Sensing gestures for business intelligence

The combination of sensor data with analytic techniques is growing in popularity for both practitioners and researchers as an Internet of Things (IoT) offers new opportunities and insights. Organisations are trying to use sensor technologies to derive intelligence and gain a competitive edge in their industries. Obtaining data from sensors might not pose too much of a problem, however subsequent utilisation in meeting an organisation’s decision making can be more problematic. Understanding how sensor data analytics can be undertaken is the first step to deriving business intelligence from front line retail environments. This paper explores the use of the Microsoft Kinect sensor to provide intelligence by identifying and sensing gestures to better understand customer behaviour in the retail space

Data quality management in a business intelligence environment : from the lens of metadata

Business Intelligence is becoming more pervasive in many large and medium-sized organisations. Being a long term undertaking Business Intelligence raises many issues that an organisation has to deal with in order to improve its decision making processes. Data quality is one of the main issues exposed by Business Intelligence. Within the organisation data quality can affect attitudes to Business Intelligence itself, especially from the business users group. Comprehensive management of data quality is a crucial part of any Business Intelligence endeavour. It is important to address all types of data quality issues and come up with an all-in-one solution. We believe that extensive metadata infrastructure is the primary technical solution for management of data quality in Business Intelligence. Moreover, metadata has a more broad application for improving the Business Intelligence environment. Upon identifying the sources of data quality issues in Business Intelligence we propose a concept of data quality management by means of metadata framework and discuss the recommended solution.<br /

Absorptive Capacity and its Potential Role in Supporting Organisational knowledge Creation: A Qualitative Approach

Absorptive Capacity (ACAP) is the ability of a firm to integrate, transform, and apply valuable knowledge required for business success. ACAP is proposed to play a significant role in enriching the process of knowledge creation embraced inside contemporary organizational Information Systems (IS) environments. Many misperceptions surround how ACAP can be measured and understood as an organizational construct. The aim of this research is to decrease such misperception by providing qualitative measures for ACAP dimensions extracted using data from (22) semi-structured interviews conducted with senior managers working in two telecommunication companies, and analysed following Grounded Theory Methodology (GTM) coding techniques. Drawing on our analysis, we propose a relational model that includes measures that can be commonly used in the literature, and treated as guides to IS researchers and senior managers in exploring the rich facets of ACAP. The extracted measures are proposed to offer foundations for shaping where and how further potential organizational assets can be leveraged

THE APPLICATION OF DECISION SUPPORT IN THE PROCESS OF ACHIEVING MANAGER EFFICIENCY

İşletmelerin sürdürülebilirliğinin temelinde, müşteri memnuniyetini arttırmak,kaynaklarını etkin ve rasyonel kullanmak ve iş hacimlerini arttırmak yatmaktadır.Müşterilerin memnuniyetinin sağlanmasının yanında iş yapış şekillerinde verimliliğinarttırılması, iş süreçlerinin etkin yönetilmesine bağlıdır. Karar vericilerin iş sonuçlarınabakarak süreçlerde yapacağı değişikliklerin planlanmasında müşteriyi etkileyebilmesibüyük önem taşımaktadır. Bu çalışmada, müşteri ilişkileri yönetimi sürecinde kararvericilerin, memnuniyet sonuçlarına erişim ve sonuçları değerlendirme sürecindebilgisayar teknolojileri kullanılarak bir "Karar Destek Uygulaması" gerçekleştirmekamaçlanmaktadır.Sustainability for companies depends on increase customer satisfaction, effectiveand rational usage of resources and increases the volume of business. Both providingcustomer satisfaction and increase the productivity of business processes, effective processmanagement is required. The effect of results that decision-makers change processes due tothe working results, on customers is very important. In this study, a decision supportapplication is realized with using computer technologies on customer satisfaction resultsaccess and evaluation tasks in customer relations management process

Evaluation of business intelligence system usability

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe reality in businesses nowadays is the easy access to big amount of data that is being transferred into information. Reliable and useful information provide a competitive advantage in turbulent waters on all fields of business activities. Information is the foundation for all important business decisions. That means that on the informational foundation entire business structure is based. Information is needed at each stage of business operations, from enterprise’s entrance into the market, its growth, and throughout its every day strategic responses to the market’s demands. Due to the almost limitless processing power and storage capabilities, it is relatively easy to provide sufficient amount of information. Information is in many organizational structures often so accessible, that employees are confronted with saturation and overflow of it, on a daily basis. For that reason we should be aware, that it is extremely difficult to capture, access and process the right information at the right time. This can quickly become impossible, if we are about to prepare the information from billions of terabytes of data (The solution for limitless processing power, storage and RAM, 2011). For several years now, Business Intelligence (hereinafter: BI) products are, with their increased functionality, trying to help the day-to-day users and “super users” in organizations, to make the best decisions. These knowledge workers, as IT staff, power users, executives, functional managers and last but not least the occasional Information customers, such as business partners and data consumers, are for sure gaining all the needed information

The influence of critical success factors on business intelligence net benefits

Background: Business intelligence (BI) is regarded as a key practice to invest into and adopt. This is due to the benefits that can be realized from BI. Critical success factors (CSFs) need to be managed appropriately for organizations to realize maximum benefit from their BI investments. Objective: The objective of this study is to measure the influence of BI CSFs on BI net benefits. In addition, the interrelationships between these CSFs will be measured, the effect of moderating variables will be determined and the reasons why these CSFs are important will be explored. Method: A deductive approach was followed. A conceptual model was derived from literature. This model was used to construct an online survey. The data gathered from the survey was analysed using statistical techniques. The results from the statistical analysis were validated and expanded on by conducting semi-structured interviews with participants who completed the online survey. Results: The results found that top management support, alignment between BI and business objectives and BI technology fit for the business were determined to be the most influential BI CSFs to realize BI net benefits. Top management support was shown to have a relationship with all other CSFs. Well-defined user requirements and user participation did not have a relationship with BI net benefits. Industry and size moderated a small portion of the relationships between BI CSFs and BI net benefits. Conclusion: Organizations need to prioritize top management support, alignment between BI and business objectives, BI technology fit for the business, incremental project management methodology and adequate team skills, to realize BI net benefits. Special attention should be given to top management support as it influences all other BI CSFs

Effectiveness of Real-time Business Intelligence on Enterprise Performance Management: a Systematic Literature Review

The purpose of the systematic literature review is to better understand how a real-time business intelligence can enhance an enterprise performance management (EPM) solution. The various processes and methodologies of EPM along with its integration with real-time business intelligence is studied in this paper. Many studies focus on the role of real-time analytics in organizations, but there are very few that focus on the linkage between real-time business intelligence and enterprise performance management. This Master of Science thesis aims to address that gap. The review is conducted by comparing and synthesizing research studies done in this area. The findings prove that real-time business intelligence is beneficial enough to implement to monitor enterprise performance since faster and better decision making on business processes is enabled. With the emergence of artificial intelligence and machine learning algorithms, there is a bright scope for automated decision making and performing actions. This could help reduce the third and impeding latency type in business intelligence which is decision/action latency. The literature review also suggests that there is more research needed linking real-time business intelligence and enterprise performance management. In conclusion, this review’s findings have shown that real-time business intelligence when integrated with enterprise performance management solutions can help the business gain competitive advantage after careful consideration of the purpose and effects of implementation

Developing unbiased artificial intelligence in recruitment and selection : a processual framework : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand

For several generations, scientists have attempted to build enhanced intelligence into computer systems. Recently, progress in developing and implementing Artificial Intelligence (AI) has quickened. AI is now attracting the attention of business and government leaders as a potential way to optimise decisions and performance across all management levels from operational to strategic. One of the business areas where AI is being used widely is the Recruitment and Selection (R&S) process. However, in spite of this tremendous growth in interest in AI, there is a serious lack of understanding of the potential impact of AI on human life, society and culture. One of the most significant issues is the danger of biases being built into the gathering and analysis of data and subsequent decision-making. Cognitive biases occur in algorithmic models by reflecting the implicit values of the humans involved in defining, coding, collecting, selecting or using data to train the algorithm. The biases can then be self-reinforcing using machine learning, causing AI to engage in ‘biased’ decisions. In order to use AI systems to guide managers in making effective decisions, unbiased AI is required. This study adopted an exploratory and qualitative research design to explore potential biases in the R&S process and how cognitive biases can be mitigated in the development of AI-Recruitment Systems (AIRS). The classic grounded theory was used to guide the study design, data gathering and analysis. Thirty-nine HR managers and AI developers globally were interviewed. The findings empirically represent the development process of AIRS, as well as technical and non-technical techniques in each stage of the process to mitigate cognitive biases. The study contributes to the theory of information system design by explaining the phase of retraining that correlates with continuous mutability in developing AI. AI is developed through retraining the machine learning models as part of the development process, which shows the mutability of the system. The learning process over many training cycles improves the algorithms’ accuracy. This study also extends the knowledge sharing concepts by highlighting the importance of HR managers’ and AI developers’ cross-functional knowledge sharing to mitigate cognitive biases in developing AIRS. Knowledge sharing in developing AIRS can occur in understanding the essential criteria for each job position, preparing datasets for training ML models, testing ML models, and giving feedback, retraining, and improving ML models. Finally, this study contributes to our understanding of the concept of AI transparency by identifying two known cognitive biases similar-to-me bias and stereotype bias in the R&S process that assist in assessing the ML model outcome. In addition, the AIRS process model provides a good understanding of data collection, data preparation and training and retraining the ML model and indicates the role of HR managers and AI developers to mitigate biases and their accountability for AIRS decisions. The development process of unbiased AIRS offers significant implications for the human resource field as well as other fields/industries where AI is used today, such as the education system and insurance services, to mitigate cognitive biases in the development process of AI. In addition, this study provides information about the limitations of AI systems and educates human decision makers (i.e. HR managers) to avoid building biases into their systems in the first place