Richard Self

Combining Digital Forensic Practices and Database Analysis as an Anti-Money Laundering Strategy for Financial Institutions

Digital forensics is the science that identify, preserve, collect, validate, analyse, interpret, and report digital evidence that may be relevant in court to solve criminal investigations. Conversely, money laundering is a form of crime that is compromising the internal policies in financial institutions, which is investigated by analysing large amount of transactional financial data. However, the majority of financial institutions have adopted ineffective detection procedures and extensive reporting tasks to detect money laundering without incorporating digital forensic practices to handle evidence. Thus, in this article, we propose an anti-money laundering model by combining digital forensics practices along with database tools and database analysis methodologies. As consequence, admissible Suspicious Activity Reports (SARs) can be generated, based on evidence obtained from forensically analysing database financial logs in compliance with Know-Your-Customer policies for money laundering detection.

Governance Strategies for the Cloud, Big Data, and Other Technologies in Education

The Cloud, Big Data and many emerging technologies are now being considered by many educational establishments as candidates for deriving benefits for both students in their learning and also for the organisation in terms of more effective and efficient operation. This paper considers the governance strategies which need to be developed and implemented in order to ensure that the technologies can be safely incorporated into the technical and operational infrastructure. It demonstrates that synthesising ISO 27002 with a new framework of 12 Vs of Big Data provides an effective approach to identifying some important aspects of new technologies that do not naturally arise from traditional frameworks.

Big Data Analytics: A Threat or an Opportunity for Knowledge Management?

Big Data Analytics is a rapidly developing field which already shows early promising successes. There are considerable synergies between this and Knowledge Management: both have the goal of improving decision-making, fostering innovation, fuelling competitive edge and economic success through the acquisition and application of knowledge. Both operate in a world of increasing deluges of information, with no end in sight. Big Data Analytics can be seen as a threat to the practice of knowledge management: it could relegate the latter to the mists of organizational history in the rush to adopt the latest techniques and technologies. Alternatively, it can be approached as an opportunity for knowledge management in that it wrestles with many of the same issues and dilemmas as knowledge management, The key, it is argued, lies in the application of the latter’s more social and discursive construction of knowledge, a growing trend in knowledge management. This conceptual paper explores the synergies, opportunities and contingencies available to both fields. It identifies challenges and opportunities for future research into the application of Big Data to Knowledge Management.

Discourse and Knowledge Matters: Can Knowledge Management Be Saved?

The Knowledge Management (KM) literature is reviewed with a focus on theory, finding a core issue in the lack of a widely accepted and understood definition of knowledge. Theories are categorised on the bisecting continua of personal vs. organizational knowledge, and reified knowledge vs. knowledge as social action. It is argued that a fresh approach based on the Discourse Psychology framework, and its research tool of discourse analysis, would shed new light on the primary issues. Social interaction – and therefore, language – is considered by many KM theorists to be essential to knowledge sharing and creation, yet language has not been the locus of investigation. DP views language as the site of social action, and reality construction. Consequently, a study of talk in interaction is likely to reveal more about the nature of knowledge and in particular its psychological formulation, with implications for its management.

Tools and Technologies for the Implementation of Big Data

Abstract This chapter uses the five V’s of Big Data (volume, velocity, variety, veracity, and value) to form the basis for consideration of the current status and issues relating to the introduction of Big Data analysis into organizations. The first three are critical to understanding the implications and consequences of available choices for the techniques, tools, and order to provide an understanding of choices that need to be made based on understanding the nature of the data sources and the content. All five V’s are invoked to evaluate some of the most critical issues involved in the choices made during the early stages of implementing a Big Data analytics project. Big Data analytics is a comparatively new field; as such, it is important to recognize that elements are currently well along the Gartner hype cycle into productive use. The concept of the planning fallacy is used with information technology project success reference class data created by the Standish Group to improve the success rates of Big Data projects. International Organization for Standardization 27002 provides a basis considering critical issues raised by data protection regimes in relation to the sources and locations of data and processing of Big Data.

TechnoStress in the 21st Century; Does It Still Exist and How Does It Affect Knowledge Management and Other Information Systems Initiatives

This paper critically evaluates the impact and consequences of TechnoPhobia and TechnoStress (Rosen and Weil 1995) on public and corporate ICT policy makers. This research is set in the context of government ministries in Malta and performed by Aquilina in 2010/2011, as part of his MSc Dissertation. It is of particular significance in indicating how little has changed in the perceptions of ICT users surveyed as to their levels of acceptance and trust or distrust in ICT in their work and recreation, using the CARS and GATS scales of Rosen and Weil. It intimates that the problem may have got worse. The fact that 56% of the respondents in Malta showed some degree of technophobia should be of particular concern to policy makers. The consequences of Technophobia need to be incorporated into the policy making forum to ensure more effective ICT systems are developed.