Building a Data Warehouse step by step

Data warehouses have been developed to answer the increasing demands of quality information required by the top managers and economic analysts of organizations. Their importance in now a day business area is unanimous recognized, being the foundation for developing business intelligence systems. Data warehouses offer support for decision-making process, allowing complex analyses which cannot be properly achieved from operational systems. This paper presents the ways in which a data warehouse may be developed and the stages of building it.data warehouse, data mart, data integration, database management system, OLAP, data mining

Modelling and Simulation of a Decision Support System Prototype Built on an Improved Data Warehousing Architecture for the School of Postgraduate, MAUTECH, Yola – Nigeria

A Data Warehouse (DW) is constructed with the goal of storing and providing all the relevant information that is generated along the heterogeneous databases of an organization. The development and management of precise and up-to-date information concerning academic staff, department, faculty, student’s academic record etc. is critically important in the management of a university. This study has become necessary because, data warehousing is a new field, a small number of investigations has been done regarding the features of academic data analysis and report. At present, data warehousing is among the best solution for gathering and maintaining data for decision making.  Therefore, the aim of this paper is to develop a DW prototype model for the School of Postgraduate Studies’ (SPGS) programmes of Modibbo Adama University of Technology (MAUTEC), Yola. The objective of the study is to model and simulate a decision support system that is capable of querying the prototype DW database model to generate reports as output in order to help administrative decision making of the SPGS MAUTEC, Yola. The study has provided relevant literatures in relation to the subject matter. In the methodology, a secondary, field and case study research were conducted. The software engineering development methodology considered was the “Realistic Waterfall Model”. The findings of this paper provide a DW prototype database model using a dimensional modeling technique and the graphic user interface tool for reports and analysis. The researchers have demonstrated their understanding on the subject matter and as a matter of fact, possible future work has been suggested from where we stopped. Keywords - Data Warehouse, Modeling, Simulation, Prototype and Decision Support Syste

Design of a Data Warehouse Model for a University Decision Support System

Data Warehouse (DW) can be a valuable asset in providing a stress-free access to data for reporting and analysis. Regrettably, building and preserving an active DW is usually associated with numerous hitches ranging from design to maintenance.  Research in the field of data warehousing has led to the emergence of vital contemporary technologies to aid design, management, and use of information systems that is capable of conveying a Decision Support System (DSS) to organizations. Nevertheless, in the face of persistent achievement and evolution of the field, abundant research is still left unturned across many diverse areas of the data warehousing. The objective of the paper therefore, is to design a DW database model for a University DSS using a dimensional modeling and techniques. A proposed DW database model with specific focus on modeling and design has been realized in this study.  The researchers have demonstrated on how a DW database model can be realized using the dimensional modeling and technique. Keywords: Data Warehouse, Modeling, Decision Support System, Decision Making

Data warehouse automation trick or treat?

Data warehousing systems have been around for 25 years playing a crucial role in collecting data and transforming that data into value, allowing users to make decisions based on informed business facts. It is widely accepted that a data warehouse is a critical component to a data-driven enterprise, and it becomes part of the organisation’s information systems strategy, with a significant impact on the business. However, after 25 years, building a Data Warehouse is still painful, they are too time-consuming, too expensive and too difficult to change after deployment. Data Warehouse Automation appears with the promise to address the limitations of traditional approaches, turning the data warehouse development from a prolonged effort into an agile one, with gains in efficiency and effectiveness in data warehousing processes. So, is Data Warehouse Automation a Trick or Treat? To answer this question, a case study of a data warehousing architecture using a data warehouse automation tool, called WhereScape, was developed. Also, a survey was made to organisations that are using data warehouse automation tools, in order to understand their motivation in the adoption of this kind of tools in their data warehousing systems. Based on the results of the survey and on the case study, automation in the data warehouses building process is necessary to deliver data warehouse systems faster, and a solution to consider when modernize data warehouse architectures as a way to achieve results faster, keeping costs controlled and reduce risk. Data Warehouse Automation definitely may be a Treat.Os sistemas de armazenamento de dados existem há 25 anos, desempenhando um papel crucial na recolha de dados e na transformação desses dados em valor, permitindo que os utilizadores tomem decisões com base em fatos. É amplamente aceite, que um data warehouse é um componente crítico para uma empresa orientada a dados e se torna parte da estratégia de sistemas de informação da organização, com um impacto significativo nos negócios. No entanto, após 25 anos, a construção de um Data Warehouse ainda é uma tarefa penosa, demora muito tempo, é cara e difícil de mudar após a sua conclusão. A automação de Data Warehouse aparece com a promessa de endereçar as limitações das abordagens tradicionais, transformando o desenvolvimento da data warehouse de um esforço prolongado em um esforço ágil, com ganhos de eficiência e eficácia. Será, a automação de Data Warehouse uma doçura ou travessura? Foi desenvolvido um estudo de caso de uma arquitetura de data warehousing usando uma ferramenta de automação, designada WhereScape. Foi também conduzido um questionário a organizações que utilizam ferramentas de automação de data warehouse, para entender sua motivação na adoção deste tipo de ferramentas. Com base nos resultados da pesquisa e no estudo de caso, a automação no processo de construção de data warehouses, é necessária para uma maior agilidade destes sistemas e uma solução a considerar na modernização destas arquiteturas, pois permitem obter resultados mais rapidamente, mantendo os custos controlados e reduzindo o risco. A automação de data warehouse pode bem vir a ser uma “doçura”

Heterogeneous biomedical database integration using a hybrid strategy: a p53 cancer research database.

Complex problems in life science research give rise to multidisciplinary collaboration, and hence, to the need for heterogeneous database integration. The tumor suppressor p53 is mutated in close to 50% of human cancers, and a small drug-like molecule with the ability to restore native function to cancerous p53 mutants is a long-held medical goal of cancer treatment. The Cancer Research DataBase (CRDB) was designed in support of a project to find such small molecules. As a cancer informatics project, the CRDB involved small molecule data, computational docking results, functional assays, and protein structure data. As an example of the hybrid strategy for data integration, it combined the mediation and data warehousing approaches. This paper uses the CRDB to illustrate the hybrid strategy as a viable approach to heterogeneous data integration in biomedicine, and provides a design method for those considering similar systems. More efficient data sharing implies increased productivity, and, hopefully, improved chances of success in cancer research. (Code and database schemas are freely downloadable, http://www.igb.uci.edu/research/research.html.)

Developing a business intelligence initiative in higher education

ACM Classification: H.4.2 Types of Systems – Decision SupportIn nowadays, Business Intelligence (BI) is one of the most important areas for managers and their organizations, whose investments on this type of projects are increasing. The decision-making process has become crucial to be more competitive, and higher education institutions (HEIs) are not an exception. For the last years, HEIs from all over the world have started to apply BI to their educational and decision-making challenges. In 2013, the BI Task Force from EUNIS (European University Information Systems) surveyed several HEIs to understand the maturity of their BI systems. The results revealed inconsistencies, raising the doubt about the comprehension of BI concepts. Considering this survey and its basis on maturity models, this dissertation analyses the existing models regarding higher education. Understanding the difficulties in answering the EUNIS survey from a perspective of two Portuguese universities is also a goal. It was created a feedback survey, whose results revealed it was a positive experience, although the lack of clarification of BI concepts was underlined. Thinking about other universities starting their BI journey, it was developed a kit proposal that clarifies concepts and best practices for this sector. It was validated by the two universities mentioned above, which will be starting their initiative in January 2015. This validation was made through an interview, and the feedback was encouraging. Having a guidance to be methodical in this phase was highlighted, as well as the presentation of real success cases that allow to understand what other institutions do on their daily basis.Atualmente, Business Intelligence (BI) é uma das mais importantes áreas para gestores e empresas, cujo investimento tem vindo a aumentar substancialmente. A tomada de decisão tem-se tornado fundamental para o aumento da competitividade e as instituições do ensino superior não são exceção. Nos últimos anos, instituições de todo o mundo têm começado a aplicar BI nos seus desafios. Em 2013, a BI Task Force da EUNIS (European University Information Systems) decidiu realizar um inquérito a instituições de ensino superior para conhecer a maturidade dos seus sistemas de BI. Os resultados revelaram incoerências, criando a dúvida sobre a correta compreensão dos conceitos. Tendo em conta este inquérito e a sua base em modelos de maturidade de BI, é realizada uma revisão bibliográfica dos modelos existentes direcionados para o ensino superior. Compreender as dificuldades em responder ao inquérito da EUNIS, na perspetiva de duas universidades, também é um objetivo deste estudo. Foi criado um questionário de feedback, cujos resultados revelaram ter sido uma experiência positiva, embora a falta de clarificação dos conceitos fosse sublinhada. Considerando instituições a iniciar a sua aventura em BI, foi criado um guião que clarifica conceitos e boas práticas para o sector. Foi validado pelas universidades mencionadas, que vão começar as suas iniciativas no próximo ano. Essa validação, feita com entrevistas, revelou que um guião que ajude as universidades a serem metódicas nesta fase é essencial, bem como a apresentação de casos reais de sucesso que permitem dar a conhecer o que é feito no dia-a-dia do sector

Review of modern business intelligence and analytics in 2015: How to tame the big data in practice?: Case study - What kind of modern business intelligence and analytics strategy to choose?

The objective of this study was to find out the state of art architecture of modern business intelligence and analytics. Furthermore the status quo of business intelligence and analytics' architecture in an anonymous case company was examined. Based on these findings a future strategy was designed to guide the case company towards a better business intelligence and analytics environment. This objective was selected due to an increasing interest on big data topic. Thus the understanding on how to move on from traditional business intelligence practices to modern ones and what are the available options were seen as the key questions to be solved in order to gain competitive advantage for any company in near future. The study was conducted as a qualitative single-case study. The case study included two parts: an analytics maturity assessment, and an analysis of business intelligence and analytics' architecture. The survey included over 30 questions and was sent to 25 analysts and other individuals who were using a significant time to deal with or read financial reports like for example managers. The architecture analysis was conducted by gathering relevant information on high level. Furthermore a big picture was drawn to illustrate the architecture. The two parts combined were used to construct the actual current maturity level of business intelligence and analytics in the case company. Three theoretical frameworks were used: first framework regarding the architecture, second framework regarding the maturity level and third framework regarding reporting tools. The first higher level framework consisted of the modern data warehouse architecture and Hadoop solution from D'Antoni and Lopez (2014). The second framework included the analytics maturity assessment from the data warehouse institute (2015). Finally the third framework analyzed the advanced analytics tools from Sallam et al. (2015). The findings of this study suggest that modern business intelligence and analytics solution can include both data warehouse and Hadoop components. These two components are not mutually exclusive. Instead Hadoop is actually augmenting data warehouse to another level. This thesis shows how companies can evaluate their current maturity level and design a future strategy by benchmarking their own actions against the state of art solution. To keep up with the fast pace of development, research must be continuous. Therefore in future for example a study regarding a detailed path of implementing Hadoop would be a great addition to this field