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

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.

2. Lesson 1: Data, Information, and Knowledge 

2. Lesson 1: Data, Information, and Knowledge 

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. 

"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.

"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. 

"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. 

2.5 The DIKW Hierarchy 

LEVEL:

Data 

Information 

Knowledge 

Wisdom

Data

QUSTION ANSWERED

What are the raw facts? 

DESCRIPTION

Unprocessed symbols, numbers, or signals with no context. 

Information 

QUESTION ANSWERED

Who / What / Where / When? 

DESCRIPTION

Data that has been organized and given context or structure. 

Knowledge 

QUESTION

How / Why? 

DESCRIPTION

Information combined with experience, rules, and interpretation, enabling action. 

Wisdom 

QUESTION

What is the best course of action, and why does it matter? 

DESCRIPTION

Applied knowledge guided by judgment, values, and long-term insight.

2.6 Why Distinguishing Data from Information Matters 

• Data overload without insight:

• Poor decision-making:

• Wasted IT investment:

• Missed opportunities:

• 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. 

2.6 Why Distinguishing Data from Information Matters 

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 

2.7.2 By Nature 

2.7.3 By Source 

2.7.1 By Structure 

• Structured data:

• Semi-structured datA

• Unstructured data

• Structured data:

• ighly organized and stored in fixed fields, such as rows and columns in a database or spreadsheet (e.g., sales records, employee IDs).