Understanding Information and Knowledge: Definitions and Differences

Walk into any library, log into any database, or open any search engine, and you are surrounded by two things that most people treat as identical: information and knowledge. We use these words almost interchangeably in everyday speech. Yet in Library and Information Science, the distinction between them is foundational. Understanding where information ends and knowledge begins shapes how we organise collections, design services, and help users find what they actually need. This post breaks down what these terms mean, how they relate to raw data, and why the gap between knowing a fact and truly understanding it matters more than it first appears.

Defining information and knowledge 🔗

Before we can separate these two concepts, we need clear definitions. Standard dictionaries give us a useful starting point. Authoritative reference works generally describe information as facts or details about a particular subject, and knowledge as the awareness, understanding, or skill a person gains through experience or education. As one widely used dictionary puts it, knowledge is the knowing of something, while information is what you can or cannot know. The two are related but not the same.

Dictionaries that focus on library and information science push this further. The Oxford English Dictionary traces “information” to the act of informing or shaping the mind, and “knowledge” to the state of knowing through familiarity and experience. Specialised online library science glossaries typically define information as data that has been processed, organised, and given context so it becomes meaningful, while knowledge is treated as information that a person has internalised, evaluated, and connected to what they already understand.

In short, information is something that exists outside us, sitting in a book, a file, or a record, waiting to be picked up. Knowledge lives inside a person. It is the result of taking that external information and making sense of it. This subtle difference is the thread that runs through the rest of this discussion.

Why definitions matter in practice 🔗

For a library professional, these definitions are not just academic. Cataloguing, indexing, and reference services all deal directly with information: organising it, describing it, and making it findable. Helping a user move from finding information to actually understanding it, however, is a different and harder task. A clear grasp of the two concepts helps information professionals design better services and set realistic expectations about what a library can and cannot deliver.

The relationship between data, information, and knowledge 🔗

Information and knowledge do not appear out of nowhere. They sit on top of something even more basic: data. The most widely used framework for explaining this progression is the DIKW hierarchy, which stands for Data, Information, Knowledge, and Wisdom. It is usually drawn as a pyramid, with data at the bottom and wisdom at the top. This model has been used widely within information science and knowledge management to explain how raw input becomes useful understanding.

From data to information 🔗

Data, on its own, is raw and largely meaningless. A list of numbers, a string of symbols, or a set of unconnected facts has no inherent value until someone gives it shape. The DIKW model holds that raw data without context lacks inherent value, and only becomes useful information once it is contextualised. The number “37” means nothing by itself. Attach it to a context, such as a person’s body temperature in degrees Celsius, and it suddenly carries meaning. That act of adding context, structure, and relevance is what converts data into information.

From information to knowledge 🔗

The next step is harder. Information becomes knowledge when a person analyses it, interprets it, and connects it to existing understanding. Knowledge is the result of analysing and interpreting information to uncover patterns, trends, and relationships, providing an understanding of how and why things happen. This is where experience enters the picture. Two people can read the same report, but the one who has worked in that field for years will extract far more from it than a newcomer.

At the top of the pyramid sits wisdom, the ability to apply knowledge and experience to make sound judgments. While wisdom is beyond the immediate scope of defining information and knowledge, it reminds us that the journey from raw data does not stop at simply knowing things. It is worth noting that the DIKW model is a simplification. Critics point out that the boundaries between the levels are not always sharp and that knowledge can develop in non-linear ways. Still, the model remains a clear and practical way to picture how value is added at each stage.

Communication and telecommunication in the transfer of knowledge 🔗

Information and knowledge are of little use if they stay locked in one place. They have to move, from a source to a receiver, from one mind to another, from one generation to the next. This movement is the job of communication. Communication is the process through which messages travel between a sender and a receiver, and it sits at the heart of every information service.

The communication process 🔗

One of the most influential explanations of how messages travel is the Shannon-Weaver model, first set out in the 1948 paper “A Mathematical Theory of Communication.” The model breaks communication into a clear sequence: an information source produces a message, a transmitter encodes it into a signal, a channel carries it, a receiver decodes it, and the message reaches its destination. A later addition introduced feedback, turning the model from a one-way line into a loop. The framework was so widely adopted that it is often called the “mother of all models” because of its wide popularity, and its main value lies in explaining how messages get lost or distorted along the way.

A key element in this model is noise, anything that interferes with the message during transmission. During the process, messages can be distracted or affected by physical noise such as sounds, or by distortion of the encoded signal during transmission. Noise explains why the message a receiver gets is not always the message a sender intended. For information professionals, reducing noise, whether through clear labelling, accurate metadata, or well-designed interfaces, is part of ensuring information reaches users intact.

Telecommunication and the wider reach of knowledge 🔗

Shannon developed his theory while working at Bell Labs to improve communication over the telephone. Telecommunication, the transmission of information over distance through electronic means, grew directly out of this work. The model was originally built to solve technical problems in telecommunication systems before being applied more broadly to human communication.

This matters enormously today. Telecommunication networks, the internet, and digital databases have removed the old limits of distance and time. A student can access a journal published on another continent, and a library can serve users who never physically enter the building. The transfer of information has become faster and cheaper than ever. Yet, as we will see, transferring information is not the same as transferring knowledge.

Why knowledge is different from information 🔗

Here we reach the central distinction. Information and knowledge are closely linked, but they differ in two important ways: the depth of comprehension involved and the role of personal experience.

Depth of comprehension 🔗

Information answers the surface questions: who, what, when, and where. Knowledge goes deeper, addressing why and how. Information brings about comprehension of facts, while knowledge leads to a genuine understanding of the subject. Knowing that a fire burns is information. Understanding why it burns and how to control it is knowledge. This depth allows a person to interpret, predict, and solve problems in ways that mere information cannot.

The role of experience 🔗

The second difference is experience. Information is static and easily transferred; it can be copied, stored, and sent at low cost. Knowledge is dynamic and personal because it is built through learning and reflection over time. When information is combined with a context into which it can be accurately placed, it begins to turn into knowledge. This is also why knowledge is harder to transfer. You can hand someone a document, but you cannot hand them your years of experience. They have to do the learning themselves.

This connects to a long tradition in philosophy. Epistemology, the branch of philosophy that studies the nature, origin, and limits of knowledge, distinguishes between knowing facts and knowing through familiarity and skill. The same idea appears in information science: a database can store information indefinitely, but knowledge requires a knowing mind to engage with it.

What this means for information services 🔗

Recognising this gap changes how we think about the work of libraries and information centres. Providing access to information is necessary but not sufficient. The real goal is to support users as they transform that information into knowledge through reading, study, and reflection. Information literacy programmes, reference guidance, and well-curated collections all exist to bridge this gap. The library supplies the raw material; the user builds the understanding.

What do you think? If two people read the exact same article but only one truly understands it, what does that tell us about where knowledge really lives? And as telecommunication makes information instantly available everywhere, do you think the challenge of building genuine knowledge becomes easier or harder?

References

1. https://www.britannica.com/dictionary/eb/qa/Knowledge-and-Information
2. https://www.isko.org/cyclo/dikw
3. https://www.ebsco.com/research-starters/library-and-information-science/dikw-pyramid
4. https://www.datacamp.com/cheat-sheet/the-data-information-knowledge-wisdom-pyramid
5. https://helpfulprofessor.com/shannon-weaver-model/
6. https://www.communicationtheory.org/shannon-and-weaver-model-of-communication/
7. https://en.wikipedia.org/wiki/DIKW_pyramid
8. https://keydifferences.com/difference-between-information-and-knowledge.html
9. https://www.psychologytoday.com/us/blog/snow-white-doesnt-live-here-anymore/202111/whats-the-difference-between-knowledge-and
10. https://en.wikipedia.org/wiki/Epistemology