scholarly journals Managing the Process of Evaluation of the Academic Teachers with the Use of Data Mart and Business Intelligence

2021 ◽  
Vol XXIV (Special Issue 2) ◽  
pp. 127-140
Author(s):  
Piotr Muryjas ◽  
Monika Wawer ◽  
Magdalena Rzemieniak
Keyword(s):  
Business Intelligence ◽  
Data Mart ◽  
Use Of Data ◽  
Academic Teachers

scholarly journals O uso de Data Mart para apoio à tomada de decisão na Gestão de Pessoas em uma Instituição Federal de Ensino

2021 ◽  
Author(s):  
Edivaldo da Silva Souza ◽  
Luiz Antônio Abrantes ◽  
Jugurta Lisboa-Filho
Keyword(s):  
Business Intelligence ◽  
Data Mart

Business Intelligence (BI) é composto por um banco de dados multidimensional, orientado por assunto, não volátil, histórico, decisório e variável em relação ao tempo. Ao aplicar o uso do Data Mart para uma Instituição Federal de Ensino no setor de gestão de pessoas, essa pesquisa trabalhou com os dados para desenvolver indicadores para a tomada de decisão. Questões como falta de sinergia entre as bases de dados existentes, qualidade dos dados fornecidos e impossibilidade da emissão de relatórios gerenciais em tempo hábil, foram tratados nesse estudo. Concluiu-se que a aplicação e implantação de BI através de Data Mart pode gerar dados precisos, solucionar os problemas e fornecer indicadores de desempenho.

Data Profiling and Data Quality Metric Measurement as a Proactive Input into the Operation of Business Intelligence Systems

2014 ◽  
pp. 253-270
Author(s):  
Scott Delaney
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Business Intelligence ◽  
Intelligence System ◽  
Use Of Data ◽  
Data Profiling ◽  
Business Intelligence Systems ◽  
Profiling Techniques ◽  
System Data ◽  
Taking Action

Business intelligence systems have reached business critical status within many companies. It is not uncommon for such systems to be central to the decision-making effectiveness of these enterprises. However, the processes used to load data into these systems often do not exhibit a level of robustness in line with their criticality to the organisation. The processes of loading business intelligence systems with data are subject to compromised execution, delays, or failures as a result of changes in the source system data. These ETL processes are not designed to recognise nor deal with such shifts in data shape. This chapter proposes the use of data profiling techniques as a means of early discovery of issues and changes within the source system data and examines how this knowledge can be applied to guard against reductions in the decision making capability and effectiveness of an organisation caused by interruptions to business intelligence system availability or compromised data quality. It does so by examining issues such as where profiling can be best be applied to get appropriate benefit and value, the techniques of establishing profiling, and the types of actions that may be taken once the results of profiling are available. The chapter describes components able to be drawn together to provide a system of control that can be applied around a business intelligence system to enhance the quality of organisational decision making through monitoring the characteristics of arriving data and taking action when values are materially different than those expected.

Data Profiling and Data Quality Metric Measurement as a Proactive Input into the Operation of Business Intelligence Systems

2016 ◽  
pp. 2171-2188
Author(s):  
Scott Delaney
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Business Intelligence ◽  
Intelligence System ◽  
Use Of Data ◽  
Data Profiling ◽  
Business Intelligence Systems ◽  
Profiling Techniques ◽  
System Data ◽  
Taking Action

Business intelligence systems have reached business critical status within many companies. It is not uncommon for such systems to be central to the decision-making effectiveness of these enterprises. However, the processes used to load data into these systems often do not exhibit a level of robustness in line with their criticality to the organisation. The processes of loading business intelligence systems with data are subject to compromised execution, delays, or failures as a result of changes in the source system data. These ETL processes are not designed to recognise nor deal with such shifts in data shape. This chapter proposes the use of data profiling techniques as a means of early discovery of issues and changes within the source system data and examines how this knowledge can be applied to guard against reductions in the decision making capability and effectiveness of an organisation caused by interruptions to business intelligence system availability or compromised data quality. It does so by examining issues such as where profiling can be best be applied to get appropriate benefit and value, the techniques of establishing profiling, and the types of actions that may be taken once the results of profiling are available. The chapter describes components able to be drawn together to provide a system of control that can be applied around a business intelligence system to enhance the quality of organisational decision making through monitoring the characteristics of arriving data and taking action when values are materially different than those expected.

Big Data, Data Management, and Business Intelligence

2021 ◽  
pp. 509-519
Author(s):  
Richard T. Herschel
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Management ◽  
Business Intelligence ◽  
Personal Information ◽  
Poor Quality ◽  
Management Support ◽  
Use Of Data ◽  
Master Data Management ◽  
Iot Devices

This paper examines big data and the opportunities it presents for improved business intelligence and decision making. Big data comes in multiple forms. It can be structured, semi-structured, or unstructured. The opportunity it presents is that there is so much of it and it is readily available to organizations. Organizations use big data for business intelligence (BI). They can apply analytics in BI activities to assess big data in order to gain new insights and opportunities for decision making. The problem is that oftentimes the data is of poor quality and it contains personal information. This paper explores these issues and examines the importance of effective data management in facilitating sound business intelligence. The Master Data Management methodology is reviewed and the importance of management support in its deployment is emphasized. With the advent of new sources of big data from IoT devices, the need for even more management involvement is stressed to ensure that organizational BI yield sound decisions and that use of data are in compliance with new regulations.

Adequação do processo de desenvolvimento de Data Warehouse para empresa varejista que utiliza software Cots

2014 ◽  
Vol 5 (2) ◽  
pp. 156-171
Author(s):  
Cleisson Fabricio Leite Batista ◽  
Mario Godoy Neto ◽  
Ellen Polliana Ramos Souza
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Data Mart ◽  
Commercial Off The Shelf

A demanda por informação é cada vez mais frequente em pequenas, médias e grandes empresas, que precisam tomar decisões de forma rápida para manterem-se competitivas. Visando atender não somente a demanda de mercado, mas suprir a carência de muitas organizações no que diz respeito à transformação de dados em informação, surgiram as soluções de Business Intelligence (BI) baseadas em dados, tais como Data Warehouse (DW) e Data Mart (DM). O desenvolvimento destas soluções de BI, entretanto, está ainda muito longe da realidade da maioria das empresas brasileiras, em especial daquelas de médio e pequeno porte que, em geral, utilizam software de prateleira ou Commercial off-the-shelf (COTS). Os processos de construção de DW/DM são direcionados para software desenvolvidos sob encomenda, que contam com a participação efetiva dos analistas dos sistemas transacionais, projetistas e administradores de Banco de Dados, não contemplando as especificidades do processo de desenvolvimento de um DW/DM para Pequenas e Médias Empresas (PME) que fazem uso de software COTS. Neste sentido, este artigo relata as oportunidades e desafios enfrentados em um estudo de caso onde foi realizada a construção de Data Warehouse, para uma empresa varejista de médio porte que utiliza COTS na operacionalização dos seus processos de negócio.

scholarly journals MEMAHAMI KINERJA STARTUP : STUDI PADA STARTUP DI JAWA BARAT

AdBispreneur ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 297
Author(s):  
Sunu Puguh Hayu Triono ◽  
Adryan Rachman
Keyword(s):  
Absorptive Capacity ◽  
Business Intelligence ◽  
Entrepreneurial Orientation ◽  
Business Performance ◽  
Dynamic Capabilities ◽  
Positive Association ◽  
Data Utilization ◽  
Innovation Activities ◽  
Use Of Data ◽  
The Relationship

One characteristics of startups compared to conventional MSMEs is the use of data. Utilization of data starts from business intelligence & analytics. The knowledge generated together with other dynamic capabilities will support each other in creating value to improve startup business performance. The purpose of this study was to determine the relationship between business intelligence & analytics, absorptive capacity, innovation ambiance, and entrepreneurial orientation towards startup business performance.Data obtained by survey distributed by email to 194 startups in West Java. PLS-SEM was used to estimate model in this study. We propose and test a model that integrates the domains of dynamic capability, knowledge management, and entrepreneurship. The main findings show that the use of BI&A has a positive association with the ability to balance competitive innovation activities, supported by entrepreneurial orientation, in turn improving startup performance.This study integrates insights gained from the application of IT value creation and dynamic capabilities perspectives to explain how the use of BI&A is associated with the ambidexterity of innovation and business performance and through entrepreneurial orientation. Salah satu ciri startup dibandingkan UMKM konvensional adalah penggunaan data. Pemanfaatan data dimulai dari business intelligence & analytics. Pengetahuan yang dihasilkan bersama dengan kapabilitas dinamis lainnya akan saling mendukung dalam menciptakan nilai guna meningkatkan kinerja bisnis startup. Tujuan dari penelitian ini adalah untuk mengetahui hubungan antara business intelligence & analytics, absorptive capacity, innovation ambidexterity, dan entrepreneurial orientation terhadap kinerja bisnis startup. Data didapatkan dari survei yang disebarkan melalui email kepada 194 startup di Jawa Barat. PLS-SEM digunakan untuk mengestimasi model dalam penelitian ini. Kami menguji model yang mengintegrasikan domain dynamic capabilities, manajemen pengetahuan, dan kewirausahaan. Temuan utama menunjukkan bahwa penggunaan BI&A memiliki hubungan positif dengan kemampuan mengimbangi aktivitas inovasi kompetitif, didukung oleh orientasi kewirausahaan, yang pada gilirannya meningkatkan kinerja startup. Studi ini mengintegrasikan wawasan yang diperoleh dari penerapan perspektif penciptaan nilai TI dan kapabilitas dinamis untuk menjelaskan bagaimana penggunaan BI&A dikaitkan dengan innovation ambidexterity dan kinerja bisnis dan melalui orientasi kewirausahaan. 

scholarly journals Web Mining for the Integration of Data Mining with Business Intelligence in Web-Based Decision Support Systems

2015 ◽  
pp. 120-142 ◽  
Author(s):  
Marcos Aurélio Domingues ◽  
Alípio Mário Jorge ◽  
Carlos Soares ◽  
Solange Oliveira Rezende
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Web Mining ◽  
Support Systems ◽  
Web Documents ◽  
Web Based ◽  
Use Of Data ◽  
Computer Based

Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information system that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites.

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