Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 16

Business Intelligence Related Abstracts

16 Sustainability Model for Rural Telecenter Using Business Intelligence Technique

Authors: Razak Rahmat, Azizah Ahmad, Rafidah Razak, Roshidi Din, Azizi Abas

Abstract:

Telecenter is a place where communities can access computers, the Internet, and other digital technologies to enable them to gather information, create, learn, and communicate with others. However, previous studies found that sustainability issues related to economic, political and institutional, social and technology is one of the major problem faced by the telecenter. Based on that problem, this research is planning to design a possible solution on rural telecenters sustainability with the support of business intelligence (BI). The empirical study will be conducted through the qualitative and quantitative method including interviews and observations with a range of stakeholders including ministry officers, telecenters managers and operators. Result from the data collection will be analyze using the causal modeling approach of SEM SmartPLS for the validity. The expected finding from this research is the Business Intelligent Requirement Model as a guild for sustainability of the rural telecenters.

Keywords: Business Intelligence, Sustainability, Rural ICT Telecenter(RICTT), requirement analysis modal

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15 Endeavor in Management Process by Executive Dashboards: The Case of the Financial Directorship in Brazilian Navy

Authors: R. S. Quintal, J. L. Tesch Santos, M. D. Davis, E. C. de Santana, M. de F. Bandeira dos Santos

Abstract:

The objective is to identify the contributions from the introduction of the computerized system deal within the Accounting Department of Brazilian Navy Financial Directorship and its possible effects on the budgetary and financial harvest of Brazilian Navy. The relevance lies in the fact that the management process is responsible for the continuous improvement of organizational performance through higher levels of quality in their activities. Improvements in organizational processes have direct effects on crops cost, quality, reliability, flexibility and speed. The method of study of this research is the case study. The choice of case study attended, among other demands, a need for greater flexibility to study processes related to a computerized system. The sources of evidence were used literature, documentary and direct observation. Direct observation was made by monitoring the implementation of the computerized system in the Division of Management Analysis. The main findings of the study point to the fact that the computerized system may contribute significantly to the standardization of information. There was improvement of internal processes in the division of management analysis, made possible the consolidation of a standard management and performance analysis that contribute to global homogeneity in the treatment of information essential to the process of decision making. This study has limitations related to the fact the search result be subject exclusively to the case studied, and it is impossible to generalize to other organs of government.

Keywords: Business Intelligence, Process Management, management control, Brazilian Navy

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14 Data Analytics in Energy Management

Authors: Sanjivrao Katakam, Thanumoorthi I., Antony Gerald, Ratan Kulkarni, Shaju Nair

Abstract:

With increasing energy costs and its impact on the business, sustainability today has evolved from a social expectation to an economic imperative. Therefore, finding methods to reduce cost has become a critical directive for Industry leaders. Effective energy management is the only way to cut costs. However, Energy Management has been a challenge because it requires a change in old habits and legacy systems followed for decades. Today exorbitant levels of energy and operational data is being captured and stored by Industries, but they are unable to convert these structured and unstructured data sets into meaningful business intelligence. It must be noted that for quick decisions, organizations must learn to cope with large volumes of operational data in different formats. Energy analytics not only helps in extracting inferences from these data sets, but also is instrumental in transformation from old approaches of energy management to new. This in turn assists in effective decision making for implementation. It is the requirement of organizations to have an established corporate strategy for reducing operational costs through visibility and optimization of energy usage. Energy analytics play a key role in optimization of operations. The paper describes how today energy data analytics is extensively used in different scenarios like reducing operational costs, predicting energy demands, optimizing network efficiency, asset maintenance, improving customer insights and device data insights. The paper also highlights how analytics helps transform insights obtained from energy data into sustainable solutions. The paper utilizes data from an array of segments such as retail, transportation, and water sectors.

Keywords: Business Intelligence, Optimization, Energy Management, Energy Analytics, operational data

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13 Parallel Genetic Algorithms Clustering for Handling Recruitment Problem

Authors: Ahmad Shahin, Walid MOUDANI

Abstract:

This research presents a study to handle the recruitment services system. It aims to enhance a business intelligence system by embedding data mining in its core engine and to facilitate the link between job searchers and recruiters companies. The purpose of this study is to present an intelligent management system for supporting recruitment services based on data mining methods. It consists to apply segmentation on the extracted job postings offered by the different recruiters. The details of the job postings are associated to a set of relevant features that are extracted from the web and which are based on critical criterion in order to define consistent clusters. Thereafter, we assign the job searchers to the best cluster while providing a ranking according to the job postings of the selected cluster. The performance of the proposed model used is analyzed, based on a real case study, with the clustered job postings dataset and classified job searchers dataset by using some metrics.

Keywords: Business Intelligence, Genetic Algorithms, Clustering, job postings, job searchers

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12 Knowledge Management in the Interactive Portal for Decision Makers on InKOM Example

Authors: K. Marciniak, M. Owoc

Abstract:

Managers as decision-makers present in different sectors should be supported in efficient and more and more sophisticated way. There are huge number of software tools developed for such users starting from simple registering data from business area – typical for operational level of management – up to intelligent techniques with delivering knowledge - for tactical and strategic levels of management. There is a big challenge for software developers to create intelligent management dashboards allowing to support different decisions. In more advanced solutions there is even an option for selection of intelligent techniques useful for managers in particular decision-making phase in order to deliver valid knowledge-base. Such a tool (called Intelligent Dashboard for SME Managers–InKOM) is prepared in the Business Intelligent framework of Teta products. The aim of the paper is to present solutions assumed for InKOM concerning on management of stored knowledge bases offering for business managers. The paper is managed as follows. After short introduction concerning research context the discussed supporting managers via information systems the InKOM platform is presented. In the crucial part of paper a process of knowledge transformation and validation is demonstrated. We will focus on potential and real ways of knowledge-bases acquiring, storing and validation. It allows for formulation conclusions interesting from knowledge engineering point of view.

Keywords: Knowledge Management, Business Intelligence, Decision Support Systems, knowledge transformation, knowledge validation, managerial systems

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11 An Analysis of Business Intelligence Requirements in South African Corporates

Authors: Adheesh Budree, Olaf Jacob, Louis CH Fourie, James Njenga, Gabriel D Hoffman

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Business Intelligence (BI) is implemented by organisations for many reasons and chief among these is improved data support, decision support and savings. The main purpose of this study is to determine BI requirements and availability within South African organisations. The study addresses the following areas as identified as part of a literature review; assessing BI practices in businesses over a range of industries, sectors and managerial functions, determining the functionality of BI (technologies, architecture and methods). It was found that the overall satisfaction with BI in larger organisations is low due to lack of ability to meet user requirements.

Keywords: Business Intelligence, Data Management, South Africa, business value

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10 Gamification of a Business Intelligence Tool

Authors: Stephen Miller

Abstract:

The act of applying game mechanics and dynamics (which have been traditionally used in video games) into business applications is being widely trialed in an effort to make conventional business software a bit more participative, fun and engaging. This new trend, named ‘gamification’ has its believers and of course, its critics who still need convincing that the concept is an effective and beneficial business tool worthy of investment. The literature reveals that user engagement of business intelligence (BI) tools is much lower than expected and investors are failing to get a good return on their investment (ROI). So, a software prototype will be designed and developed to add gamification to a BI tool to determine its effect upon the user engagement levels of test participants. The experimental study will be evaluated using the comprehensive User Engagement Scale (UES) to see if there are improvements in areas such as; aesthetics, perceived usability, endurability, novelty, felt involvement and focused attention. The results of this unique study should demonstrate whether or not ‘gamifying’ a BI tool has the potential to increase an individual’s motivation to use BI software more often.

Keywords: Business Intelligence, Human Computer Interaction, Gamification, user engagement

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9 Customer Data Analysis Model Using Business Intelligence Tools in Telecommunication Companies

Authors: Monica Lia

Abstract:

This article presents a customer data analysis model using business intelligence tools for data modelling, transforming, data visualization and dynamic reports building. Economic organizational customer’s analysis is made based on the information from the transactional systems of the organization. The paper presents how to develop the data model starting for the data that companies have inside their own operational systems. The owned data can be transformed into useful information about customers using business intelligence tool. For a mature market, knowing the information inside the data and making forecast for strategic decision become more important. Business Intelligence tools are used in business organization as support for decision-making.

Keywords: Data Mining, Business Intelligence, Process Modelling, data warehouse, Logical Data Model, customer analysis, decisions, self-service reports, interactive visual analysis, and dynamic dashboards, use cases diagram, data mart, ETL, star schema, OLAP, data universes

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8 Business Intelligence for Profiling of Telecommunication Customer

Authors: Rokhmatul Insani, Hira Laksmiwati Soemitro

Abstract:

Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller.

Keywords: Data Mining, Business Intelligence, data warehouse, customer segmentation

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7 A Decision Support Framework for Introducing Business Intelligence to Midlands Based SMEs

Authors: Amritpal Slaich, Mark Elshaw

Abstract:

This paper explores the development of a decision support framework for the introduction of business intelligence (BI) through operational research techniques for application by SMEs. Aligned with the goals of the new Midlands Enterprise Initiative of improving the skill levels of the Midlands workforce and addressing high levels of regional unemployment, we have developed a framework to increase the level of business intelligence used by SMEs to improve business decision-making. Many SMEs in the Midlands fail due to the lack of high quality decision making. Our framework outlines how universities can: engage with SMEs in the use of BI through operational research techniques; develop appropriate and easy to use Excel spreadsheet models; and make use of a process to allow SMEs to feedback their findings of the models. Future work will determine how well the framework performs in getting SMEs to apply BI to improve their decision-making performance.

Keywords: Business Intelligence, SMEs, decision support framework, operational research techniques

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6 Enabling Cloud Adoption Based Secured Mobile Banking through Backend as a Service

Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram

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With the increase of prevailing non-traditional rivalry, mobile banking experiences an ever changing commercial backdrop. Substantial customer demands have established to be more intricate as customers request more expediency and superintend over their banking services. To enterprise advance and modernization in mobile banking applications, it is gradually obligatory to deeply leapfrog the scuffle using business model transformation. The dramaturgical vicissitudes taking place in mobile banking entail advanced traditions to exploit security. By reforming and transforming older back office into integrated mobile banking applications, banks can engender a supple and nimble banking environment that can rapidly respond to new business requirements over cloud computing. Cloud computing is transfiguring ecosystems in numerous industries, and mobile banking is no exemption providing services innovation, greater flexibility to respond to improved security and enhanced business intelligence with less cost. Cloud technology offer secure deployment possibilities that can provision banks in developing new customer experiences, empower operative relationship and advance speed to efficient banking transaction. Cloud adoption is escalating quickly since it can be made secured for commercial mobile banking transaction through backend as a service in scrutinizing the security strategies of the cloud service provider along with the antiquity of transaction details and their security related practices.

Keywords: Business Intelligence, cloud adoption, backend as a service, secured mobile banking

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5 Factors Affecting the Adoption of Cloud Business Intelligence among Healthcare Sector: A Case Study of Saudi Arabia

Authors: Victor Chang, Muthu Ramachandran, Raed Alsufyani, Hissam Tawfik

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This study investigates the factors that influence the decision by players in the healthcare sector to embrace Cloud Business Intelligence Technology with a focus on healthcare organizations in Saudi Arabia. To bring this matter into perspective, this study primarily considers the Technology-Organization-Environment (TOE) framework and the Human Organization-Technology (HOT) fit model. A survey was hypothetically designed based on literature review and was carried out online. Quantitative data obtained was processed from descriptive and one-way frequency statistics to inferential and regression analysis. Data were analysed to establish factors that influence the decision to adopt Cloud Business intelligence technology in the healthcare sector. The implication of the identified factors was measured, and all assumptions were tested. 66.70% of participants in healthcare organization backed the intention to adopt cloud business intelligence system. 99.4% of these participants considered security concerns and privacy risk have been the most significant factors in the adoption of cloud Business Intelligence (CBI) system. Through regression analysis hypothesis testing point that usefulness, service quality, relative advantage, IT infrastructure preparedness, organization structure; vendor support, perceived technical competence, government support, and top management support positively and significantly influence the adoption of (CBI) system. The paper presents quantitative phase that is a part of an on-going project. The project will be based on the consequences learned from this study.

Keywords: Business Intelligence, Cloud Computing, HOT-fit model, TOE, healthcare and innovation adoption

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4 Business Intelligence Dashboard Solutions for Improving Decision Making Process: A Focus on Prostate Cancer

Authors: Mona Isazad Mashinchi, Davood Roshan Sangachin, Francis J. Sullivan, Dietrich Rebholz-Schuhmann

Abstract:

Background: Decision-making processes are nowadays driven by data, data analytics and Business Intelligence (BI). BI as a software platform can provide a wide variety of capabilities such as organization memory, information integration, insight creation and presentation capabilities. Visualizing data through dashboards is one of the BI solutions (for a variety of areas) which helps managers in the decision making processes to expose the most informative information at a glance. In the healthcare domain to date, dashboard presentations are more frequently used to track performance related metrics and less frequently used to monitor those quality parameters which relate directly to patient outcomes. Providing effective and timely care for patients and improving the health outcome are highly dependent on presenting and visualizing data and information. Objective: In this research, the focus is on the presentation capabilities of BI to design a dashboard for prostate cancer (PC) data that allows better decision making for the patients, the hospital and the healthcare system related to a cancer dataset. The aim of this research is to customize a retrospective PC dataset in a dashboard interface to give a better understanding of data in the categories (risk factors, treatment approaches, disease control and side effects) which matter most to patients as well as other stakeholders. By presenting the outcome in the dashboard we address one of the major targets of a value-based health care (VBHC) delivery model which is measuring the value and presenting the outcome to different actors in HC industry (such as patients and doctors) for a better decision making. Method: For visualizing the stored data to users, three interactive dashboards based on the PC dataset have been developed (using the Tableau Software) to provide better views to the risk factors, treatment approaches, and side effects. Results: Many benefits derived from interactive graphs and tables in dashboards which helped to easily visualize and see the patients at risk, better understanding the relationship between patient's status after treatment and their initial status before treatment, or to choose better decision about treatments with fewer side effects regarding patient status and etc. Conclusions: Building a well-designed and informative dashboard is related to three important factors including; the users, goals and the data types. Dashboard's hierarchies, drilling, and graphical features can guide doctors to better navigate through information. The features of the interactive PC dashboard not only let doctors ask specific questions and filter the results based on the key performance indicators (KPI) such as: Gleason Grade, Patient's Age and Status, but may also help patients to better understand different treatment outcomes, such as side effects during the time, and have an active role in their treatment decisions. Currently, we are extending the results to the real-time interactive dashboard that users (either patients and doctors) can easily explore the data by choosing preferred attribute and data to make better near real-time decisions.

Keywords: Business Intelligence, Decision Making, Healthcare, Prostate Cancer, dashboard, value-based healthcare

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3 Using Business Intelligence Capabilities to Improve the Quality of Decision-Making: A Case Study of Mellat Bank

Authors: Jalal Haghighat Monfared, Zahra Akbari

Abstract:

Today, business executives need to have useful information to make better decisions. Banks have also been using information tools so that they can direct the decision-making process in order to achieve their desired goals by rapidly extracting information from sources with the help of business intelligence. The research seeks to investigate whether there is a relationship between the quality of decision making and the business intelligence capabilities of Mellat Bank. Each of the factors studied is divided into several components, and these and their relationships are measured by a questionnaire. The statistical population of this study consists of all managers and experts of Mellat Bank's General Departments (including 190 people) who use commercial intelligence reports. The sample size of this study was 123 randomly determined by statistical method. In this research, relevant statistical inference has been used for data analysis and hypothesis testing. In the first stage, using the Kolmogorov-Smirnov test, the normalization of the data was investigated and in the next stage, the construct validity of both variables and their resulting indexes were verified using confirmatory factor analysis. Finally, using the structural equation modeling and Pearson's correlation coefficient, the research hypotheses were tested. The results confirmed the existence of a positive relationship between decision quality and business intelligence capabilities in Mellat Bank. Among the various capabilities, including data quality, correlation with other systems, user access, flexibility and risk management support, the flexibility of the business intelligence system was the most correlated with the dependent variable of the present research. This shows that it is necessary for Mellat Bank to pay more attention to choose the required business intelligence systems with high flexibility in terms of the ability to submit custom formatted reports. Subsequently, the quality of data on business intelligence systems showed the strongest relationship with quality of decision making. Therefore, improving the quality of data, including the source of data internally or externally, the type of data in quantitative or qualitative terms, the credibility of the data and perceptions of who uses the business intelligence system, improves the quality of decision making in Mellat Bank.

Keywords: Business Intelligence, Decision Making, Decision Quality, business intelligence capability

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2 Technology, Organizational and Environmental Determinants of Business Intelligence Systems Adoption in Croatian SME: A Case Study of Medium-Sized Enterprise

Authors: Dalia Suša Vugec, Ana-Marija Stjepić, Luka Sušac

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In the last few years, examples from scientific literature and business practices show that the adoption of technological innovations increases enterprises' performance. Recently, when it comes to the field of information technology innovation, business intelligence systems (BISs) have drawn a significant amount of attention of the scientific circles. BISs can be understood as a form of technological innovation which can bring certain benefits to the organizations that are adopting it. Therefore, the aim of this paper is twofold: (1) to define determinants of successful BISs adoption in small and medium enterprises and thus contribute to this neglected research area and (2) to present the current state of BISs adoption in small and medium-sized companies. In order to do so, determinants are defined and classified into three dimensions, according to the Technology – Organization – Environment (TOE) theoretical framework that describes the impact of each dimension on technological innovations adoption. Moreover, paper brings a case study presenting the adoption of BISs in practice within an organization from tertiary (service) industry sector. Based on the results of the study, guidelines for more efficient, faster and easier BISs adoption are presented.

Keywords: Business Intelligence, Case study, Business intelligence systems, Adoption, TOE framework

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1 A Predictive Model of Supply and Demand in the State of Jalisco, Mexico

Authors: M. Gil, R. Montalvo

Abstract:

Business Intelligence (BI) has become a major source of competitive advantages for firms around the world. BI has been defined as the process of data visualization and reporting for understanding what happened and what is happening. Moreover, BI has been studied for its predictive capabilities in the context of trade and financial transactions. The current literature has identified that BI permits managers to identify market trends, understand customer relations, and predict demand for their products and services. This last capability of BI has been of special concern to academics. Specifically, due to its power to build predictive models adaptable to specific time horizons and geographical regions. However, the current literature of BI focuses on predicting specific markets and industries because the impact of such predictive models was relevant to specific industries or organizations. Currently, the existing literature has not developed a predictive model of BI that takes into consideration the whole economy of a geographical area. This paper seeks to create a predictive model of BI that would show the bigger picture of a geographical area. This paper uses a data set from the Secretary of Economic Development of the state of Jalisco, Mexico. Such data set includes data from all the commercial transactions that occurred in the state in the last years. By analyzing such data set, it will be possible to generate a BI model that predicts supply and demand from specific industries around the state of Jalisco. This research has at least three contributions. Firstly, a methodological contribution to the BI literature by generating the predictive supply and demand model. Secondly, a theoretical contribution to BI current understanding. The model presented in this paper incorporates the whole picture of the economic field instead of focusing on a specific industry. Lastly, a practical contribution might be relevant to local governments that seek to improve their economic performance by implementing BI in their policy planning.

Keywords: Business Intelligence, Supply and demand, predictive model, Mexico

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