Requirements Engineering for Data Warehouses (RE4DW): From Strategic Goals to Multidimensional Model

Nasiri, Azadeh; Ahmed, Waqas; Wrembel, Robert; Zimányi, Esteban

doi:10.1007/978-3-319-70625-2_13

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Data warehouse is a key technology of BI that integrates data from different sources for analytical purposes (Arnott, 2008;Inmon et al, 2008;Nasiri et al, 2017). It integrates data from disparate, heterogeneous, organization-wide systems and often represents them in a multidimensional model (Nasiri et al, 2017).…”

Section: Business Intelligence Project Implementation Methodologiesmentioning

confidence: 99%

Critical Success Factors for Implementing Business Intelligence Projects (A BI Implementation Methodology Perspective)

Ranjbarfard

Hatami

2020

IJIKM

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Aim/Purpose: The purpose of this paper is to identify Critical Success Factors (CSFs) for Business Intelligence (BI) implementation projects by studying the existing BI project implementation methodologies and to compare these methodologies based on the identified CSFs. Background: The implementation of BI project has become one of the most important technological and organizational innovations in modern organizations. The BI project implementation methodology provides a framework for demonstrating knowledge, ideas and structural techniques. It is defined as a set of instructions and rules for implementing BI projects. Identifying CSFs of BI implementation project can help the project team to concentrate on solving prior issues and needed resources. Methodology: Firstly, the literature review was conducted to find the existing BI project implementation methodologies. Secondly, the content of the 13 BI project implementation methodologies was analyzed by using thematic analysis method. Thirdly, for examining the validation of the 20 identified CSFs, two questionnaires were distributed among BI experts. The gathered data of the first questionnaire was analyzed by content validity ratio (CVR) and 11 of 20 CSFs were accepted as a result. The gathered data of the second questionnaire was analyzed by fuzzy Delphi method and the results were the same as CVR. Finally, 13 raised BI project implementation methodologies were compared based on the 11 validated CSFs. Contribution: This paper contributes to the current theory and practice by identifying a complete list of CSFs for BI projects implementation; comparison of existing BI project implementation methodologies; determining the completeness degree of existing BI project implementation methodologies and introducing more complete ones; and finding the new CSF “Expert assessment of business readiness for successful implementation of BI project” that was not expressed in previous studies. Findings: The CSFs that should be considered in a BI project implementation include: “Obvious BI strategy and vision”, “Business requirements definition”, “Business readiness assessment”, “BI performance assessment”, “Establishing BI alignment with business goals”, “Management support”, “IT support for BI”, “Creating data resources and source data quality”, “Installation and integration BI programs”, “BI system testing”, and “BI system support and maintenance”. Also, all the 13 BI project implementation methodologies can be divided into four groups based on their completeness degree. Recommendations for Practitioners: The results can be used to plan BI project implementation and help improve the way of BI project implementation in the organizations. It can be used to reduce the failure rate of BI implementation projects. Furthermore, the 11 identified CSFs can give a better understanding of the BI project implementation methodologies. Recommendation for Researchers: The results of this research helped researchers and practitioners in the field of business intelligence to better understand the methodology and approaches available for the implementation and deployment of BI systems and thus use them. Some methodologies are more complete than other studied methodologies. Therefore, organizations that intend to implement BI in their organization can select these methodologies according to their goals. Thus, Findings of the study can lead to reduce the failure rate of implementation projects. Future Research: Future researchers may add other BI project implementation methodologies and repeat this research. Also, they can divide CSFs into three categories including required before BI project implementation, required during BI project implementation and required after BI project implementation. Moreover, researchers can rank the BI project implementation CSFs. As well, Critical Failure Factors (CFFs) need to be explored by studying the failed implementations of BI projects. The identified CSFs probably affect each other. So, studying the relationship between them can be a topic for future research.

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Section: Business Intelligence Project Implementation Methodologiesmentioning

confidence: 99%

Critical Success Factors for Implementing Business Intelligence Projects (A BI Implementation Methodology Perspective)

Ranjbarfard

Hatami

2020

IJIKM

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“…The work of (Nasiri et al, 2017) proposes a Goal oriented RE framework for DW systems, calledRE4DW,whichiscomposedoftwomodelingcomponents.Oneisthecontextmodelingthat identifiestheorganizationaldatarequiredtodesignaDWsupportingKeyPerformanceIndicators 'KPIs'formonitoringpurposesbymeansoftheI*framework.Thesecondcomponentisthedata modelingthatusesamultidimensionalmodeltoassuretheappropriatenessoftheDWdataschemata. InthecontextofVolunteerDataWarehouse,itisimportanttonotethatthisframeworkproposes aformalizedsetofstepswithaniterativeguidance,whichgoesinlinewiththe'formalandlight documentation'principle.…”

Section: Requirement Elicitation Design and Implementation Stepsmentioning

confidence: 99%

Volunteer Data Warehouse

Sakka

Bimonte

Pinet

et al. 2021

International Journal of Data Warehousing and Mining

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With the maturity of crowdsourcing systems, new analysis possibilities appear where volunteers play a crucial role by bringing the implicit knowledge issued from practical and daily experience. At the same time, data warehouse and OLAP systems represent the first citizen of decision-support systems. They allow analyzing a huge volume of data according to the multidimensional model. The more the multidimensional model reflects the decision-makers' analysis needs, the more the DW project is successful. However, when volunteers are involved in the design of DWs, existing DW design methodologies present some limitations. In this work, the authors present the main features of volunteer data warehouse (VDW) design, and they study the main existing DW design methodology to find out how they can contribute to fulfil the features needed by this particular DW approach. To provide a formal framework to classify existing work, they provide a study of differences between classical DW users and volunteers. The paper also presents a set of open issues for VDW.

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Prototipe data warehouse pada instansi pelayanan pajak daerah: Studi kasus pada Badan Pendapatan Daerah Kabupaten Badung

Pratama

2020

EMACS Journal

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Perpajakan daerah merupakan salah satu sarana pemerintah daerah untuk mendapatkan anggaran belanja pembangunan. Data perpajakan daerah sangat penting karena menjadi dasar rencana kemampuan pemerintah daerah untuk membuat anggaran. Sistem yang terintegrasi menjadi sangat penting guna mendapatkan sumber data yang lengkap dan valid untuk kepentingan analisis kebijakan daerah. Penelitian ini akan memberikan rancangan data warehouse bagi instansi pengelola pajak daerah guna pengelolaan data analisis yang lengkap dan cepat. Perancangan data warehouse dalam penelitian ini akan mengadopsi perancangan data warehouse oleh Ralph Kimball. Hasil dari penelitian ini adalah berupa rancangan data warehouse dengan sumber data dari beberapa aplikasi operasional yang ada yang dapat digunakan sebagai sumber data analisis yang lengkap dan cepat. Perancangan data warehouse mampu memberikan mekanisme integrasi data dan kemampuan analisis yang lebih luas.

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Requirements Engineering for Data Warehouses (RE4DW): From Strategic Goals to Multidimensional Model

Cited by 3 publications

References 12 publications

Critical Success Factors for Implementing Business Intelligence Projects (A BI Implementation Methodology Perspective)

Critical Success Factors for Implementing Business Intelligence Projects (A BI Implementation Methodology Perspective)

Volunteer Data Warehouse

Prototipe data warehouse pada instansi pelayanan pajak daerah: Studi kasus pada Badan Pendapatan Daerah Kabupaten Badung

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