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.

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.

scholarly journals Business Intelligence para uma análise da qualidade da entrega dos objetos postais

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Jean Barros Teixeira ◽  
Maílson Melo dos Santos Filho ◽  
Carlos André Duarte Costa
Keyword(s):  
Open Source ◽  
Business Intelligence ◽  
Data Mart

Atualmente as organizações necessitam de sistemas que forneçam suporte a tomada de decisão através de análises do grande volume de dados oriundos de seus sistemas transacionais. É o caso dos Correios que carece de uma solução que possa trazer vantagem competitiva no mercado em que atua. Dessa forma o objetivo desse trabalho é propor e implementar uma solução de Business Intelligence que irá auxiliar os gestores na análise da qualidade dos objetos postais e em uma tomada de decisão mais assertiva. Para isso foi utilizada a ferramenta Open Source Pentaho e técnicas que permitiram a criação de um Data Mart, realização de consultas analíticas e o desenvolvimento de um dashboard.

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.

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