Investigating the use of business, competitive and marketing intelligence as management tools in the mining industry

The main objective of this research study is to investigate the extent to which business intelligence, competitive intelligence and marketing intelligence are used within the mining industry. Business intelligence, competitive intelligence and marketing intelligence are the management tools used to mine information to produce up-to-date intelligence and knowledge for operative and strategic decision making. A structured questionnaire is used for the study. A total of 300 mines are randomly selected from a research population of mining organizations in South Africa, Africa and globally. The respondents are all part of senior management. A response rate of 64% is achieved. The results indicat that more than half of the respondents do not have real-time intelligence and proper data mining tools to identify patterns and relationships within a data warehouse. Although a large proportion agrees that their organizations have systematic ways of gathering these different types of intelligence and use them for strategic decision making, there is a significant proportion that did not have any systems. Statistically and practically significant positive relationships with a large effect are found among the dimensions of business intelligence, marketing intelligence, competitive intelligence and perceived business performanc

ENHANCEMENT OF CHURN PREDICTION ALGORITHMS

Customer churn can be described as the process by which consumers of goods and services discontinue the consumption of a product or service and switch over to a competitor.It is of great concern to many companies. Thus, decision support systems are needed to overcome this pressing issue and ensure good return on investments for organizations. Decision support systems use analytical models to provide the needed intelligence to analyze an integrated customer record database to predict customers that will churn and offer recommendations that will prevent them from churning. Customers churn prediction, unlike most conventional business intelligence techniques, deals with customer demographics, net worth-value, and market opportunities. It is used in determining customers who are likely to churn, those likely to remain loyal to the organization, and for prediction of future churn rates. Customer defection is naturally a slow rate event, and it is not easily detected by most business intelligent solutions available in the market; especially when data is skewed, large, and distinct. Thus, accurate and precise prediction methods are needed to detect the churning trend. In this study, a churn model that applies business intelligence techniques to detect the possibility that a customer will churn using churn trend analysis of customer records is proposed. The model applies clustering algorithms and enhanced SPRINT decision tree algorithms to explore customer record database, and identify the customer profile and behavior patterns. The Model then predicts the possibility that a customer will churn. Additionally, it offers solutions for retaining customers and making them loyal to a business entity by recommending customer-relationship management measures

A requirement engineering model for big data software

Most prevailing software engineering methodologies assume that software systems are developed from scratch to capture business data and subsequently generate reports. Nowadays, massive data may exist even before software systems are developed. These data may also be freely available on Internet or may present in silos in organizations. The advancement in artificial intelligence and computing power has also prompted the need for big data analytics to unleash more business values to support evidence-based decisions. Some business values are less evident than others, especially when data are analyzed in silos. These values could be potentially unleashed and augmented from the insights discovered by data scientists through data mining process. Data mining may involve overlaying and merging data from different sources to extract data patterns. Ideally, these values should be eventually incorporated into the information systems to be. To realize this, we propose that software engineers ought to elicit software requirements together with data scientists. However, in the traditional software engineering process, such collaboration and business values are usually neglected. In this paper, we present a new requirement engineering model that allows software engineers and data scientists to discover these values hand in hand as part of software requirement process. We also demonstrate how the proposed requirement model captures and expresses business values that unleashed through big data analytics using an adapted use case diagram

AI management an exploratory survey of the influence of GDPR and FAT principles

As organisations increasingly adopt AI technologies, a number of ethical issues arise. Much research focuses on algorithmic bias, but there are other important concerns arising from the new uses of data and the introduction of technologies which may impact individuals. This paper examines the interplay between AI, Data Protection and FAT (Fairness, Accountability and Transparency) principles. We review the potential impact of the GDPR and consider the importance of the management of AI adoption. A survey of data protection experts is presented, the initial analysis of which provides some early insights into the praxis of AI in operational contexts. The findings indicate that organisations are not fully compliant with the GDPR, and that there is limited understanding of the relevance of FAT principles as AI is introduced. Those organisations which demonstrate greater GDPR compliance are likely to take a more cautious, risk-based approach to the introduction of AI

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Organizing Knowledge in Implementation of Knowledge Management as Strategy for Competitive Bussiness at PT Telkom

This study is entitled Organizing Knowledge in Implementation of Knowledge Management. The research was conducted in bussines organization. The research objectives are to find out new concept in coverage of knowledge by knowledge management implementation at Telkom organizing explicit knowledge ; to analysis personal characteristic knowledge manager. This research use by qualitative methode with case study approach at Telkom Japati 1st street Bandung. Technique of gathering data uses observation, archived record, interview, documentation dan physical ware. From the results of studies that have been done, so the conclusion can be drawn as follows: Knowledge management which is done by making taxonomy based processes and business operations is called as knowledge centers that are stored on the intranet while competency-based stream called virtual competency center. Organizing knowledge in virtual storage by creating taxonomy of knowledge toward process and operating bussines, tree types of knowledge are:Structure knowledge: unstructure knowledge and less structure knowledge. For other media are managed by a special unit that is the library. The technology media support information and communications intended to improve information transfer and sharing of knowledge organization as a whole through cooperation and communication between individuals. Recomendation: It is better to make guidelines of writing articles on KM Tool, in order to avoid a flood of information that is not need. For example the text have been made by others. .It is better also to make the theme of writing, so that the contributors will more focus in creating the knowledge. Therefore, it will give deep exploration a theme. Form of virtual communication in KM should also explore the tacit knowledge. It is appropriate if the contributors are also allowed to create works that are audio-visual format. For example how to use technology in the 3.5 G DAT file format, or how to assemble the satellite Telkom2. Keyword: Business communication; Knowledge management; Organizational Communication; Organizing knowledge; Knowledge strorag

Invited article: Adaptability

For the last several decades, organizations have dealt with economic shifts using change management. Based on the new science, there are two major flaws with this approach. First, the word change implies an event with an ending. Second, it implies that change can be managed. In a world of economic volatility, this approach is no longer viable. The continuous climate of uncertainty and volatility demands another view, one that supports adaptability and resilience.Organization, alignment, speed of change, economic volatility, market shift, Total Quality Management, Business Reprocess Engineering, living systems, chaos, evolution, fifth discipline, learning organization, system thinking.