A Business Intelligence Approach to Support Decision Making in Service Evolution Management

2012 ◽  
Author(s):  
Ernando Silva ◽  
Bruno Vollino ◽  
Karin Becker ◽  
Renata Galante
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Service Evolution ◽  
Intelligence Approach ◽  
Support Decision Making

Data Warehouse Performance

2011 ◽  
pp. 580-585 ◽  
Author(s):  
Beixin ("Betsy") Lin ◽  
Yu Hong ◽  
Zu-Hsu Lee
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Performance Tuning ◽  
Data Warehouses ◽  
User Query ◽  
Data Volume ◽  
Predictive Data Mining ◽  
Support Decision Making ◽  
Over Time

A data warehouse is a large electronic repository of information that is generated and updated in a structured manner by an enterprise over time to aid business intelligence and to support decision making. Data stored in a data warehouse is non-volatile and time variant and is organized by subjects in a manner to support decision making (Inmon et al., 2001). Data warehousing has been increasingly adopted by enterprises as the backbone technology for business intelligence reporting and query performance has become the key to the successful implementation of data warehouses. According to a survey of 358 businesses on reporting and end-user query tools, conducted by Appfluent Technology, data warehouse performance significantly affects the Return on Investment (ROI) on Business Intelligence (BI) systems and directly impacts the bottom line of the systems (Appfluent Technology, 2002). Even though in some circumstances it is very difficult to measure the benefits of BI projects in terms of ROI or dollar figures, management teams are still eager to have a “single version of the truth,” better information for strategic and tactical decision making, and more efficient business processes by using BI solutions (Eckerson, 2003). Dramatic increases in data volumes over time and the mixed quality of data can adversely affect the performance of a data warehouse. Some data may become outdated over time and can be mixed with data that are still valid for decision making. In addition, data are often collected to meet potential requirements, but may never be used. Data warehouses also contain external data (e.g. demographic, psychographic, etc.) to support a variety of predictive data mining activities. All these factors contribute to the massive growth of data volume. As a result, even a simple query may become burdensome to process and cause overflowing system indices (Inmon et al., 1998). Thus, exploring the techniques of performance tuning becomes an important subject in data warehouse management.

Analysis of Business Intelligence Solutions in the SME Sectors

2015 ◽  
Vol 795 ◽  
pp. 123-128
Author(s):  
Leszek Kiełtyka ◽  
Klaudia Smoląg
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Complex Analysis ◽  
Small And Medium Enterprises ◽  
Decision Makers ◽  
Current Events ◽  
Decision Making Processes ◽  
Barriers To Implementation ◽  
Medium Enterprises ◽  
Support Decision Making

Business intelligence (BI) solutions are aimed to help managers make decisions in enterprises. Through complex analysis, decision-makers are supported in building strategies of operation. Managers in small and medium-sized enterprises (SME) are also becoming more aware of the fact that conventional methodology of analysis of current events is insufficient. Therefore, the need arises for using the solutions that support the processes of data analysis, finding relationships between each other or pointing to important tendencies and anomalies. These systems were primarily oriented at larger enterprises. However, BI solutions are more and more often adjusted to SME enterprises, offering a complex tool to support decision-making processes. This paper presents key stages in evolution of BI systems and characterizes selected BI systems dedicated to small and medium enterprises (SMEs). Substantial barriers to implementation of BI systems in SMEs were also indicated.

Visual stories in business intelligence to support decision making

2017 ◽  
Author(s):  
Andres Gutierrez ◽  
Cynthia B. Perez ◽  
Luis A. Castro ◽  
Francisco Chavez ◽  
Francisco Fernandez de Vega
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Support Decision Making

Relating Business Intelligence and Enterprise Architecture — A Method for Combining Operational Data with Architectural Metadata

2016 ◽  
Vol 25 (02) ◽  
pp. 1650007 ◽  
Author(s):  
R. K. M. Veneberg ◽  
M.-E. Iacob ◽  
M. J. van Sinderen ◽  
L. Bodenstaff
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Data Model ◽  
Enterprise Architecture ◽  
Business Analytics ◽  
Raw Data ◽  
Architecture Model ◽  
Model Based ◽  
Operational Data ◽  
Support Decision Making

Combining enterprise architecture and operational data is complex (especially when considering the actual ‘matching’ of data with enterprise architecture elements), and little has been written on how to do this. In this paper we aim to fill this gap, and propose a method to combine operational data with enterprise architecture to better support decision-making. Using such a method may result in either an enriched enterprise architecture model (which is very suitable as basis for model-based architecture analyses) or a warehouse data model where operational data is enriched with enterprise architecture metadata (which leads to more traceability by easing the retrieval and interpretation of raw data and of business analytics results). The method is illustrated by means of a case and evaluated by experts. Also, a model for mapping enterprise architecture, operational data, and time is proposed, which allows the model-based execution of new types of analyses.

scholarly journals Barriers of using Business Intelligence in enterprises

2019 ◽  
Vol 51 (2) ◽  
pp. 71-80
Author(s):  
Kamil Soszka
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Business Management ◽  
Decision Making Processes ◽  
Purpose Of Business ◽  
The Right ◽  
Support Decision Making ◽  
The Way

The purpose of Business Intelligence (BI) systems is to support decision-making processes, which is to improve business management. Achieving this goal boils down to obtaining the right information, which is used by the right people and in the right way. The said process is related to the method of using BI and the elements that affect it. However, on the way to a certain level of efficiency when it comes to the use of BI, there are obstacles that inhibit or prevent its achievement. The aim of the work is to identify barriers that reduce the effectiveness of BI use in enterprises.

scholarly journals GERANDO INTELIGÊNCIA ATRAVÉS DE MICRODADOS: UMA PROPOSTA DE BUSINESS INTELLIGENCE PARA A ÁREA DE ENSINO DO INSTITUTO FEDERAL DA BAHIA (IFBA)

2020 ◽  
Vol 13 (4) ◽  
pp. 463
Author(s):  
Iara Margarida de Souza Barreto ◽  
Allan Edgard Silva Freitas
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Academic Life ◽  
Educational Institutions ◽  
Strategic Decisions ◽  
Business Intelligence Systems ◽  
Technology Market ◽  
Educational Indicators ◽  
Support Decision Making

Educational indicators are instruments of significant importance for assessing the outcomes and quality of educational institutions. In order to provide information for strategic decisions, Business Intelligence systems are gaining more and more space in the Information Technology market. This article intends to identify the main educational indicators and their various approaches, in order to evaluate their contributions and/or inadequacies to the pedagogical management of the Instituto Federal da Bahia, besides addressing the Business Intelligence theme, identifying their origin, concepts and applicability. In conclusion, it was presented a proposal to develop a BI system to generate intelligence through microdata extracted from IFBA's academic and administrative systems and, finally, to produce strategic indicators to support decision making regarding students' academic life.

Business Intelligence Enterprise Solution at Abu Dhabi Finance

2013 ◽  
pp. 310-323
Author(s):  
Salam Abdallah
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Strategic Planning ◽  
Business Intelligence ◽  
Abu Dhabi ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Knowledge Based ◽  
Transaction Processing Systems ◽  
Support Decision Making

The challenge of transforming data and information in enterprise information systems into knowledge that can be rolled up and presented to management as key performance indicators is business-critical. The implementation of a business intelligence layer on top of the transaction processing systems and management information systems is viewed as an opportunity to move up a level to promote knowledge-based decision-making and strategic planning. This chapter attempts to examine the issues and challenges associated with the initiative by Abu Dhabi Finance to implement business intelligence solutions that extract information from the enterprise information systems, present them as KPIs for senior management, and produce knowledge that can be used to support decision-making and strategic planning.

Identification of User Preference for Multi-Criteria Reporting in Business Intelligence

2018 ◽  
pp. 16-47
Author(s):  
Martin Aruldoss ◽  
Miranda Lakshmi Travis ◽  
Prasanna Venkatesan Venkatasamy
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
User Preference ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Order Preference ◽  
Banking Business ◽  
Different Levels ◽  
Support Decision Making

Business intelligence (BI) is an integrated set of tools used to support the transformation of data into information in order to support decision making. Among different functionalities, reporting plays a significant role that provides information to its readers to make better decisions. BI lacks user-specific reporting to the different levels of users of an organization. Different users require different kinds of reporting with respect to different requirement (criteria) in an organization. A multi-criteria reporting (MCR) finds the suitable information to suitable user based on the multiple conflicting preferences of a user. Technique for order preference by similarity to ideal solution (TOPSIS) is the most popularly applied multi-criteria decision-making (MCDM) technique selected to identify different levels of user preference for MCR. Banking business is considered as a case study to identify user preference for MCR. This research also designs evaluation metrics for TOPSIS.

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