Business Intelligence Projects Research Articles

Business Strategies Based on Large Sets of Data and Interaction: Business Intelligence

.The dominant perspective in Business Intelligence (BI) projects and applications has been the technological conception, usually focused on the technical-instrumental nature of computing. This conception has avoided the change in the paradigm from a business model based on the use of tangible resources in favour of one based on the exploitation of intangible resources (data, interaction, networks, etc.). This would explain why applied BI projects, remain anchored in the old organization and operational patterns of traditional businesses in most companies. The technological perception of BI gives continuity to the stovepipe activity of companies, both in their management and in their organizational structures, where the impact of interaction as a generator of business opportunities is very limited, and often non-existent; and the effect of large volumes of data as a value generator is reduced to an operational and technical problem. Hence, the importance of considering BI as a new business philosophy that entails new forms of business organization and a new way of management based on the interaction and analysis of large volumes of internally generated data. Our interest is not only to emphasize the nature of the new business philosophy in the application of BI, but to carry out a discussion about the organizational and operational structure of businesses -according to a conception based on interaction and data as generators of business value-, and about actionable intelligence.

Adapting Agile Practices for Data Warehousing, Business Intelligence, and Analytics

Business surveys indicate that fewer than 30% of data warehousing and business intelligence (DW/BI) projects meet the stated goals of the budget, schedule, and quality. Agile methods have been suggested as a possible solution, but because of the large size of the typical DW/BI project, it may be difficult to apply the agile values and principles. In this article, the following research questions are raised: Can agile practices be adapted for DW/BI development? What factors influence agile DW/BI development? Six semi-structured interviews were conducted using a questionnaire. The interview transcripts were coded using the grounded theory approach. Eight categories emerged from the analysis: business value, project management, agile development, shared understanding, technological capability, top management commitment, complexity, and organizational culture. Based on the categories, a research framework is proposed. The findings reveal that agile methods are suited for only certain aspects of DW/BI projects and need to be augmented with project management practices.

BUSINESS INTELLIGENCE AND ITS ROLE IN ORGANIZATIONAL AGILITY

Business Intelligence (BI) is one of the business tools on which competitive advantages of various world-class companies are based. Also in the highly competitive and changing global environment, the ability of an organization to detect changes, make decisions and to do a fast implementation, is the difference between grow or disappear; this attribute in an organization is named Organizational Agility (AO). In this paper, through the systematic review of the literature, the state of art of both fields (BI and AO) is established and in particular studies examining the relationship between the two. The research about this relation is beginning, and therefore is a field with much to explore.About BI, its evolution is denoted on the concepts of maturity, technical and analytical technologies. Referring to organizational agility, the concept is reviewed as such and factors that promote, with special emphasis on the factors for the couple of concepts. The conclusion is that a positive relationship is two-way, the AO is favored with BI projects in BI mature companies, and agile enterprises obtain better results of BI.

Business intelligence and its role in organizational agility

Business Intelligence (BI) is one of the business tools on which competitive advantages of various world-class companies are based. Also in the highly competitive and changing global environment, the ability of an organization to detect changes, make decisions and to do a fast implementation, is the difference between grow or disappear; this attribute in an organization is named Organizational Agility (AO). In this paper, through the systematic review of the literature, the state of art of both fields (BI and AO) is established and in particular studies examining the relationship between the two. The research about this relation is beginning, and therefore is a field with much to explore.About BI, its evolution is denoted on the concepts of maturity, technical and analytical technologies. Referring to organizational agility, the concept is reviewed as such and factors that promote, with special emphasis on the factors for the couple of concepts. The conclusion is that a positive relationship is two-way, the AO is favored with BI projects in BI mature companies, and agile enterprises obtain better results of BI.

Key success factors to business intelligence solution implementation

Business intelligence (BI) solutions have been adopted within organizations as amean to achieve a more grounded decision making process that results in better organizational outcomes. Nowadays, about 70% to 80% of business intelligence implementation projects fail due to both technological and managerial issues. Multi-methodology proposed by Mingers (2006) was followed to develop the research in four phases: appreciation, where documental search wasconducted through a literature review; analysis, where hypothetical structures related with the key success factors were proposed; assessment, where key success factors were assessed along with experts; and action, where research results discussion was shown. As a result, 13 factors that affect the business intelligence solution’s success were identified. Those factors contributeto improve planning and implementation of business intelligence projects, accomplishing in a greater extent the purposes of these projects.

Third special issue on knowledge discovery and business intelligence

Third special issue on knowledge discovery and business intelligence

Critical success factors: the success of business intelligence systems

Business intelligence (BI) systems have the potential to improve business performance by facilitating innovations, creating new products and service, and enhancing decision making effectiveness. However, it requires certain institutional and scientific capabilities to fully realise the values of BI systems. One of the critical issues concerned with BI systems (BIS) for both researchers and practitioners is to identify the factors which are very important for successful implementation of business intelligence projects. Hence, this paper provides a brief explanation about the most general and impact factors which can be impacting in implementing business intelligence projects in organisations. I anticipated that it is critical to determine these factors, particularly for practitioners of those companies are involved in carry out business intelligence projects and they face a problem in evaluating the willingness of their organisations before implementing the project in pre-implementation stage. The objective of this paper is to provide a better understanding of the vital and valuable success factors and it is to conduct a survey and comprehensive study of the critical success factors before implementation phase of the willingness by categorising the factors into two main groups; institutional and scientific factors. It is clear that each category has its own features and a brief explanation of each factor is discussed.

Critical success factors: the success of business intelligence systems

Business intelligence (BI) systems have the potential to improve business performance by facilitating innovations, creating new products and service, and enhancing decision making effectiveness. However, it requires certain institutional and scientific capabilities to fully realise the values of BI systems. One of the critical issues concerned with BI systems (BIS) for both researchers and practitioners is to identify the factors which are very important for successful implementation of business intelligence projects. Hence, this paper provides a brief explanation about the most general and impact factors which can be impacting in implementing business intelligence projects in organisations. I anticipated that it is critical to determine these factors, particularly for practitioners of those companies are involved in carry out business intelligence projects and they face a problem in evaluating the willingness of their organisations before implementing the project in pre-implementation stage. The objective of this paper is to provide a better understanding of the vital and valuable success factors and it is to conduct a survey and comprehensive study of the critical success factors before implementation phase of the willingness by categorising the factors into two main groups; institutional and scientific factors. It is clear that each category has its own features and a brief explanation of each factor is discussed.

Optimising business intelligence results through strategic application of software process model

According to Gartner, 60% of organisations are still unable to make fruitful decisions due to various factors like, data quality issues, lack of careful consideration of components involved, skill shortage. In this paper, we consider the software process, which is one of the key factors and show how a software process model may be applied to business intelligence (BI) process by defining the entire BI process as a two stage software component process model which internally involves other components. For this purpose, we have defined two software process component models for the entire BI process. We also emphasise all key considerations that need to be followed to achieve successful BI results. We throw light on how the sequential iteration phase can be applied at each component level. This model serves as a prototype for the future BI projects which can benefit organisations and top decision makers in making quality decisions.

Using OLAP Data Cubes in Business Intelligence

Abstract The purpose of this paper is to demonstrate that it is possible to develop business intelligence projects in big and medium-size organizations, only with Microsoft products, used in accordance with standard OLAP cube technology, and presented possible alternatives, in relation with the requested functions.

Data Warehouses and Business Intelligence in Croatia

Authors of this paper were directly involved in several data warehousing and business intelligence projects that were carried out in companies in Croatia. Based on the experience from those projects, this paper presents how those solutions were implemented — and more importantly, how they were used by managers at different levels of management. Three different companies are described, including three different implemented solutions. One company was small, one was mid-sized and one was large. All had differences in ownership and education level of managers. Implementation (technical) details are omitted, but this paper shows that managers do not always see what business intelligence can do. Furthermore, it is shown that such projects do not always have to be expensive.

Application of an integrated multi-criteria decision making AHP-TOPSIS methodology for ETL software selection.

Actually, a set of ETL software (Extract, Transform and Load) is available to constitute a major investment market. Each ETL uses its own techniques for extracting, transforming and loading data into data warehouse, which makes the task of evaluating ETL software very difficult. However, choosing the right software of ETL is critical to the success or failure of any Business Intelligence project. As there are many impacting factors in the selection of ETL software, the same process is considered as a complex multi-criteria decision making (MCDM) problem. In this study, an application of decision-making methodology that employs the two well-known MCDM techniques, namely Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods is designed. In this respect, the aim of using AHP is to analyze the structure of the ETL software selection problem and obtain weights of the selected criteria. Then, TOPSIS technique is used to calculate the alternatives’ ratings. An example is given to illustrate the proposed methodology. Finally, a software prototype for demonstrating both methods is implemented.

Business Intelligence Readiness for Higher Learning Institution (IHL): Preliminary Study and Research Model

Introduction: Business Intelligence Systems (BIS) are the most potential application for many popular organizations in different industries such as telecommunication, healthcare, airline, transport, government and others. The implementation of Business Intelligence (BI) for Institutions of Higher Learning (IHL) is still at the early stage for most IHL organizations. Results of previous studies have found that more than half of BI projects fail to meet their objectives even though a lot money is spent. Based on that problem, it is important to identify the readiness level of BI for IHL in order to reduce the risk before the actual BI project is implemented. In this paper, a rigorous literature review on success factors such as Critical Success Factors (CSFs), Readiness Factors (RFs), Success Factors (SFs) are discussed by different authors. Aims: The aim of study is the Business Intelligent Readiness Model (BiRM) as a guild for IHL before implementing the BI system. Study Protocol Article Rahmat et al.; BJAST, 15(5): 1-10, 2016; Article no.BJAST.24383 2 Study Design: For this study, semi-structured interviews will be chosen as a method of collecting relevant qualitative data to explore and refine the model of BI readiness model for IHL in Malaysian. Here, the list of themes, issues to be addressed and questions to be asked were identified and predefined by the researcher. The use of semi-structured interviews ensures that information is captured from the respondent’s perspectives rather than being imposed by the researcher. Place and Duration of Study: The study will be conducted at Malaysian Institutions of Higher Learning (IHL). This study is expected to finish within a 12-month period. Methodology: The study is proposed to be conducted through a mix of methodology comprising qualitative and quantitative methods, including interviews and observations with a range of stakeholders such as ministry officers, computer centre managers, technicians and the end users from different IHL in Malaysia. Results: The result of the data collection will be analyzed using the causal modelling approach of SEM SmartPLS for the validity and the relationship between construct. The expected finding from this research is the Business Intelligent Readiness Model (BiRM) as a guild for IHL before implementing the BI system.

Effectiveness of Agile Implementation Methods in Business Intelligence Projects from an End-user Perspective

The global Business Intelligence (BI) market grew by 10% in 2013 according to the Gartner Report. Today organizations require better use of data and analytics to support their business decisions. Internet power and business trend changes have provided a broad term for data analytics – Big Data. To be able to handle it and leverage a value of having access to Big Data, organizations have no other choice than to get proper systems implemented and working. However traditional methods are not efficient for changing business needs. The long time between project start and go-live causes a gap between initial solution blueprint and actual user requirements in the end of the project. This article presents the latest market trends in BI systems implementation by comparing Agile with traditional methods. It presents a case study provided in a large telecommunications company (20K employees) and the results of a pilot research provided in the three large companies: telecommunications, digital, and insurance. Both studies prove that Agile methods might be more effective in BI projects from an end-user perspective and give first results and added value in a much shorter time compared to a traditional approach.

Business Intelligence and Big Data Analytics for Higher Education: Cases from UK Higher Education Institutions

With the rise of big data, its impact has becoming apparent even in the Higher Education sector. The strategic use and applications of big data in higher education would lead to higher educational quality and better student and staff experience. Using the output from UK JISC’s Business Intelligence projects, this paper reviews and outlines eleven cases of big data analytics in the UK Higher Education institutions, followed by presenting one of the projects to demonstrate the application of big data analytics on student engagement. This leads to further research implications for future big data research in Higher Education.

Extract Transform Load Data with ETL Tools

As we all know business intelligence (BI) is considered to have an extraordinary impact on businesses. Research activity has grown in the last years. A significant part of BI systems is a well performing Implementation of the Extract, Transform, and Load (ETL) process. In typical BI projects, implementing the ETL process can be the task with the greatest effort. Here, set of generic Meta model constructs with a palette of regularly used ETL activities, is specialized, which are called templates. Keywords: ETL tools, transform, data, business, intelligence

A Data Quality in Use model for Big Data

Beyond the hype of Big Data, something within business intelligence projects is indeed changing. This is mainly because Big Data is not only about data, but also about a complete conceptual and technological stack including raw and processed data, storage, ways of managing data, processing and analytics. A challenge that becomes even trickier is the management of the quality of the data in Big Data environments. More than ever before the need for assessing the Quality-in-Use gains importance since the real contribution–business value–of data can be only estimated in its context of use. Although there exists different Data Quality models for assessing the quality of regular data, none of them has been adapted to Big Data. To fill this gap, we propose the “3As Data Quality-in-Use model”, which is composed of three Data Quality characteristics for assessing the levels of Data Quality-in-Use in Big Data projects: Contextual Adequacy, Operational Adequacy and Temporal Adequacy. The model can be integrated into any sort of Big Data project, as it is independent of any pre-conditions or technologies. The paper shows the way to use the model with a working example. The model accomplishes every challenge related to Data Quality program aimed for Big Data. The main conclusion is that the model can be used as an appropriate way to obtain the Quality-in-Use levels of the input data of the Big Data analysis, and those levels can be understood as indicators of trustworthiness and soundness of the results of the Big Data analysis.

A data pipeline for PHM data-driven analytics in large-scale smart manufacturing facilities


 
 
 The term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and processing of information across the factory will be used to produce advanced manufacturing intelligence that can optimize every aspect of its operation. In recent years, initiatives and groups such as the Smart Manufacturing Leadership Coalition (SMLC), Industry 4.0, and the Industrial Internet Consortium (IIC), have led the way by bringing together industry, academia and government to establish policies, roadmaps and platforms to support smart manufacturing. Although there are many characteristics that can be associated with smart manufacturing across these initiatives, a common theme is the emphasis on transitioning operations from reactive and responsive, to predictive and preventative. The research presented in this paper focuses on the development of a data pipeline that supports the development of data-driven Prognostics and Health Management (PHM) applications. In the context of smart manufacturing, PHM enables facilities to transition from preventative and reactive maintenance strategies, to predictive, preventative and condition-based strategies. The benefits that can be derived by PHM are aligned with those of smart manufacturing, which include the opportunity to decrease costs, increase machine availability, reduce energy consumption, and improve production yield.
 However, the process of ingesting, cleaning and transforming real-time data streams for data-driven PHM is a difficult, complex and time-consuming task, with estimates from business intelligence projects ranging from 80% to 90% of total project effort. This effort may only be exacerbated further in manufacturing environments due to additional technology challenges, such as low levels of standardization, disparate protocols and interfaces, and ad hoc data management. While emerging technologies such as Cyber Physical Systems (CPS) and Internet of Things (IoT) can overcome many of these challenges and provide an open platform for transmitting data, existing large-scale manufacturing facilities that are subject to compliance, regulation, and stringent quality
 
 
 assurance policies may not be able to adopt these technologies in the short-term due to the associated cost, risk and effort. Therefore, PHM applications that need to access data streams in large-scale manufacturing facilities must do so using transparent data integration that does not discriminate between emerging and legacy technologies in the factory. To this end, this research presents a real- time, scalable, robust, and fault tolerant data pipeline for ingesting, cleaning, transforming, processing and contextualizing time-series data from a wide-range of sources in the factory.
 
 

Suboptimal business intelligence implementations: understanding and addressing the problems

Purpose – The purpose of this paper is to examine the failures of business intelligence (BI) implementations and to understand why they fail as well as what action can be taken to ensure implementation success. Design/methodology/approach – The paper is based on a literature review of academic journals and case studies relating to BI, and the success and failure of the implementation of such projects. It focuses on four areas of BI projects to measure success: return on investment, non-concrete measures, project management measures and user satisfaction. The literature provides insights into what factors contribute to the success of a BI implementation and what factors contribute to the failure. Once the failures can be ascertained, a strategic approach to remedying the failure is discussed. Findings – Implementation failure specifically relating to BI is a rarely discussed topic. This paper provides an understanding of why BI implementations fail and how organisations can ensure, prior to implementing su...

Measuring the Impact of Data Warehouse and Business Intelligence on Enterprise Performance in Peru: A Developing Country

The purpose of this research is to assess the impact of data warehouse and business intelligence on the business performance of enterprises in developing regions such as Peru. Two models were developed; the qualitative exploratory model employed 23 interviews from several business sectors that use data warehouse and business intelligence. This model was exploratory in nature and helped to identify specific factors relevant to data warehouse and business intelligence in developing countries. The quantitative model examined 110 survey responses from different business sectors that use data warehouse and business intelligence. The DeLone and McLean model from 2003 was applied to test the fit of a structural equation model. Through this model, several hypotheses were tested. Thereafter, insights were combined from a review of the literature and from qualitative and quantitative analysis to consider the extension of additional constructs relevant to studies of data warehouse and business intelligence in developing economies. The additional constructs include: financial investment, alignment between the business intelligence project and business, and technology and specific skills. Implications for the study of advanced technologies in developing regions are considered.