The proliferation of 'big data' can indeed lead to a qualitative change in knowledge. Big data refers to extremely large datasets that can be analysed computationally to reveal patterns, trends, and associations. This kind of data analysis can uncover insights that were previously unattainable due to the limitations of smaller datasets. For instance, big data allows for more nuanced and granular understandings of complex phenomena, from human behaviour to climate patterns. It also enables predictive analytics, which can foresee trends and outcomes, thereby extending the scope of knowledge. However, this shift is not without challenges. The reliance on big data can lead to an overemphasis on quantitative analysis, potentially overlooking qualitative aspects of knowledge such as context, interpretation, and human experience. Additionally, big data analytics can raise ethical concerns related to privacy, consent, and the potential for biased or inaccurate interpretations.

Data privacy and security are increasingly crucial in the management and perception of knowledge. In an era where vast amounts of personal data can be easily collected and analysed, concerns about privacy and security directly impact how individuals and organisations perceive and handle knowledge. Privacy concerns can lead to reluctance in sharing information, potentially hindering the collaborative nature of knowledge creation and distribution. On the other hand, inadequate data security raises the risk of sensitive information being misused or manipulated, which can lead to mistrust in the sources and content of knowledge. Additionally, regulations and policies around data privacy and security can shape the ways in which knowledge is accessed and used, influencing academic research, business practices, and public policy. The balance between protecting personal data and fostering an open, collaborative knowledge environment is a critical issue in the digital age.

Knowledge management in organisations is directly related to the transformation of data into knowledge. It involves collecting, organising, sharing, and analysing data to convert it into useful information and actionable knowledge. Effective knowledge management allows organisations to leverage their collective expertise and information resources to enhance decision-making, innovation, and strategic planning. This process involves not only technological tools, such as databases and AI, but also organisational strategies that foster a culture of knowledge sharing and collaboration. By efficiently managing the flow of information and ensuring that data is accurately interpreted and applied, organisations can create a knowledge base that supports their goals and adapts to changing environments. This practice highlights the importance of not only possessing data and information but also the ability to transform them into meaningful and applicable knowledge.

The digital divide – the gap between those with and without access to modern information and communication technology – significantly impacts the distribution of knowledge. In regions with limited access to the internet and digital resources, the ability to transform data into useful information and subsequently into knowledge is severely hampered. This divide not only limits access to existing knowledge but also restricts the ability of individuals in technologically underserved areas to contribute their knowledge and perspectives to the global digital repository. As a result, the knowledge that is widely available tends to reflect the perspectives and realities of more technologically advanced societies, creating a skewed global knowledge landscape. This inequity has profound implications for education, research, and policy-making, as it reinforces existing socio-economic disparities and hinders the development of a more inclusive and representative body of global knowledge.

The advent of artificial intelligence (AI) and machine learning has significantly expanded and complicated the concept of knowledge. Traditionally, knowledge has been viewed as a human-centric attribute, involving conscious understanding and interpretation. However, AI systems, through machine learning, now possess the ability to 'learn' from vast amounts of data, create patterns, and make decisions, a process akin to human knowledge acquisition. This raises philosophical questions about the nature of knowledge itself: Can machines truly 'know' something, or are they merely processing information without understanding? AI challenges the traditional boundary between data processing and knowledge, suggesting a new form of non-human or synthetic knowledge. This evolution invites a re-evaluation of knowledge, considering not just human cognitive processes but also the capabilities of intelligent machines. The implications are vast, impacting fields from ethics to epistemology, as it forces us to reconsider what constitutes knowledge and who or what can be a knower.