Understanding the Concept of Information in Information Systems
In the field of Information Systems, the concept of "information" is central yet multifaceted, often leading to confusion and debate among scholars and practitioners alike. The ambiguity surrounding the definitions of data, information, and knowledge has led to a variety of interpretations, each shaped by the origins and perspectives of the authors who discuss these terms. This blog post aims to delve into these complexities, exploring the different ways in which information is understood within the literature, the implications for information systems, and how these definitions influence our approach to managing and using information in digital environments.
Defining Data, Capta, Information, and Knowledge
To understand the concept of information, it's crucial first to distinguish it from related terms such as data, capta, and knowledge. Each of these terms occupies a specific place in the hierarchy of understanding that leads from raw symbols to meaningful insights.
Data refers to symbols that represent the properties of objects and events. These symbols, as described by Ackoff (1999), are in their raw format, unprocessed, and devoid of any inherent meaning. For instance, numbers recorded by a sensor or words typed into a document are data; they are the raw material from which information is derived.
Capta is a subset of data, specifically the portion that we have an interest in knowing. Checkland and Poulter (2006) describe capta as selected data that has been singled out because it is relevant or significant to a particular context or inquiry. Capta represents the first step in the transformation of data into something more meaningful.
Information is data that has been processed to improve its usefulness. This processing involves organizing, structuring, or interpreting data in such a way that it becomes meaningful. Information, therefore, is not just raw data but data that has been made comprehensible and applicable to a particular purpose. It turns the raw symbols of data into meaningful facts that can be used to make decisions or understand situations.
Knowledge represents a further step in this process. It consists of larger, more complex structures of meaningful facts. Knowledge is living and dynamic, shaped by the continual integration of new information with existing understanding. Checkland and Poulter (2006) describe knowledge as a structure that allows us to connect and understand the meaning within data and information. It is the result of processing information over time and applying it within the broader context of what is already known.
The Relationship Between Data and Information
The transition from data to information is a critical process in the field of information systems. As the smallest and most raw symbols, data serves as the foundation upon which information is built. However, data alone is not sufficient for making informed decisions; it must be processed and interpreted to become useful.
This transformation involves selecting relevant data and then organizing it in a way that reveals patterns, relationships, or insights (capta). Once capta has been processed into information, it can then be used to create knowledge. Knowledge, in turn, allows us to connect and understand the broader implications of the data we encounter.
Despite these clear distinctions in theory, the boundaries between data and information can become blurred in practice. For example, content that is traditionally thought of as raw data may have already been processed into information before being used for further analysis. This overlap can lead to confusion when discussing the quality and nature of the information, as the same data might be considered differently depending on how it has been processed and the context in which it is used.
The Ambiguity of Information
The concept of information is not only complex but also fraught with ambiguity. The term "information" is often used interchangeably with data and knowledge, which can lead to misunderstandings. As noted by Lee (2004), the interchangeable use of these terms has contributed to a lack of clarity in the field, making it challenging to establish a consistent understanding of what information truly represents.
Buckland (1991) highlights this ambiguity by defining four aspects of information: entity, process, tangible, and intangible. These aspects provide a framework for understanding the various ways in which information can be interpreted. For example, information as an entity could refer to knowledge or a physical document, while information as a process might involve being informed or the act of data processing.
This model suggests that information can be both tangible and intangible, depending on its form and context. Information contained within a document or an online web page is tangible, as it exists in a physical or digital form that can be accessed and used. However, information as knowledge is intangible, existing in the mind of the individual who understands and applies it.
The Subjectivity of Information
Mingers (1995) challenges the notion of information as an objective commodity, arguing that the same data or documents may not have the same impact on different users. This perspective highlights the subjective nature of information, suggesting that its value and meaning can vary depending on the individual's existing knowledge and the context in which the information is used.
For instance, a piece of information that is highly valuable to one person may be irrelevant or meaningless to another. This subjectivity underscores the importance of context in understanding and interpreting information. It also suggests that information is not a static or absolute entity but rather something that is shaped by the user's perspective and needs.
The subjectivity of information also raises questions about the role of information systems in managing and delivering information. If information is inherently subjective, how can systems be designed to ensure that the information they provide is meaningful and useful to all users? This challenge is particularly relevant in the context of the modern web, where vast amounts of information are available, but the quality and relevance of that information can vary widely.
Information in the Digital Age
The rise of the internet and digital technologies has transformed the way we create, share, and use information. In the digital age, information is more accessible than ever before, but this accessibility has also led to new challenges.
One of the key challenges is the sheer volume of information available online. With so much information at our fingertips, it can be difficult to discern what is relevant, accurate, and useful. This has led to concerns about information overload and the need for effective tools and strategies to filter and manage information.
Additionally, the digital age has blurred the lines between data, information, and knowledge. With the advent of big data, artificial intelligence, and machine learning, the process of transforming data into information and knowledge has become more complex. AI systems, for example, are capable of processing vast amounts of data to generate information and insights, but the knowledge they create is still dependent on human interpretation and application.
The concept of information quality has also gained prominence in the digital age. As information systems become more sophisticated, ensuring the quality of the information they provide is critical. This involves not only the accuracy and relevance of the information but also its accessibility, timeliness, and usability.
The Evolving Nature of Information
The concept of information is both fundamental and complex within the field of information systems. Despite numerous attempts to define and categorize information, it remains a term with multiple meanings and interpretations. The distinctions between data, capta, information, and knowledge provide a useful framework for understanding how raw symbols are transformed into meaningful insights, but these distinctions are not always clear-cut in practice.
The ambiguity and subjectivity of information present challenges for both researchers and practitioners. In a digital world where information is abundant and easily accessible, understanding what information is, how it is created, and how it is used is more important than ever. As we continue to navigate this complex landscape, the concept of information will likely continue to evolve, shaped by new technologies, changing contexts, and the diverse needs of users.
In the end, information is not just about data or documents; it is about meaning, understanding, and the ability to make informed decisions. Whether tangible or intangible, objective or subjective, information is the key to unlocking knowledge and enabling progress in our increasingly digital world.
Adapted from Muirhead (2022, pp.63–65).
References
Ackoff, R. L. (1999). Re-creating the corporation: A design of organizations for the 21st century. Oxford University Press.
Buckland, M. K. (1991). Information as thing. Journal of the American Society for Information Science, 42(5), 351–360. https://doi.org/10.1002/(SICI)1097-4571(199106)42:5<351::AID-ASI5>3.0.CO;2-3
Checkland, P., & Poulter, J. (2006). Learning for action: A short definitive account of soft systems methodology and its use for practitioners, teachers, and students. John Wiley & Sons.
Floridi, L. (2005). Is information meaningful data? Philosophy and Phenomenological Research, 70(2), 351-370. https://doi.org/10.1111/j.1933-1592.2005.tb00531.x
Lee, A. S. (2004). Thinking about social theory and philosophy for information systems. In J. Mingers & L. P. Willcocks (Eds.), Social theory and philosophy for information systems (pp. 1–26). John Wiley & Sons.
Mingers, J. (1995). Self-producing systems: Implications and applications of autopoiesis. Springer.
Muirhead, J. (2022). Informative web content guidelines: A practitioner model for online content effectiveness. (Thesis). University of Salford. Available online: https://salford-repository.worktribe.com/output/1327167/informative-web-content-guidelines-a-practitioner-model-for-online-content-effectiveness
