MODULE 2: Data, Information, and Knowledge | Sensors and the Internet of Things (IoT)

1. Module Overview 

1.1 Introduction 

Every organization, from a small sari-sari store to a multinational manufacturing plant, runs on a chain that begins with raw facts and ends with better decisions. That chain is: data → information → knowledge. Module 2 unpacks the first link in that chain and then connects it to the physical world through sensors — the devices that generate much of today's raw data in the first place, especially inside the Internet of Things (IoT). 

This manual is organized into two lessons. Lesson 1 builds your conceptual foundation on data, information, and knowledge, and shows how information technology (IT) systems convert raw data into knowledge an organization can act on. Lesson 2 shifts to the physical layer of IT — sensors — and shows how they capture the data that powers modern IoT systems and industrial operations. 

1.2 Learning Outcomes 

By the end of Module 2, you should be able to: 

1. Recognize the distinction between data and information. 

2. Differentiate the types and uses of data and information. 

3. Explore and explain real-world examples of data versus information. 

4. Discuss the role of information technology systems in converting data into organizational knowledge. 

5. Explore the different types of sensors and their uses. 

6. Examine the application of sensors in industrial processes. 

7. Analyze how sensors and IoT technologies are deployed across industrial sectors, including challenges, opportunities, and best practices. 

1.3 Course Outcome Alignment 

1.4 How to Use This Manual 

• Read each lesson in order — Lesson 2 (sensors) builds on the data concepts introduced in Lesson 1. 

• Attempt the Self-Check Questions at the end of each lesson before checking the Answer Key. 

• Study the Key Terms glossary in Section 4 alongside the lessons, not just before the exam. 

• Use the Case Analysis in Section 5 to practice applying concepts, since Case Analysis is a graded performance-based assessment for this module. 

• Bring the comparison tables (Section 2.9 and 3.3) to class — they are designed for quick recitation review. 

2. Lesson 1: Data, Information, and Knowledge 

2.1 Introduction 

In everyday speech, people use "data," "information," and "knowledge" as if they mean the same thing. In IT and business, they do not. Confusing the three is one of the most common reasons projects fail to deliver value: an organization can collect enormous amounts of data and still make poor decisions if that data is never converted into usable information and, eventually, into applied knowledge. 

2.2 Defining Data 

Data refers to raw, unorganized facts, figures, or symbols that by themselves carry no context or meaning. Data can be numbers, words, images, measurements, or signals collected through observation, transactions, or sensors. 

Example 

• "27", "Manila", "08:15", and a barcode scan are all data — isolated facts with no context attached yet. 

2.3 Defining Information 

Information is data that has been organized, processed, structured, or given context so that it becomes meaningful and useful for the person receiving it. Information answers questions such as who, what, where, and when. 

Example 

• "The temperature in Manila at 8:15 AM was 27°C" is information — the raw numbers and words from the data example now have context and meaning. 

2.4 Defining Knowledge 

Knowledge is the understanding, experience, and insight gained by interpreting and applying information over time. Knowledge allows a person or organization to make decisions, solve problems, and predict outcomes — it answers how and why. 

Example 

• "Manila temperatures above 27°C by mid-morning in April typically signal a hot, high-demand day for the beverage business, so we should increase cold-drink inventory" is knowledge — it applies experience and judgment to information in order to guide action. 

2.5 The DIKW Hierarchy 

A useful mental model for this progression is the DIKW Hierarchy (Data → Information → Knowledge → Wisdom), often drawn as a pyramid. As you move up the pyramid, volume decreases while meaning, context, and value increase. 

2.6 Why Distinguishing Data from Information Matters 

Organizations that fail to separate data from information often experience: 

• Data overload without insight: large databases that no one actually uses to make decisions. 

• Poor decision-making: relying on raw, unverified numbers instead of properly contextualized information. 

• Wasted IT investment: systems built to store data but not designed to transform it into usable information. 

• Missed opportunities: patterns in the data (e.g., seasonal demand) go unnoticed because no one converts data into information and, later, into knowledge. 

Correctly distinguishing the three levels helps organizations design better information systems, ask better questions of their data, and ultimately compete more effectively. 

2.7 Types and Uses of Data 

2.7.1 By Structure 

• Structured data: h

• Semi-structured data: has some organizational markers but is not as rigid as structured data (e.g., emails, XML/JSON files). 

• Unstructured data: has no predefined format (e.g., videos, social media posts, photos, free-text documents). 

2.7.2 By Nature 

• Quantitative data: expressed in numbers and measurable quantities (e.g., temperature readings, sales figures). 

• Qualitative data: descriptive and non-numeric, capturing qualities or characteristics (e.g., customer feedback comments, color). 

2.7.3 By Source 

• Primary data: collected firsthand for a specific purpose (e.g., a survey conducted by the researcher). 

• Secondary data: collected by someone else and reused (e.g., government census data used in a school project). 

2.8 Types of Information 

Organizations typically classify the information they use by the management level it supports: 

Information can also be classified as formal (official reports, financial statements) or informal (word-of-mouth updates, quick verbal briefings). 

2.9 Data vs. Information: Side-by-Side Comparison 

2.10 Role of Information Technology Systems in Converting Data to Organizational Knowledge 

Information technology systems are the engines that move an organization up the DIKW hierarchy — collecting raw data, processing it into information, and supporting the analysis that turns information into applied knowledge. Common categories include: 

• Transaction Processing Systems (TPS): capture and store the day-to-day raw data generated by basic business operations (e.g., point-of-sale transactions, payroll entries). 

• Management Information Systems (MIS): summarize and organize TPS data into routine reports for middle managers, turning data into information. 

• Decision Support Systems (DSS): apply models, simulations, and analytics to information so managers can evaluate options — an early step toward knowledge. 

• Executive Support Systems (ESS): give top executives high-level dashboards and trend analyses to support strategic knowledge and decision-making. 

• Knowledge Management Systems (KMS): capture, store, and share the lessons, best practices, and expertise an organization has learned so this knowledge is not lost when employees leave. 

In short: TPS generates and stores the data; MIS and DSS convert that data into meaningful information and decision options; and KMS helps preserve the resulting knowledge so the whole organization — not just one employee — can reuse it. 

Quick Check 

• Ask yourself: at your school or workplace, which system captures raw data first? Which report turns that data into information you actually read? Who or what preserves the lessons learned afterward? 

2.11 Lesson 1 Summary 

• Data is raw and context-free; information is data with context and structure; knowledge is information combined with experience that enables action. 

• The DIKW hierarchy (Data–Information–Knowledge–Wisdom) shows how meaning and value increase while volume decreases as you move upward. 

• Data can be classified by structure (structured/semi-structured/unstructured), nature (quantitative/qualitative), and source (primary/secondary). 

• Information can be strategic, tactical, or operational, and formal or informal. 

• IT systems — TPS, MIS, DSS, ESS, and KMS — work together to convert raw data into organizational knowledge. 

2.12 Self-Check Questions – Lesson 1 

8. Explain, in your own words, why "45 kg" is data while "the package weighs 45 kg, which is over the courier's 20 kg limit" is information. 

9. Give one original example each of structured, semi-structured, and unstructured data from your own daily life. 

10. A store manager receives a daily report listing total sales per branch. Which type of information is this — strategic, tactical, or operational? Justify your answer. 

11. Describe how a Transaction Processing System (TPS) and a Decision Support System (DSS) work together to help a company move from data to knowledge. 

12. Why might an organization with a large database still make poor decisions? Relate your answer to the DIKW hierarchy. 

3. Lesson 2: Sensors and the Internet of Things (IoT) 

3.1 Introduction 

If Lesson 1 explained what happens once data exists, Lesson 2 explains where a huge share of today's data comes from in the first place: sensors. Sensors are the "senses" of modern IT systems — they observe the physical world and convert what they detect into digital data that computers, dashboards, and IoT platforms can process, turning raw environmental signals into the very first link of the DIKW chain. 

3.2 What Is a Sensor? 

A sensor is a device that detects and responds to a physical input — such as temperature, light, motion, pressure, or sound — from the environment and converts that input into a signal, usually electrical, that can be measured, recorded, or transmitted for processing. In simple terms, a sensor turns a real-world physical event into raw digital data. 

3.3 Sensor Classification 

Sensors are commonly classified along several dimensions: 

3.4 Different Types of Sensors in IoT 

The Internet of Things relies on many sensor types working together to build a complete picture of an environment or process: 

• Temperature sensors: measure heat levels; used in HVAC systems, cold-chain logistics, and industrial furnaces. 

• Humidity sensors: measure moisture in the air; used in agriculture, greenhouses, and warehouses. 

• Proximity sensors: detect the presence or absence of a nearby object without physical contact; used in assembly lines and smartphones. 

• Pressure sensors: measure force applied to a surface; used in tire monitoring, hydraulic systems, and weather stations. 

• Motion / PIR (Passive Infrared) sensors: detect movement; used in security systems and smart lighting. 

• Light sensors (photodetectors): measure ambient light levels; used in smart street lighting and display brightness control. 

• Gas sensors: detect the presence of specific gases (e.g., CO2, methane); used for safety monitoring in factories and mines. 

• Level sensors: measure the level of liquids or solids in a container; used in fuel tanks and silos. 

• Accelerometers and gyroscopes: measure motion, orientation, and vibration; used in wearables, vehicles, and structural health monitoring. 

• Image sensors (cameras): capture visual data for object recognition and quality inspection. 

• GPS / location sensors: track the geographic position of assets; used in fleet management and logistics. 

3.5 Application of Sensors in Industrial Processes 

In industrial and manufacturing settings, sensors are the foundation of automation and smart operations: 

• Predictive maintenance: vibration and temperature sensors detect early signs of equipment wear so parts can be repaired before a costly breakdown occurs. 

• Quality control: image sensors and dimensional sensors inspect products on the line to catch defects in real time. 

• Process monitoring: pressure, flow, and temperature sensors keep chemical or manufacturing processes within safe, efficient operating ranges. 

• Safety and environmental monitoring: gas and smoke sensors protect workers by triggering alarms or automatic shutdowns. 

• Automation and robotics: proximity and vision sensors allow robotic arms to locate, grip, and place items accurately. 

• Supply chain and inventory tracking: RFID and level sensors provide real-time visibility of stock and shipments. 

Industry Connection 

• In a semiconductor fabrication plant, for example, temperature and humidity sensors keep cleanroom conditions within extremely tight tolerances, while vibration sensors on precision equipment support predictive maintenance schedules — both are direct, real-world applications of the sensor types described above. 

3.6 IoT Platforms 

An IoT platform is the software and cloud infrastructure that connects sensors and devices to the internet, manages them, collects and processes the data they generate, and makes that data available to applications and users. An IoT platform typically provides: 

• Connectivity management: linking sensors and devices to networks (Wi-Fi, cellular, LPWAN, Bluetooth). 

• Device management: registering, monitoring, and updating connected devices remotely. 

• Data collection and storage: ingesting the raw sensor data and storing it for processing. 

• Data processing and analytics: cleaning, analyzing, and visualizing data to generate usable information. 

• Application enablement: providing tools (APIs, dashboards) for building apps on top of the collected data. 

Examples of widely used IoT platforms include: 

Together, sensors and IoT platforms form the physical-to-digital bridge described in Lesson 1: sensors generate raw data, IoT platforms collect and process that data into information, and organizations apply analytics and human judgment to turn that information into operational knowledge. 

3.7 Lesson 2 Summary 

• A sensor detects a physical input and converts it into a measurable, transmittable signal — usually the origin point of raw data in an IoT system. 

• Sensors can be classified as active/passive, analog/digital, contact/non-contact, and by the type of quantity measured. 

• Common IoT sensor types include temperature, humidity, proximity, pressure, motion, light, gas, level, accelerometer/gyroscope, image, and GPS sensors. 

• In industry, sensors enable predictive maintenance, quality control, process monitoring, safety, automation, and supply-chain tracking. 

• IoT platforms (e.g., AWS IoT, Azure IoT, Google Cloud IoT) connect sensors to the cloud and turn raw sensor data into usable information for applications and decision-makers. 

3.8 Self-Check Questions – Lesson 2 

13. Differentiate an active sensor from a passive sensor and give one real-world example of each. 

14. Name three sensor types you would install in a smart warehouse and explain what each one would monitor. 

15. Explain how a vibration sensor supports predictive maintenance instead of simply reacting to a breakdown. 

16. What is the role of an IoT platform, and how does it relate to the data-to-information conversion process from Lesson 1? 

17. Identify one challenge and one opportunity a factory might face when deploying sensors and IoT technology on its production line. 

4. Module 2 Key Terms / Glossary 

5. Module 2 Case Analysis Activity 

Scenario: Bright Harvest Poultry Farm 

Bright Harvest is a mid-sized poultry farm that currently records temperature and feed levels manually, twice a day, on a paper clipboard. The farm owner has noticed inconsistent chick growth and occasional unexplained losses, especially during hot weather. She is considering installing an IoT-based monitoring system throughout the poultry houses. 

Guide Questions 

18. Identify what would count as data, information, and knowledge in this scenario once an IoT system is installed. 

19. Recommend at least three types of sensors Bright Harvest should install, and justify each choice. 

20. Explain what an IoT platform would need to do with the sensor data to make it useful to the farm owner. 

21. Describe one opportunity and one challenge Bright Harvest may face in adopting this technology (e.g., cost, connectivity, staff training). 

22. Propose one way the farm could turn the resulting knowledge into a long-term improvement ("wisdom") for its operations. 

Suggested Output Format 

• A short written case analysis (1–2 pages) or a slide deck, as assigned by your instructor. 

• Cite specific concepts from Lessons 1 and 2 (e.g., name the sensor types and IT system categories you use). 

• Case analyses are graded as part of the 50% performance-based assessment component for this term, per the course grading system.