Exploring the Features of a Knowledge Society: Accelerated Growth of Knowledge

Walk into any university library and the sheer volume of books, journals, and digital databases tells a story. Knowledge that once took centuries to accumulate now expands within months. This explosion of recorded information sits at the heart of what scholars call a knowledge society, a stage of human development where knowledge becomes the most valuable resource a community can possess. Understanding its core features helps explain why education, research, and information professionals matter more than ever before. This post examines three defining characteristics: the accelerated growth of recorded knowledge, the different types of knowledge that drive progress, and the rising value of the knowledge held by individuals.

The accelerated growth of recorded knowledge 🔗

The most visible feature of a knowledge society is the rapid multiplication of recorded knowledge. Beginning around the eighteenth century, organised research became a major pursuit for creating new ideas, and the twentieth century turned that pursuit into a flood. Management thinker Peter Drucker observed that the twentieth century witnessed an unprecedented wave of change, driven not by wars or political upheaval but by intellectual activity and the systematic application of new ideas to material advancement.

What makes contemporary knowledge distinct is its pace. Scholars describe a “knowledge explosion” that is both quantitative and qualitative. The amount of disciplinary knowledge keeps rising, while at the same time entirely new disciplines and sub-disciplines emerge, many of them cutting across traditional boundaries. Alongside this growth comes a trend toward rapid obsolescence, where the gap between a discovery and its practical application keeps shrinking.

What drives this acceleration 🔗

Several forces work together to push knowledge production forward. The establishment of universities, research institutions, and specialised think tanks, supported by governments and industries, has created dedicated environments for generating recorded knowledge. The result is a steady stream of scholarly papers, technical reports, patents, and datasets.

The second major driver is technology. The growth of information and communication technology has dramatically increased the world’s capacity to create raw data and the speed at which it spreads. The internet, in particular, removed the physical and geographic barriers that once slowed the circulation of ideas. A research finding published in one country can be read, debated, and built upon across the world within hours.

The Indian picture 🔗

The acceleration of recorded knowledge is clearly visible at home. The country’s research output has grown almost six-fold in fifteen years, rising from roughly 34,000 articles in 2010 to about 195,000 in 2024, making it one of the fastest-growing producers of research in the world. Much of this output comes from centrally funded institution systems such as the IITs, the CSIR network, and central universities, which together account for a large majority of national research.

Policy has supported this momentum. The National Knowledge Commission, constituted in 2005, was a think-tank tasked with sharpening the country’s advantage in knowledge-intensive sectors. It advised on education reform, research promotion, intellectual property management, and the use of knowledge in agriculture, industry, and governance. Its recommendations aimed at building excellence in education to meet the knowledge challenges of the twenty-first century.

The types of knowledge 🔗

Not all knowledge behaves the same way. In a knowledge society, understanding how different kinds of knowledge flow and transform is essential, because each type demands a different approach to capture and share. Scholars generally identify three categories: tacit, explicit, and embedded knowledge.

Tacit knowledge 🔗

Tacit knowledge is the “know-how” lodged in the human mind through experience and practice. First described by philosopher Michael Polanyi in the 1960s, it includes intuitions, insights, skills, and judgments that are hard to put into words. A skilled surgeon’s sense of touch, a senior librarian’s instinct for where a misfiled record might be, or an artisan’s feel for clay are all examples. Because tacit knowledge is context-specific and difficult to extract and codify, it usually passes from one person to another through observation, imitation, and close interaction rather than through manuals.

Explicit knowledge 🔗

Explicit knowledge is the “know-what” that can be expressed in words and numbers. It is the knowledge codified in books, documents, reports, databases, scientific formulae, and standard procedures. Because it is formalised and recorded, explicit knowledge is comparatively easy to store, retrieve, and transmit across large groups of people. The library catalogue, the textbook, and the research journal are all vehicles for explicit knowledge. Importantly, explicit knowledge rests on a broad foundation of tacit knowledge; the words in a manual only make sense because the reader already carries unspoken understanding that fills in the gaps.

Embedded knowledge 🔗

Embedded knowledge is the knowledge locked into processes, products, routines, and organisational culture rather than held in a single mind or written page. Some researchers distinguish between knowledge embodied in people and knowledge embedded in processes, organizational culture, and routines. The workflow of a busy hospital, the design logic built into a software system, or the unwritten conventions that shape how a team operates all carry embedded knowledge. It is often invisible because it is performed without conscious thought.

How knowledge converts and grows 🔗

These types are not sealed boxes; they constantly interact. Ikujiro Nonaka and Hirotaka Takeuchi captured this in their influential SECI model, which describes knowledge moving through four modes of conversion: socialisation (tacit to tacit), externalisation (tacit to explicit), combination (explicit to explicit), and internalisation (explicit to tacit). A craft apprentice absorbs tacit skills by watching a master, a researcher turns intuition into a written paper, separate documents are merged into a new report, and a reader eventually internalises that written knowledge as personal understanding. This spiral is how knowledge is created and amplified, moving from the individual to the group to the whole organisation.

The knowledge of individuals 🔗

The third defining feature is the growing recognition that the knowledge held by individuals is a genuine form of capital. In earlier eras, wealth and power rested mainly on land, machinery, and financial capital. In a knowledge society, the most decisive resource is the expertise residing in people’s minds.

Intellectual capital and the knowledge worker 🔗

Peter Drucker coined the term knowledge worker in his 1959 book The Landmarks of Tomorrow. He defined knowledge workers as employees who apply theoretical and analytical knowledge, gained through formal training, to develop products and services. He predicted they would become the most valuable assets of a twenty-first-century organisation because of their productivity and creativity. Programmers, analysts, designers, researchers, and information professionals all fall into this group; their work, as Drucker put it, requires them to think for a living.

This human expertise forms the core of an organisation’s intellectual capital, the intangible assets that increasingly determine a company’s value. Unlike a factory machine that wears out with use, knowledge can be applied repeatedly without being depleted, which is why investment in intangibles is now widely seen as a source of competitiveness and prosperity. Drucker went so far as to argue that knowledge is not merely another resource alongside labour and capital, but the only truly important resource of our time.

Managing what people know 🔗

If individual knowledge is so valuable, organisations face a clear challenge: how to capture, preserve, and share it. This is the task of knowledge management, the discipline built largely around the relationship between tacit and explicit knowledge. The danger is obvious. When an experienced employee leaves, decades of tacit understanding can walk out of the door with them. Knowledge management practices, such as documentation, mentoring, communities of practice, and shared repositories, aim to convert that fragile tacit knowledge into more durable explicit forms wherever possible.

Technology now extends these efforts. Artificial intelligence tools help organisations sift through enormous volumes of information, surface relevant insights, and connect people with the knowledge they need. By automating the retrieval and organisation of explicit knowledge, AI frees knowledge workers to focus on the judgment, creativity, and problem-solving that machines cannot easily replicate. This is especially relevant in fast-moving sectors like information technology, where technical skills can become outdated within a few years and continuous learning becomes a professional necessity.

Why this matters for information professionals 🔗

Library and information science professionals sit at the centre of this picture. They enable access to recorded knowledge, preserve intellectual heritage, and guide people in navigating and evaluating information. As the volume of information swells, the ability to filter signal from noise becomes a vital skill. Strong information literacy helps individuals and communities cope with the cognitive overload that the knowledge explosion can produce, and it helps address the uneven access that persists between well-connected urban centres and rural areas with weaker digital infrastructure.

Bringing the features together 🔗

These three features reinforce one another. The accelerated growth of recorded knowledge produces an ever-larger pool of explicit knowledge. Understanding the difference between tacit, explicit, and embedded knowledge explains how that pool is created and how much remains locked inside people. And the rising value of individual expertise pushes organisations to manage knowledge deliberately, using tools from mentoring to artificial intelligence. Together they describe a society in which the capacity to create, organise, share, and apply knowledge has become the primary engine of social and economic development.

What do you think? If knowledge now doubles within a matter of months, how should students and professionals decide what is worth learning deeply versus what they can simply look up when needed? And as artificial intelligence takes over more of the work of organising explicit knowledge, which uniquely human forms of tacit knowledge do you believe will remain the hardest to replace?

References

1. https://unesdoc.unesco.org/ark:/48223/pf0000134422
2. https://en.wikipedia.org/wiki/Knowledge_society
3. https://stories.springernature.com/global-research-pulse-india/index.html
4. https://en.wikipedia.org/wiki/National_Knowledge_Commission
5. https://www.tlu.ee/~sirvir/Information%20and%20Knowledge%20Management/Key_Concepts_of_IKM/tacit_and_explicit_knowledge.html
6. https://knowledge-management-tools.net/different-types-of-knowledge
7. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2019.02730/full
8. https://corporatefinanceinstitute.com/resources/valuation/knowledge-workers/