Community and data integration approach using requirement centric operational data store model (ReCODS-Model) for business intelligence applications

Building a Business Intelligence (BI) application is very challenging as it is a young discipline and does not yet offer well-established strategies and techniques for the developments process when compared to the software engineering discipline. Furthermore, information requirements analysis for BI applications which integrate data from heterogeneous sources differs significantly from requirements analysis for a conventional information system. Requirement Centric Operational Data Store Model (ReCODS-M) to build BI application that focuses on operational information to support business operations is proposed. In this model, combination of community interaction and data integration approach were used to identify the requirements for developing BI application. Furthermore, how the operational data store can be used for operational and tactical information and can be transferred to a data warehouse for supporting analytical information and decision making is also presented. Finally, to verify and validate the proposed model, the case study approach using web application development in selected subject areas is elaborated

Model Reka Bentuk Konseptual Operasian Storan Data Bagi Aplikasi Kepintaran Perniagaan

The development of business intelligence (BI) applications, involving of data sources, Data Warehouse (DW), Data Mart (DM) and Operational Data Store (ODS), imposes a major challenge to BI developers. This is mainly due to the lack of established models, guidelines and techniques in the development process as compared to system development in the discipline of software engineering. Furthermore, the present BI applications emphasize on the development of strategic information in contrast to operational and tactical. Therefore, the main aim of this study is to propose a conceptual design model for BI applications using ODS (CoDMODS). Through expert validation, the proposed conceptual design model that was developed by means of design science research approach, was found to satisfy nine quality model dimensions, which are, easy to understand, covers clear steps, is relevant and timeless, demonstrates flexibility, scalability, accuracy, completeness and consistency. Additionally, the two prototypes that were developed based on CoDMODS for water supply service (iUBIS) and telecommunication maintenance (iPMS) recorded a high usability average min value of 5.912 using Computer System Usability Questionnaire (CSUQ) instrument. The outcomes of this study, particularly the proposed model, contribute to the analysis and design method for the development of the operational and tactical information in BI applications. The model can be referred as guidelines by BI developers. Furthermore, the prototypes that were developed in the case studies can assist the organizations in using quality information for business operations

Conceptual design model using operational data store(CoDMODS) for developing business intelligence applications

Building a Business Intelligence (BI) application is very challenging as it is a young discipline and does not yet offer well-established strategies and techniques for the developments process when compared to the software engineering discipline. Furthermore, information requirements analysis for BI applications which integrate data from heterogeneous sources differs significantly from requirements analysis for a conventional information system.Conceptual Design Model Operational Data Store (CoDMODS) to build BI application that focuses on operational information to support business operations is proposed.In this model, combination of community interaction and data integration approach were used to identify the requirements for developing BI application.Furthermore, how the operational data store can be used for operational and tactical information and can be transferred to a data warehouse for supporting analytical information and decision making is also presented. Finally, to verify and validate the proposed model, the case study approach using web application development in selected subject areas is elaborated

IS THERE STILL A NEED FOR MULTIDIMENSIONAL DATA MODELS?

Organizational and technical changes challenge standards of data warehouse design and initiate a redesign of contemporary Business Intelligence and Analytics environments. As a result, the use of multidimensional models for performance oriented reasons is not necessarily taken for granted. Simple data models or operational structures emerge as a basis for complex analyses. The paper therefore conducts a laboratory experiment to examine from a non-technical perspective the influnce of different data modeling types on the representational information quality of end users. A comparison is made between the multidimensional model and the transactional model respectively the flat file model. The experiment involves 78 participants and aims to compare perceived and observed representational information quality aspects of ad hoc analyses regarding the data modeling type. The results indicate a higher observed quality for multidimensional modeled data, while different types of data models do not influnce the end user perception of the representational information quality

SUPPORTING FINANCIAL DATA WAREHOUSE DEVELOPMENT: A COMMUNICATION THEORY-BASED APPROACH

Data warehouses increasingly play important roles in the information technology landscape of the financial industry. However, semantic heterogeneity is high in banking – data is defined differently by different banks, business units, and users. Therefore data integration in financial data warehouse development projects relies on the knowledge, know-how, and judgment of human experts. Up to now, methodical support is missing for the communication process among experts that determine and negotiate a shared understanding of requirements. In contrast to ontologydriven or schema-matching approaches proposing the automatic resolution of differences ex-post, we introduce an approach that addresses data integration already in early project phases. Our approach supports developing shared understanding of domain concepts and data fields in financial data warehouse projects, good communication of all participants while the project progresses, and early detection of errors within projects. This way, we prevent problems that result from the ex-post resolution of semantic heterogeneity

Deriving Initial Data Warehouse Structures from the Conceptual Data Models of the Underlying Operational Information Systems

In recent years the construction of large scale data schemes for operational systems has been the major problem of conceptual data modeling for business needs. Multidimensional data structures used for decision support applications in data warehouses have rather different requirements to data modeling techniques. In case of operational systems the data models are created from application specific requirements. The data models in data warehouses base on the analytical requirements of the users. Furthermore, the development of data warehouse structures implicates the consideration of user-defined information requirements as well as the underlying operational source systems. In this paper we show that the conceptual data models of the underlying operational information systems can support the construction of multidimensional structures. We would like to point out that the special features of the Structured Entity Relationship Model (SERM) are not only useful for the development of big ope..

Proceedings of the University Alliance Executive Directors Workshop - ECIS 2001

The introduction of Enterprise Systems (ES) into the curriculum at Business and IS Faculties and Schools is for many universities a major challenge. However, this problem is in various aspects of special nature: the students' demand is enormous and in many cases product-focused, Enterprise Systems are typically very comprehensive and complex, and knowledge about ES is often missing. By the time textbooks of satisfying quality are available, there are new systems' upgrades and innovation cycles to deal with. The Queensland University of Technology (QUT) is comprehensively using the market leading Enterprise System SAP R/3 within its curriculum and is also conducting research in this area. This paper briefly reports on the activities and experiences at QUT, which is now one of the few world-wide mySAP University Application Hosting Centers.<br/

Uma metodologia para desenvolvimento da data warehouse e estudo de caso

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.O ambiente de data warehouse (DW) surgiu como uma evolução dos ambientes de suporte a decisão, integrando fontes de dados dos sistemas transacionais. Sua crescente popularidade reflete a necessidade das empresas em obter informações analíticas derivadas dos seus sistemas transacionais. O ambiente de data warehouse tem características diferentes do ambiente tradicional e é construído tendo-se em mente as necessidades de processamento analítico das organizações. Os projetos de data warehouse têm mais chances de sucesso quando desenvolvidos através de uma metodologia consistente que identifique e guie o projetista durante as várias fases do projeto. Neste trabalho, apresentamos três metodologias de desenvolvimento de data warehouse identificadas a partir de pesquisa bibliográfica. Estas metodologias foram avaliadas com o objetivo de verificar a sua efetiva aplicabilidade em projetos de desenvolvimento de data warehouses. A partir desta avaliação concluiu-se que as metodologias apresentadas não podem ser utilizadas integralmente como base para a construção de sistemas de data warehouse. Conseqüentemente, elaborou-se uma proposta de metodologia com o objetivo de suprir as deficiências e limitações das metodologias pesquisadas. Posteriormente, essa metodologia proposta, foi utilizada para desenvolver o estudo de caso desta dissertação. O estudo de caso tem a finalidade de verificar e avaliar a aplicabilidade da metodologia proposta. Para o estudo de caso foi utilizado o sistema de Concurso Vestibular da UNIJUÍ sediada na cidade de Ijuí (RS)

Developing a data quality scorecard that measures data quality in a data warehouse

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe main purpose of this thesis is to develop a data quality scorecard (DQS) that aligns the data quality needs of the Data warehouse stakeholder group with selected data quality dimensions. To comprehend the research domain, a general and systematic literature review (SLR) was carried out, after which the research scope was established. Using Design Science Research (DSR) as the methodology to structure the research, three iterations were carried out to achieve the research aim highlighted in this thesis. In the first iteration, as DSR was used as a paradigm, the artefact was build from the results of the general and systematic literature review conduct. A data quality scorecard (DQS) was conceptualised. The result of the SLR and the recommendations for designing an effective scorecard provided the input for the development of the DQS. Using a System Usability Scale (SUS), to validate the usability of the DQS, the results of the first iteration suggest that the DW stakeholders found the DQS useful. The second iteration was conducted to further evaluate the DQS through a run through in the FMCG domain and then conducting a semi-structured interview. The thematic analysis of the semi-structured interviews demonstrated that the stakeholder's participants‘ found the DQS to be transparent; an additional reporting tool; Integrates; easy to use; consistent; and increases confidence in the data. However, the timeliness data dimension was found to be redundant, necessitating a modification to the DQS. The third iteration was conducted with similar steps as the second iteration but with the modified DQS in the oil and gas domain. The results from the third iteration suggest that DQS is a useful tool that is easy to use on a daily basis. The research contributes to theory by demonstrating a novel approach to DQS design This was achieved by ensuring the design of the DQS aligns with the data quality concern areas of the DW stakeholders and the data quality dimensions. Further, this research lay a good foundation for the future by establishing a DQS model that can be used as a base for further development