Knowledge management in support of enterprise risk management

Risk management and knowledge management have so far been studied almost independently. The evolution of risk management to the holistic view of Enterprise Risk Management requires the destruction of barriers between organizational silos and the exchange and application of knowledge from different risk management areas. However, knowledge management has received little or no attention in risk management. This paper examines possible relationships between knowledge management constructs related to knowledge sharing, and two risk management concepts: perceived quality of risk control and perceived value of enterprise risk management. From a literature review, relationships with eight knowledge management variables covering people, process and technology aspects were hypothesised. A survey was administered to risk management employees in financial institutions. The results showed that the perceived quality of risk control is significantly associated with four knowledge management variables: perceived quality of risk knowledge sharing, perceived quality of communication among people, web channel functionality, and risk management information system functionality. However, the relationships of the knowledge management variables to the perceived value of enterprise risk management are not significant. We conclude that better knowledge management is associated with better risk control, but that more effort needs to be made to break down organizational silos in order to support true Enterprise Risk Management.

Using knowledge management to give context to analytics and big data and reduce strategic risk

At the moment, the phrases “big data” and “analytics” are often being used as if they were magic incantations that will solve all an organization’s problems at a stroke. The reality is that data on its own, even with the application of analytics, will not solve any problems. The resources that analytics and big data can consume represent a significant strategic risk if applied ineffectively. Any analysis of data needs to be guided, and to lead to action. So while analytics may lead to knowledge and intelligence (in the military sense of that term), it also needs the input of knowledge and intelligence (in the human sense of that term). And somebody then has to do something new or different as a result of the new insights, or it won’t have been done to any purpose. Using an analytics example concerning accounts payable in the public sector in Canada, this paper reviews thinking from the domains of analytics, risk management and knowledge management, to show some of the pitfalls, and to present a holistic picture of how knowledge management might help tackle the challenges of big data and analytics.

Techniques for automated taxonomy building: towards ontologies for knowledge management

Ontologies have become widely accepted as the main method for representing knowledge in Knowledge Management (KM) applica-tions. Given the continuous and rapid change and dynamic nature of knowledge in all fields, automated methods for construct-ing ontologies are of great importance. All ontologies or taxonomies currently in use have been hand built and require consider-able manpower to keep up to date. Taxono-mies are less logically rigorous than ontolo-gies, and in this paper we consider the re-quirements for a system which automatically constructed taxonomies. There are a number of potentially useful methods for construct-ing hierarchically organised concepts from a collection of texts and there are a number of automatic methods which permit one to as-sociate one word with another. The impor-tant issue for the successful development of this research area is to identify techniques for labelling the relation between two candi-date terms, if one exists. We consider a number of possible approaches and argue that the majority are unsuitable for our re-quirements.

User-centred ontology learning for knowledge management

Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained leaner to be used for further ontology modifications.

Knowledge acquisition for knowledge management: position paper

With this paper, we propose a set of techniques to largely automate the process of KA, by using technologies based on Information Extraction (IE) , Information Retrieval and Natural Language Processing. We aim to reduce all the impeding factors mention above and thereby contribute to the wider utility of the knowledge management tools. In particular we intend to reduce the introspection of knowledge engineers or the extended elicitations of knowledge from experts by extensive textual analysis using a variety of methods and tools, as texts are largely available and in them - we believe - lies most of an organization's memory.

Knowledge management in the energy sector:review and future directions

Purpose – The purpose of this paper is to examine the state of knowledge management (KM) in the energy sector and more broadly, and consider future directions for research and practice. Design/methodology/approach – The paper reviews the literature on KM and the practice of KM as relevant to the energy sector. Findings – There are many examples of good practice in KM in the sector, and some organisations, especially in the oil industry, are seen as leaders in KM practice. However, other organisations have yet to embark on explicit KM initiatives or projects at all. In addition, some parts of the energy sector discuss KM without any reference to the more general KM literature. Originality/value – Although some parts of the energy sector have justifiably earned a good reputation for KM, other parts are completely unaware of the field, as is apparent from the literature. This review helps to raise awareness and guide future work.

Group perceptions of knowledge management

This paper reports preliminary results of a project investigating how staff in UK organisations perceive knowledge management in their organisation as a group. The group setting appears to be effective in surfacing opinions and enabling progress in both understanding and action to be made. Among the findings thus far are the importance of the knowledge champion role and the state of the “knowledge management life cycle” in each organisation, and continuing confusion between knowledge, information and mechanisms.

Using a simulation model for knowledge elicitation and knowledge management

The work reported in this paper is part of a project simulating maintenance operations in an automotive engine production facility. The decisions made by the people in charge of these operations form a crucial element of this simulation. Eliciting this knowledge is problematic. One approach is to use the simulation model as part of the knowledge elicitation process. This paper reports on the experience so far with using a simulation model to support knowledge management in this way. Issues are discussed regarding the data available, the use of the model, and the elicitation process itself. © 2004 Elsevier B.V. All rights reserved.

Knowledge management and the limits of knowledge codification

Purpose - The idea that knowledge needs to be codified is central to many claims that knowledge can be managed. However, there appear to be no empirical studies in the knowledge management context that examine the process of knowledge codification. This paper therefore seeks to explore codification as a knowledge management process. Design/methodology/approach - The paper draws on findings from research conducted around a knowledge management project in a section of the UK Post Office, using a methodology of participant-observation. Data were collected through observations of project meetings, correspondence between project participants, and individual interviews. Findings - The principal findings about the nature of knowledge codification are first, that the process of knowledge codification also involves the process of defining the codes needed to codify knowledge, and second, that people who participate in the construction of these codes are able to interpret and use the codes more similarly. From this it can be seen that the ability of people to decodify codes similarly places restrictions on the transferability of knowledge between them. Research limitations/implications - The paper therefore argues that a new conceptual approach is needed for the role of knowledge codification in knowledge management that emphasizes the importance of knowledge decodification. Such an approach would start with one's ability to decodify rather than codify knowledge as a prerequisite for knowledge management. Originality/value - The paper provides a conceptual basis for explaining limitations to the management and transferability of knowledge. © Emerald Group Publishing Limited.

Proposing a systems vision of knowledge management in emergency care

This paper makes a case for taking a systems view of knowledge management within health-care provision, concentrating on the emergency care process in the UK National Health Service. It draws upon research in two casestudy organizations (a hospital and an ambulance service). The case-study organizations appear to be approaching knowledge (and information) management in a somewhat fragmented way. They are trying to think more holistically, but (perhaps) because of the ways their organizations and their work are structured, they cannot ‘see’ the whole of the care process. The paper explores the complexity of knowledge management in emergency health care and draws the distinction for knowledge management between managing local and operational knowledge, and global and clinical knowledge.

Comparing knowledge management between organisations:searching for good practice

This paper reports ongoing work that is attempting to find out ‘what is good practice for knowledge management’. The data we have to analyse this issue is 109 maps of knowledge (on knowledge management) which were built during 18 group workshops with 152 people from 15 different organisations. The maps contain data on the aspirations and action plans which UK managers have to improve knowledge management practices in their organisation. So far we have attempted a number of approaches to analysing this data, both inductive and deductive, but we still feel there is more to be learned from the rich data set we have. The paper presents a flavour of the work we have done, have considered doing, and have resisted doing. The aim of the paper is to stimulate debate on the strengths of our analyses and, more importantly, on amassing views of how it can be further strengthened, and the difficulties and dilemmas which might need to be overcome.

Knowledge management: a review of the field and of OR's contribution

This paper examines the field of knowledge management (KM) and identifies the role of operational research (OR) in key milestones and in KM's future. With the presence of the OR Society journal Knowledge Management Research and Practice and with the INFORMS journal Organization Science, OR may be assumed to have an explicit and a leading role in KM. Unfortunately, the origins and the evidence of recent research efforts do not fully support this assumption. We argue that while OR has been inside many of the milestones there is no explicit recognition of its role and while OR research on KM has considerably increased in the last 5 years, it still forms a rather modest explicit contribution to KM research. Nevertheless, the depth of OR's experience in decision-making models and decision support systems, soft systems with hard systems and in risk management suggests that OR is uniquely placed to lead future KM developments. We suggest that a limiting aspect of whether OR will be seen to have a significant profile will be the extent to which developments are recognized as being informed by OR.

Achieving closure through knowledge management strategy

Case studies of knowledge management practices are often conducted in organizations where the aim is to manage knowledge for future operational improvements. What about knowledge management for organizations with limited life-spans that are preparing for closure? Such organizations are not common but can benefit from knowledge management strategy. This case study concerns the knowledge management strategy of an organization that is preparing for its final phase of operations. We facilitated two group workshops with senior managers to scope a strategy, following which the organization initiated a set of projects to implement the resulting actions. This paper reviews their implemented actions against those designed in the workshop to shed light on knowledge management in this uncommon situation.

Intelligence agents (knowledge management)

Reports some insights into knowledge management (KM) derived from UK one-day workshops with six businesses, three non-profits and one public sector organization. Lists the four questions posed to participants and discusses the themes which emerged, e.g. the need for a KM strategy to make raw information more useable, KM performance measurement etc. Stresses the need for commitment from a top-level champion and a wide range of employees to make this work and identifies three types of solutions for improving KM strategy: technological (e.g. databases and intranets), people (e.g. motivation, retention, training and networking) and processes (e.g. procedural instructions and balancing formal/informal knowledge sharing methods). Finds that accountants and senior managers do not generally see KM as very important but argues that management accountants are suitable knowledge champions who could develop explicit links between KM and organizational performance.