Data-information-knowledge-wisdom Research Articles

Investigating wisdom: Call for research

ABSTRACTThis study aims to address the gap in understanding “wisdom” as it relates to Data, Information, Knowledge, Wisdom (DIKW) Model/Hierarchy and the information science discipline. Disciplines like data science, information science, and knowledge management study wisdom without defining it. This study identifies the definition of the term wisdom within the information science discipline.

Comparing the Definitions of Data and Information in Data Protection Law and Machine Learning: A Useful Way Forward for Algorithmic Regulation?

The aim of this paper is to make a detour through the notion of information (e.g., Adriaans, van Benthem, 2008) in order to explore whether it can bring useful insights concerning the regulation of algorithms and machine learning from a data protection law perspective. The notion of information is central both to data protection law (which defines personal data as “any information relating to an identified or identifiable natural person”), and to algorithms/machine learning, which are predicated upon the possibility of extracting information from data, pursuant to the canonical DIKW (data, information, knowledge, wisdom) pyramid, as first conceptualized by Ackoff (1989). It is this centrality that gives the impressions that algorithms and machine learning processes are just yet another data processing operation to be regulated. However, current debates around the data protection law-oriented regulation of algorithms show that there are still broad interrogations concerning the protection afforded thereby (see Bygrave, 2017; Wachter, Mittelstadt, Floridi, 2017). More radical critics argue that data protection is simply ill-suited for such regulatory purpose (see de Vries, 2016; Gurses, van Hoboken, 2017). This contribution echoes this last strand of criticism insofar as it argues that the under-explored meaning of information might account for such inadequacy. Albeit mobilized by both practices (data protection law and machine learning), it seems that information and its meaning are taken for granted, and in so doing justify the regulatory reach of data protection in the first place. Yet, as it transpires from debates in data protection law (see Bygrave, 2014; Hallinan and De Hert, 2016), the meaning of personal data and information (which is a core component of the latter) are far from being clear. Furthermore, the present conceptual exploration shows that the meaning of information as it is deployed in algorithms/machine learning is in any case dissimilar therefrom. Therefore, in unpacking how both practices frame and mobilise different notions of information, this contribution aims at showing that data protection law may not necessarily be the most adequate framework for regulating algorithms and machine learning; and possibly at showing some alternative way(s) forward.

Implementing a DIKW model on a deep mine cooling system

The South African mining industry has been experiencing increasing economic pressure. Deep mines also suffer from very hot workplaces, which leads to safety risks. These factors place stress on managers to reach their production targets while providing safe workplace conditions. The data information knowledge wisdom (DIKW) model, also known as the wisdom hierarchy, was implemented on a deep mine cooling system. This study aims to show that a simple model such as the DIKW model can assist managers in improving their deep mine cooling system’s performance. The study found that the DIKW approach is a suitable approach for use on mine cooling systems to facilitate operational improvements. Applying the DIKW approach to a case study on a mine cooling system created substantial awareness and facilitated a cooling duty improvement of 55% which relates to an increase of 5.3 MW of refrigeration. The results of this study indicate that the DIKW approach may be a suitable approach to optimise management on deep mines using their existing infrastructure.

A DIKW Architecture for Cognitive Engineering

Though the word “cognitive” has a wide range of meanings we define cognitive engineering as learning from brain to bolster engineering solutions. However, giving an achievable framework to the process towards this has been a difficult task. In this work we take the classic data-information-knowledge-wisdom (DIKW) framework to set some achievable goals and sub-goals towards cognitive engineering. A layered framework like DIKW aligns nicely with the layered structure of pre-frontal cortex. And breaking the task into sub-tasks based on the layers also makes it easier to start developmental endeavours towards achieving the final goal of a brain-inspired system.

Surveillance during monitoring phase of an eradication programme against Anoplophora glabripennis (Motschulsky) guided by a spatial decision support system

In 2003, the Canadian Food Inspection Agency (CFIA) undertook an eradication programme against Anoplophora glabripennis (Motschulsky), a phloem- and wood-boring cerambycid beetle from Asia discovered in Toronto and Vaughan, Ontario. The programme included a multi-year surveillance phase in a quarantine area. We developed a spatial decision support system (SDSS) to aid in effective and efficient allocation, tracking, forecasting, and reporting of these surveillance activities. We parameterised our SDSS with data collected by the CFIA during the treatment phase, and from existing policy, scientific and expert knowledge. The SDSS operated within a multi-loop adaptive management framework designed to foster four levels of organisational learning, analogous to the Data–Information–Knowledge–Wisdom hierarchy, to address uncertainty in founding parameters. Increasingly complex feedback was escalated to higher levels of programme management for review, and then integrated into the SDSS for future implementation. Numerous enhancements were approved and implemented during both the SDSS development and operation because of organisational learning fostered by the system. Two basic forms of learning methods were used: visual pattern recognition within graphical SDSS outputs, and hypothesis testing through re-evaluation of founding precepts and parameters. The system successfully guided surveillance until pest-free status was declared nine survey-cycles later, the first four leading to the discovery of small, residual beetle populations. We believe our system is flexible enough to successfully integrate different precepts and parameters, and be implemented as a best practice against this species or other organisms.

The role of digital technologies for the service transformation of industrial companies

The role of digital technologies in service business transformation is under-investigated. This paper contributes to filling this gap by addressing how the Internet of things (IoT), cloud computing (CC) and predictive analytics (PA) facilitate service transformation in industrial companies. Through the Data–Information–Knowledge–Wisdom (DIKW) model, we discuss how the abovementioned technologies transform low-level entities such as data into information and knowledge to support the service transformation of manufacturers. We propose a set of digital capabilities, based on the extant literature and the findings from four case studies. Then, we discuss how these capabilities support the service transformation trajectories of manufacturers. We find that IoT is foundational to any service transformation, although it is mostly needed to become an availability provider. PA is essential for moving to the performance provider profile. Besides providing scalability in all profiles, CC is specifically used to implement an industrialiser strategy, therefore leading to standardised, repeatable and productised offerings.

Information in the knowledge acquisition process

PurposeThe purpose of this paper is to propose an appropriate symbolic representation, as well as its metaphorical interpretation, to illustrate the special role of information in the knowledge acquisition process.Design/methodology/approachBesides the literature review, this is a speculative study based on a symbolic and metaphorical point of view.FindingsThe proposed symbolic representation was derived from the conceptual designation of information “as a flow” and, accordingly, by the corresponding redrawing of the data-information-knowledge-wisdom (DIKW) pyramid. The knowledge acquisition process is symbolically represented by the growth of a “tree of knowledge” which is planted on a “data earth,” filled with “information sap” and lit by the rays of the “sun of the mind,” a new symbol of the concept of wisdom in the DIKW model. As indicated, a key concept of this metaphorical interpretation is the role of “information sap” which rises from the roots of the “tree of knowledge” to the top of the tree and it is recognized as an invisible link between “world of data” and “world of knowledge.” This concept is also proposed as a new symbolic representation of the DIKW model.Originality/valueOn the basis of specific symbolic-metaphorical representation, this paper provides a relatively new concept of information which may help bridge observed gaps in the understanding of information in various scientific fields, as well as in its understanding as an objective or subjective phenomenon.

The Evolution of Data-Information-Knowledge-Wisdom in Nursing Informatics.

The data-information-knowledge-wisdom (DIKW) model has been widely adopted in nursing informatics. In this article, we examine the evolution of DIKW in nursing informatics while incorporating critiques from other disciplines. This includes examination of assumptions of linearity and hierarchy and an exploration of the implicit philosophical grounding of the model. Two guiding questions are considered: (1) Does DIKW serve clinical information systems, nurses, or both? and (2) What level of theory does DIKW occupy? The DIKW model has been valuable in advancing the independent field of nursing informatics. We offer that if the model is to continue to move forward, its role and functions must be explicitly addressed.

A model for the implementation of industry-wide knowledge sharing to improve risk management practice

This article examines the process of industry-wide OHS/safety information management in the Australian coal industry. It uses as a case study the novel RISKGATE interactive database that has been created as part of collaborative efforts between multiple coal mining industry stakeholders over the last five years. The RISKGATE database operates within both the information systems and organisational learning models of knowledge management, capturing inter-organisational expert knowledge and facilitating dissemination to field practitioners through the medium of a digital web-based tool. This discussion will utilise variations of the Data–Information–Knowledge–Wisdom (DIKW) hierarchy as a means of interrogating, firstly, the process of how the various industry stakeholders codify their tacit knowledge on safety issues in the coal mining industry; and secondly, how that data is then made available through the RISKGATE database to practitioners (and others) working in the field. While Frické (2009, 131) thinks the DIKW hierarchy out-dated by reason of its ‘philosophical backdrops of operationalism and inductivism’ amongst other problems, we believe it still has relevance if considered a dynamic entity and not a fixed hierarchy.

Some Comments on Big Data and Data Science

Based on previous paper titled in “Data, DIKW, Big data and Data science” presented in ITQM2014 and published in Procedia Computer Science we will make some modifications. We discuss the relationship between data and data–information–knowledge–wisdom. Now the big data occupies much attention in some extent for his volume, velocity, and variety. But in practical use the value plays more important role. Finally to judge the value for data not necessary for big, in some cases the small data also may lead to big value. So we appreciate the data science, which may consider more inherent value from data. We will make some comments on the big data and data science from another angle.

Towards a wisdom manufacturing vision

Manufacturing enterprises are socio-technical systems, which necessitate overall integration of not only the technical aspects from devices in shop-floor to enterprise resources planning vertically and from product order to shipment horizontally, but also the social aspects such as human interactions and consumers’ intentions. Moreover, there is a growing need in the use of knowledge in enterprise contexts. To meet such needs, wisdom manufacturing (WM) is emerging with advances in the Internet and manufacturing as well as intelligence. In this paper, the most recently developed manufacturing models such as smart manufacturing (SM)/smart factory (SF), cloud manufacturing (CM) and socialised enterprise (SE)/Enterprise 2.0 are analysed, and a WM vision is presented to aggregate SM, CM, SE and existing intelligent manufacturing (IM) that are complementary to each other. Then pathways towards the WM vision are addressed in relationship to knowledge, intelligence, creativity/innovation, learning and wisdom, especially from DIKW (data-information-knowledge-wisdom) and semiotic perspectives as well as from the web evolution. And wisdom and realisation towards the WM vision are investigated. Finally, a case study is used to illustrate the WM vision landscape followed by a conclusion. As a consequence, things, computers and humans, ubiquitous, artificial and collective intelligence, as well as explicit and tacit knowledge, are integrated as a whole in the WM.

Knowledge, Theory and Practice in Knowledge Management: Betweeen Associative Pattering and Context-Rich Action

Embedded throughout this paper you will find the diversity of opinions that correlates to the diversity of theories, frameworks, case studies and stories that are related to the field of Knowledge Management (KM). We begin by introducing the Sampler Research Call approach and the 13 KM academics and practitioners working in different parts of the world who answered the call. We then provide baseline definitions and briefly explore the process of knowledge creation within the human mind/brain. After a brief (and vastly incomplete) introduction to KM literature at the turn of the Century, the frameworks of Sampler Call participants are introduced, and two early frameworks that achieved almost cult status—the Data-Information-Knowledge-Wisdom (DIKW) continuum and the SECI (socialization, externalization, combination and internalization) model—are explored through the eyes of Sampler Call participants. We then introduce the results of the KMTL (Knowledge Management Thought Leader) Study, which suggest theories consistent with the richness and diversity of thought interwoven throughout this paper. The field of KM is introduced as a complex adaptive system with many possibilities and opportunities. Finally, we share summary thoughts, urging us as KM academics and practitioners to find the balance between the conscious awareness/understanding of higher-order patterns and the actions we take; between the need for overarching theory and the experiential freedom necessary to address context-rich situations.

Data, Information, Knowledge, Wisdom (DIKW): A Semiotic Theoretical and Empirical Exploration of the Hierarchy and its Quality Dimension

What exactly is the difference between data and information? What is the difference between data quality and information quality; is there any difference between the two? And, what are knowledge and wisdom? Are there such things as knowledge quality and wisdom quality? As these primitives are the most basic axioms of information systems research, it is somewhat surprising that consensus on exact definitions seems to be lacking. This paper presents a theoretical and empirical exploration of the sometimes directly quoted, and often implied Data, Information, Knowledge, Wisdom (DIKW) hierarchy and its quality dimension. We first review relevant literature from a range of perspectives and develop and contextualise a theoretical DIKW framework through semiotics. The literature review identifies definitional commonalities and divergences from a scholarly perspective; the theoretical discussion contextualises the terms and their relationships within a semiotic framework and proposes relevant definitions grounded in that framework. Next, rooted in Wittgenstein’s ordinary language philosophy, we analyse 20 online news articles for their uses of the terms and present the results of an online focus group discussion comprising 16 information systems experts. The empirical exploration identifies a range of definitional ambiguities from a practical perspective.

Transfer of data or re-creation of knowledge – Experiences of a shared electronic patient medical records system

A shared electronic medical record (EMR) can improve communication between primary and secondary care. A consideration of the contents using Data-Information-Knowledge-Wisdom (DIKW) hierarchy could help inform further development of such systems regarding communication about prescribed medication. To investigate primary and secondary care doctors' experiences of the shared EMR in Uppsala, Sweden, focusing on the creation, use and cross-sector transfer of data, information, knowledge and wisdom about individual patients' prescribed medication. Nine focus groups were held with hospital doctors, of different grades and medical specialties, working at a single large teaching hospital in Uppsala, Sweden and primary care doctors worked in the same geographical area, in urban and rural primary care centers. The transcribed data were analyzed used the constant comparative method, based on data from the participants and application of the DIKW hierarchy. The doctors were very positive about accessing and using the shared EMR. Data and information in the system were efficiently retrieved and combined with newly collected data and information to create further knowledge. However, they also described a data and information overload, where it was difficult to get a general overview of what had happened over time, coupled with the frequent lack of knowledge being created and shared by other healthcare providers. Doctors were, instead, either explicitly asked or implicitly expected to read and interpretall available data and information and recreate knowledge themselves. This study highlighted the differences between access to data and information and access to knowledge in a shared EMR. In rolling out such a system, an increased availability of data and information should not be at the expense of a reduced availability of knowledge.

Wiedza i mądrość

In the specialist literature, especially related to IT, it is easy to find a review about the relationship between the concepts: data–information–knowledge–wisdom. In this article the author confronted the last two terms of the chain knowledge and wisdom. The main question was whether these terms mean essentially the same human property and whether one of them can replace the other? Presented in the article, the characteristics of both concepts (albeit very limited) allows to formulate an answer: both knowledge and wisdom they are two different dimensions of human personality, and both are necessary to him. They have different roles to meet. Wisdom is a kind of compass indicating the purpose and course of action. The knowledge is essential to evaluate the potential to achieve this purpose and the selection of ways to achieve it. Reducing person to only one of them deform the human figure and reduce it to the role of an intelligent robot, much knowing but uncritically.

The Data/Information/Knowledge/Wisdom Hierarchy Goes to Seminary

In Information Science studies, the Data/Information/Knowledge/Wisdom (DIKW) hierarchy is a conventional construct for making sense of the terms. However, when examined with any rigor, the distinctions become challenged and the hierarchy appears to fail. I suggest that the reason for this is the tacit classification of this hierarchy as a disciplinary ontological narrative. With context-appropriate definitions and delimitations, the DIKW hierarchy can still be useful as a model for specific applications in information literacy pedagogy. This is illustrated in the context of theological education by using the construct to differentiate the identification of primary sources in the Seminary disciplines.

A conceptual framework for linking risk and the elements of the data–information–knowledge–wisdom (DIKW) hierarchy

There are numerous definitions of the risk concept. When studying the suitability of these definitions one key issue is the degree that the risk concept is able to reflect the data (D), information (I), knowledge (K) and wisdom (W) available, i.e. the various elements of the well-known DIKW hierarchy. In this paper we present and discuss a structure (conceptual framework) for linking some common risk perspectives and the DIKW elements. The structure is based on the following main ideas: Data=the input to the risk assessment, information=the risk description, knowledge (for the decision maker)=understanding the risk description, knowledge (for analysts)=understanding how to do the risk assessment and understanding the risk description, wisdom (for the decision maker)=the ability to use the results of the analysis in the right way and wisdom (for analysts)=the ability to present the results of the analysis in the right way. The principal aim of this paper is to contribute to a better understanding of the link between the risk concept and the DIKW elements, in order to strengthen the foundations of the meaning and characterisation of risk, and in this way provide a basis for improved risk management.

The Helix of Human Cognition: Knowledge Management According to DIKW, E2E, and the Proposed View

Motto: Attributing cognitive uncertainty and subjectivity of communicating entities to the communicated information is as delusional as attributing movements of the planet Earth to the Sun. Section 1: Introduction In some respects, our "age of information" resembles the pre-Copernicus era in astronomy. Cognitive problems of uncertainty and subjectivity that are experienced by communicating entities (informing entities and entities informed) are attributed to information they exchange. When information (a message or a communication) is viewed as anything "in form"--a pattern of physical states that plays a role only with regard to its form as a factor for operations (e.g., identifier, time, location in drone attacks)--it reminds us of blaming the messenger for the message, just as much of a delusion as attributing movements of our planet Earth to the Sun. Disparate views abound in scholarly literature. Frequently, a wide chasm separates views among academicians and the views of practitioners. This physical and pragmatic approach offers a platform and framework for verifiable reasoning of improved transparency and cohesion. Frank Blackler (1995), in his seminal study, outlined an alternative approach to knowledge. He considers the term, knowing, to be more appropriate, because he views knowledge as a complex process that is mediated, situated, provisional, pragmatic, and contested. Martin Fricke (2009) reviewed the Data-Information-Knowledge-Wisdom (DIKW) Hierarchy as "part of the canon of information science and management." He identified a central logical error in DIKW, offered arguments that the hierarchy is unsound and methodologically undesirable, and offered some alternatives. About the same time, Jean-Baptiste P. L. Faucher, Andre M. Everett, and Rob Lawson (2008) embarked independently upon reconstituting knowledge management. These researchers move away from the hierarchical relationships among data, information, knowledge, and wisdom and offer a new model from Existence to Enlightenment (E2E) as a "cognitive system of knowledge" that redefines the scope of knowledge management. They criticize the current approaches, saying that they lack clear boundaries of knowledge, so they add Existence at the lower end and Enlightenment at the higher end. The latter two efforts partially complement each other, but both ignore most of Blackler's postulates regarding knowledge and the need for quality assurance and feedback in knowledge management, which is surprising in light of the nearly two-decade-long MIT Information Quality (MITIQ) Program (http://mitiq.mit.edu/MITIQ/MITIQ.aspx) that studies and promotes information quality. This paper proposes a further departure from the current way of thinking. On the one hand, one may accept some elements of their critiques: (a) Fricke's (2009) critique of the DIKW Hierarchy, and (b) the implicit critique by Faucher et al. (2008) of the hierarchy and the extension of the model by Existence (see Section 2). On the other hand, a physical and pragmatic approach to information is proposed, where data are purely human constructs and are subsets of information. Knowledge management must deal with a broad scope of information, data, and other elements of knowledge from the rigorous to the esoteric. This is a theoretical paper that presents the author's position in a positivist down-to-earth manner that views information and informing as natural physical phenomena that are interpreted pragmatically; it makes no references to ontology, about the nature of being, and to epistemology, which studies the nature of knowledge. Specifically, this approach: 1. views increments of understanding through the pragmatic lens of decision making to act 2. emphasizes the use of natural semantics of material pragmatic consequences 3. recognizes a gradual linear additive of the quantitative extension of understanding until a critical point is reached that triggers qualitative changes 4. …

An Integrative Framework for Continuous Knowledge Discovery

With the growth in the amount of data generated and collected, the need for automatically analyzing the data has received more and more attention from the business community in recent times. It requires some theoretical considerations, which are well understood and popularized throughout the community. The traditional dominant KDD process model offers valuable guidance on the projectedmanagement oriented stages, with no formal foundation to provide guidance in the crucial issue of business understanding, model selection and known knowledge integration. An integrative framework for continuous knowledge discovery process is proposed in evolving environment, and a DIKW (datainformation-knowledge-wisdom) conceptual hierarchy to characterize the process operation features and interrelations is discussed. As the theoretical basis, the main notions used in the process model are represented formally, in linear temporal logic.

The knowledge pyramid: a critique of the DIKW hierarchy

The paper evaluates the data—information—knowledge—wisdom (DIKW) hierarchy. This hierarchy, also known as the `knowledge hierarchy', is part of the canon of information science and management. Arguments are offered that the hierarchy is unsound and methodologically undesirable. The paper identifies a central logical error that DIKW makes. The paper also identifies the dated and unsatisfactory philosophical positions of operationalism and inductivism as the philosophical backdrop to the hierarchy. The paper concludes with a sketch of some positive theories, of value to information science, on the nature of the components of the hierarchy: that data is anything recordable in a semantically and pragmatically sound way, that information is what is known in other literature as `weak knowledge', that knowledge also is `weak knowledge' and that wisdom is the possession and use, if required, of wide practical knowledge, by an agent who appreciates the fallible nature of that knowledge.