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]

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]