A communication efficiency model for etl projects in financial data warehousing

The financial industry relies greatly on information technology (IT) because of its work with immaterial goods. Nowadays, most information collected by a financial institution is usually stored in a central data warehouse system. This central financial data warehouse (FDWH) is permanently under construction. Requirements from all over the bank have to be met by FDWH projects in an ongoing process. These requirements need to be communicated to the FDWH project team to do implementations properly throughout the system landscape. Especially the creation of extraction, transformation and loading (ETL) processes depends on the project team’s communication ability and given communication barriers. Enhancing recent research in FDWH a theoretical efficiency model based on philosophy of language and project management fundamentals is built in this paper. The conceptualization of information systems development projects as language communities is an important presumption for this theoretical model. Suggestions derived from this model lead project managers of FDWH projects to more appropriate decisions keeping efficiency drivers in communication in mind

THE ROLE OF BOUNDARY OBJECTS AND BOUNDARY SPANNING IN DATA WAREHOUSING – A RESEARCH-IN-PROGRESS REPORT

Data warehouse projects bring together different communities of practice, with the primary objective of producing one body of information which is capable of comparative advantages in business analysis. Due to the number of involved communities and the complexity of their collaboration, data warehouse projects are costly. In this paper we give a closer look at communication problems on boundaries between participating data warehouse projects’ communities. Our analysis enlightens the potential relation between the early creation of language communities of the involved communities and lowering data warehouse project development costs. As today, there is hardly any methodology available for analyzing and aligning mutual understanding between data warehouse project participants. In this paper, we propose a data warehouse development scheme for project improvement based on our discussion as a first step in a design science project

Process monitoring IAN Agroparks in India : Transforum report 2009

This is the first report of the TransForum project Process monitoring agroparks international, which focuses on India and specific on the development of the IFFCO Kisan SEZ Nellore in the south of India. It contains an overview of process design and the content of the proposition of IAN agroparks in India for 2009

What Turkey expects from logistics outsourcing ?

Copyright @ 2011 Yasar UniversityThe economies of the world have become increasingly interdependent, and organizations have come under tremendous pressure to maximize productivity and profitability. Creating value through outsourcing has emerged as a popular competitive strategy for firms of all sizes in all types of industries. The aim of this research is to investigate the use of third party logistics in Turkish companies from the users’ perspective to identify the types of logistics services outsourced, problems encountered in outsourcing these services, logistics costs, decision makers in outsourcing logistics activities, and information sources used in the decision-making process. A structured survey was selected as the tool for data collection. The field study involved face-to-face interviews with 204 companies out of top 500 companies ranked in terms of turnover that are registered with industrial associations and chambers of commerce in Turkey. Moreover, a decision support system based on Bayesian Causal Map is proposed for 3PLs in order to assist them in their service proposals for different sectors. This study is a first attempt to reveal and compare the outsourcing perception of the companies in different sectors, to expose the firms’ underlying motives as well as the respective importance of these motives for outsourcing logistics activities in Turkey. The use of Bayesian Causal Map based on the survey results provides an important guide to the 3PL providers to pick a suitable strategy and to prioritize their operational activities in different sectors in such a way to achieve a competitive advantage

On the quest for multi-methods in IS evaluation: a qualitative comparative analysis

This paper responds to calls for new approaches to IS evaluation. It does this by introducing fuzzy-set Qualitative Comparative Analysis as a new IS evaluation approach that augments the qualitative tradition by supporting cross-case analysis and theory development. Rather than disaggregating cases into independent, analytically-separate variables, fuzzy-set Qualitative Comparative Analysis advocates an approach to IS evaluation which explores the holistic effects of causal conditions working in conjunction with each other. The paper uses qualitative coding procedures and fuzzy-set Qualitative Comparative Analysis in a sequential manner to discover two typologies of monitoring systems success based on automated and manual validations respectively. Theoretical, methodological and practical implications of the use of fuzzy-set Qualitative Comparative Analysis are discussed in the context of a multi-case evaluation of monitoring systems established in the course of the implementation of a major European Union socio-economic support programme

ETL for data science?: A case study

Big data has driven data science development and research over the last years. However, there is a problem - most of the data science projects don't make it to production. This can happen because many data scientists don’t use a reference data science methodology. Another aggravating element is data itself, its quality and processing. The problem can be mitigated through research, progress and case studies documentation about the topic, fostering knowledge dissemination and reuse. Namely, data mining can benefit from other mature fields’ knowledge that explores similar matters, like data warehousing. To address the problem, this dissertation performs a case study about the project “IA-SI - Artificial Intelligence in Incentives Management”, which aims to improve the management of European grant funds through data mining. The key contributions of this study, to the academia and to the project’s development and success are: (1) A combined process model of the most used data mining process models and their tasks, extended with the ETL’s subsystems and other selected data warehousing best practices. (2) Application of this combined process model to the project and all its documentation. (3) Contribution to the project’s prototype implementation, regarding the data understanding and data preparation tasks. This study concludes that CRISP-DM is still a reference, as it includes all the other data mining process models’ tasks and detailed descriptions, and that its combination with the data warehousing best practices is useful to the project IA-SI and potentially to other data mining projects.A big data tem impulsionado o desenvolvimento e a pesquisa da ciência de dados nos últimos anos. No entanto, há um problema - a maioria dos projetos de ciência de dados não chega à produção. Isto pode acontecer porque muitos deles não usam uma metodologia de ciência de dados de referência. Outro elemento agravador são os próprios dados, a sua qualidade e o seu processamento. O problema pode ser mitigado através da documentação de estudos de caso, pesquisas e desenvolvimento da área, nomeadamente o reaproveitamento de conhecimento de outros campos maduros que exploram questões semelhantes, como data warehousing. Para resolver o problema, esta dissertação realiza um estudo de caso sobre o projeto “IA-SI - Inteligência Artificial na Gestão de Incentivos”, que visa melhorar a gestão dos fundos europeus de investimento através de data mining. As principais contribuições deste estudo, para a academia e para o desenvolvimento e sucesso do projeto são: (1) Um modelo de processo combinado dos modelos de processo de data mining mais usados e as suas tarefas, ampliado com os subsistemas de ETL e outras recomendadas práticas de data warehousing selecionadas. (2) Aplicação deste modelo de processo combinado ao projeto e toda a sua documentação. (3) Contribuição para a implementação do protótipo do projeto, relativamente a tarefas de compreensão e preparação de dados. Este estudo conclui que CRISP-DM ainda é uma referência, pois inclui todas as tarefas dos outros modelos de processos de data mining e descrições detalhadas e que a sua combinação com as melhores práticas de data warehousing é útil para o projeto IA-SI e potencialmente para outros projetos de data mining

The Examination of Using Business Intelligence Systems by Enterprises in Hungary

Abstract—Data are one of the key elements in corporate decision-making, without them, the decision-making process cannot be imagined. As a consequence, different analytical tools are needed that allow the efficient use of data, information and knowledge. These analytical tools are commonly called Business Intelligence systems that are introduced into the opeartion of enterprises to make access to business data easier, faster and broader in line with the needs of a given enterprise. Based on the findings of an empirical survey, this paper aims to give a deeper insight of the causes and purposes of using BI systems by Hungarian enterprises. It is revealed that such systems are mostly used for risk analysis, financial analysis, market analysis and controlling while their potential to make predictions is usually overlooked. One important conclusion of the paper is that the faster spread of BI systems would be facilitated by reducing costs, simpler parameter settings and a higher level of data protection. Keywords—Business Intelligence; Hungary; Enterprises I