Conceptual difference between Data, Information and Knowledge

1. Introduction

In an era where decisions, research, and policy increasingly rely on digital inputs, the distinction among data, information, and knowledge becomes critical. These terms often appear together but carry different meanings and implications. A clear understanding helps library and information science professionals in collection design, information retrieval, educating users, metadata management, and knowledge organisation.
A widely used framework that helps clarify these distinctions is the DIKW Pyramid (Data → Information → Knowledge → Wisdom). In this model, raw data is at the base; it transforms into information when contextualised, knowledge when processed, interpreted, and internalised, and ultimately into wisdom at the top when knowledge is applied in judgment and action.
The model originated in multiple disciplines, including information science, knowledge management, and philosophy. It underscores that value increases as we move from data to knowledge: each step adds meaning, relevance, context, structure and human interpretation. For LIS students, grasping the distinctions among data, information, and knowledge is foundational: it informs how we manage library resources, design digital systems, contribute to knowledge creation, and support users in navigating the information environment.

2. Meaning

Data
Data refers to raw, unprocessed facts and figures that lack context or interpretation. These may be numbers, symbols, characters, images, or observations collected through measurement, surveys, experiments, or records. Data alone does not explain relationships or meaning. For example, numbers such as 23, 27, and 31 are merely data until placed into context. Oxford Dictionary defines data as “facts and statistics collected together for reference or analysis”. In information science, data is considered the lowest level in the hierarchy of understanding, forming the foundation of information.

Information
Information is processed or organised data given meaning and relevance within a context. It becomes information when data is structured, categorised, or analysed to answer basic questions such as who, what, where, and when. For instance, the numbers 23, 27, 31 become information if presented as “average daily temperatures of a city for three consecutive days”.

Knowledge
Knowledge represents a higher level of understanding derived from information through experience, interpretation, reflection, and application. It answers “how” and “why” questions and supports judgment, reasoning, and informed action. For example, knowing that a consistent rise in daily temperatures indicates climate change patterns demonstrates knowledge.

Davenport and Prusak (1998) define knowledge as “a fluid mix of framed experiences, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information”.

In information science, knowledge is considered internalised information that guides decision-making and innovation.

Shannon and Weaver’s Information Theory sees information as reducing uncertainty in communication.

In LIS, information is viewed as data that has been interpreted and contextualised to support decision-making, problem-solving, and communication.

3. Conceptual Difference between Data, Information, and Knowledge