, but it doesn’t tell us why something happened or what will happen next.

2. Diagnostic Analytics: Why Did It Happen?

Moving up, we have diagnostic analytics, which looks at why something happened. It’s more complex than descriptive analytics because it requires diving deeper into the data.

• Purpose: To discover relationships and identify the causes behind past events.
• Example: Analysing why a specific day’s energy usage was higher than average by correlating data with external factors like weather.
• Methods: Data discovery, drill-down techniques, and correlation analysis.

This stage helps us make sense of the past by understanding the root causes of trends and anomalies.

3. Predictive Analytics: What Will Happen?

Next is predictive analytics, which focuses on forecasting future outcomes. This is where analytics becomes proactive rather than reactive.

• Purpose: To predict what might happen based on current and historical data.
• Example: A smart thermostat forecasting energy usage for the upcoming week based on weather patterns and past behaviour.
• Methods: Statistical modelling and simulations.

By identifying trends and patterns, predictive analytics helps us make informed predictions.

4. Prescriptive Analytics: How Can We Make It Happen?

At the top is prescriptive analytics, the most advanced type. This involves predicting outcomes and recommending actions to achieve desired results.

• Purpose: To decide the best course of action based on predictions.
• Example: A smart thermostat automatically adjusting settings to save energy while maintaining comfort.
• Methods: Machine learning and AI to analyse probabilities and make decisions.

Prescriptive analytics provides the highest value by enabling automated and data-driven decisions.

IoT and Analytics

This diagram also highlights how analytics works in an IoT platform:

1. Sensors: Collect data from various sources like temperature, humidity, or movement.
2. IoT Platform: Acts as a central hub to process and store the data.
3. Analytics Engine: Applies these four types of analytics to generate insights and drive decisions.

Final Thoughts

Each type of analytics builds on the previous one, moving from simple data interpretation to actionable decisions. The value increases as we move up the hierarchy, as does the complexity.

Question to consider: Which type of analytics is most valuable in your industry, and how can you implement it effectively? Let’s discuss it!

[Note: Download IoT Notes by Mazlan Abbas ]

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]