The Differences and Linkages between Data Science and Information Science
The Differences and Linkages between Data Science and Information Science
Fred Y. Ye, International Joint Informatics Laboratory, Nanjing University
Fei-Cheng Ma, School of Information Management, Wuhan University
As we tread deeper into the digital age, a sophisticated understanding of Data Science and Information Science has become more critical than ever before. These two distinct yet interconnected fields underpin the structures of our societies, economies, and technologies, offering unique methodologies to interpret the vast landscapes of data and information that define our world.
Data Science, as a research field, places its central focus on data. It involves a thorough investigation of the various types, states, attributes, and changes of data, as well as the rules governing these changes. This discipline is not merely limited to studying data in isolation. Instead, it introduces a novel methodology for conducting research in natural and social sciences, driven primarily by data. The heart of Data Science is to shed light on the phenomena and laws that govern natural and social data.
—Data Science and Information Science, while distinct, are intertwined fields that each bring their unique focus and methodology to the table.—
To achieve this, data scientists research theories and methods of data reasoning. They establish experimental methods of data processing and embark on exploratory research into the world of data using these theoretical systems and experimental methods. This process aims to comprehend data types, states, attributes, changes, and the rules of change, ultimately revealing the underlying phenomena and laws of natural and social data.
In contrast, Information Science has a different focus. It is a discipline committed to understanding the laws governing the generation, transmission, and utilization of information. Modern Information Science particularly concerns itself with leveraging information technology and means to optimize the information exchange process for maximum efficiency. It places emphasis on enhancing the efficiency of information processing, storage, retrieval, communication, and utilization. The ultimate objective is to enable people to utilize information technology to its fullest extent and make the most of the information available.
Despite the distinct focus and methodologies of these two fields, there exists a profound interplay between them, depicted by the Data-Information-Knowledge-Wisdom (DIKW) hierarchy. This model is often visualized as a pyramid or a logic chain, representing the progression from the foundational level of data, through information and knowledge, to wisdom at the apex.
In this hierarchy, data and information are part of the objective field, while knowledge and wisdom belong to the subjective field. The journey from data to information constitutes an objective process, whereas the transition from knowledge to wisdom is a subjective process. A pivotal transformation occurs between information and knowledge, where the element of value judgment is added.
To quantify the abstract concept of the DIKW chain, several theoretical models and mathematical equations have been proposed. One such model, inspired by Shannon’s Information Theory, begins by converting objective data into physical information. This physical information is measurable and can be verified using physical instruments. Following this, the physical information transitions into objective information through a social transmission process. The objective information then transforms into subjective information via subject absorption, at which point it carries a subject value judgment.
Subsequently, the subjective information is restructured and systemized to form knowledge. This knowledge, when applied extensively, evolves into wisdom. It’s important to note that the transition from information to knowledge is a key moment in the chain, where the abstract concept of value judgment is integrated.
The understanding and effective application of Data Science and Information Science necessitate the recognition of common principles that guide these fields. There are six similar principles: the principle of order, the principle of correlation, the principle of reorganized transformation, the principle of scatter distribution, the principle of logarithmic perspective, and the principle of least effort.
Each principle offers a unique perspective on how data and information are processed, manipulated, and interpreted, thereby serving as foundational linkages between Data Science and Information Science. For instance, the principle of order highlights the existence of both objective natural order and subjective artificial order in data and information, emphasizing the importance of minimizing the difference between the two during data and information processing. The principle of least effort, on the other hand, is based on the understanding that the laws governing the changes in data and information aim to minimize the total expenditure of effort.
In conclusion, Data Science and Information Science, while distinct, are intertwined fields that each bring their unique focus and methodology to the table. Through their interplay and the shared principles that underpin them, they offer profound insights into our understanding of the world. This nuanced comprehension of the two fields is indispensable in a world where data and information increasingly serve as the foundation of our lives.
Source: Ye, F.Y. & Ma, F.-C. (2023). An essay on the differences and linkages between data science and information science. Data and Information Management, DOI: 10.1016/j.dim.2023.100032
Cite this article in APA as: Ye, F. Y. & Ma, F-C. (2023, June 6). The differences and linkages between data science and information science. Information Matters, Vol. 3, Issue 6. https://informationmatters.org/2023/06/the-differences-and-linkages-between-data-science-and-information-science/
