The transition from social web (web 2.0) to the semantic web (web 3.0) opens new doors in terms ofsemantic data integration, one of the consequences being the appear- ance of a new type of BI solutions called SemanticBusinessIntelligence (SBI). This research aims to respond to the question „What is the difference between SemanticBusinessIntelligence and traditional solu- tions ofBusinessIntelligence?”; moreover, undertakes to demonstrate that SBI is not a fancy term or a marketing strategy but a newgenerationofBusinessIntelligence solutions. The importance of this research approach is justified by the fact that all the Internet users begin to be involved, consciously or not, in phenomenon ofsemantic integration, a phe- nomenon that will be felt in all applications. 2 Research Methodology
contribute significantly to the increase of profitability, productivity and company positioning in the market. In a company with a BI department, the access to data analysis is more efficient and accurate than in a company without it (Foster et al., 2015). A BI department typically has access to all the information about the company. Tintas Robbialac, SA has a BI department, which is assigned to the Commercial area. It interacts with different areas: Marketing, through marketing campaigns and new product releases; Production (logistics), through monthly forecasts and campaigns forecasts; and Financial, through sales and customers forecasts. To compile all the data, it is necessary to have tools and software that allow creating reports or analyses on time. The department uses software from two well-known vendors.
The term Web 2.0 refers to a second generationof web development and design that encourages creativity, information sharing and collaboration among users (Wikipedia 2009). This concept gave rise to the development of communities based on the Web and the evolution of supplied services, such as sites for social networking, video- sharing, wikis, blogs and folksonomy. Although Web 2.0 suggests a new version of the World Wide Web, there was not an evolution in technical specifications but only alterations in the way in which: i) the software is developed and ii) final users utilize the Web. Some technology specialists argue that the term does not make sense, as Web 2.0 just takes advantage of the technological developments and components that already exist, being just another buzzword (Brodkin 2007).
These solutions enable the intelligent usage and interpretation ofbusiness information. With their help, the users can better control the business practices and processes and ana- lyze the performance indicators. Decisions are more efficient if the information about the business environment and competitors are analyzed with such applications, which pro- vide capabilities of extrapolation and making correct forecasts concerning the future eco- nomic trends and conditions. They offer companies and users on all the hierarchical levels a solid, complete and powerful tech- nology for extracting, from a large volume of data, the key information, relevant and useful for decision process and business control. Knowing all the aspects of the business and understanding both the factors having a posi- tive influence and, especially, those having a negative impact, represents an important re- quirement for performance improvement and company’s increase on the market. By using the new information technologies, the com- panies learn what has happened in their busi- ness, why it has happened and what will hap- pen. All these things, together with the users’ experience and intuition, lead to gaining
Drill down and up are utilized to deal with hierarchical levels of a dimension, where drill down increases detail levels and drill up decreases them. Slice and dice, however, are utilized to perform the navigation of data. By slicing the cube the same visualization perspective is maintained. It resembles the use of filters to visualize only specific data under the same previous layout. And by dicing, only one part of the cube can be visualized, which means there is a change of visual perspective. This last operation lays data in a different angle, in addition to enabling the removal or inclusion ofnew data for visualization (MACHADO, 2000).
The project was created as a result of an invitation made to Unipartner by a customer in the restaurant industry, which, faced with a technological consolidation of the group, gained access to a considerable amount of information relevant to the business. Challenged to include these new data in its reporting and decision-making models, it was recognized the need to create new standardization, treatment and analysis processes that allow more complete information on customers and business to support decision making. The solution to be implemented must support several business analyzes carried out in the areas of purchasing, loyalty, sales, accounting, human resources and quality. It is important that the final solution provides a consolidated and structured view of the data residing in various data sources that the customer has.
• Campaign Management – A mail-order retailer wanted to improve the effectiveness of its direct mail marketing campaigns, with the goals of reducing costs and increasing the percent of positive responses. The retailer knew that it is too costly to send direct mail to all of its customers. Using a neural network model, they analyzed all of the factors that affect their customer’s propensity to respond. The model included many variables, such as past purchase history, purchase frequency, customer age, gender, marital status, location, etc. and it was trained on a number of historical mailing campaigns. The model was then applied to the full list of customers and the probability of them responding to the campaign was predicted. Customers marked as most likely to respond were targeted in the new campaign.
Another challenge is multiple classii cations when the customer belongs to more than one category. h ere is the case of web mining.h e Internet is get- ting the primate as a new channel for the goods dis- tribution, product promotion, transaction manage- ment and coordination ofbusiness processes and it becomes a valuable and suitable source of data about customers. More information on management can be seen in (Draker, 2003). However, multiple formats of data and distributive nature of knowledge on the Web are the challenge for collecting, revealing, organizing and knowledge management in the way that is suitable for providing support to business decision making. More information on decision support system can be seen in (Kotsiantis, 2011).
3 has kept below 60%. Despite that, we can easily deduct that, if we include all the startups that failed to raise money from investors, that percentage would be a lot higher. Why is that? In a world with accessible data, why do most startups keep failing? Is that because they’re not data driven? Maybe, is it because they’re not using all the BusinessIntelligence & Analytics (BI&A) tools available? Or even, is it because the current BI&A tools are not well designed for startups usage? Or is it simply because they’ve not achieved the level of BI&A maturity of larger companies, and their business dies before data can make its impact? Finally, can we extrapolate these numbers and conclusions to the Portuguese economy and ecosystem? What is been doing in the Lisbon ecosystem to enter this new data-economy and achieve success?
Organizations hold large amounts of data, this information is related directly with the area(s) ofbusiness activity or only with the documents management. The ability to extract, transform and load the data in order to identify business opportunities is a process known as BusinessIntelligence. Information by itself has little significance, but the ability to extract related information, and relevant to the business may be crucial to the survival of the organization. Related information and easily identified business aspects have great importance, but the BI goes further, identifying patterns, sometimes quite surprising they have resulted in newbusiness opportunities bringing of directly or indirectly, profits to the organization. The development of BI applications can be done with various tools available in the market since paid tools, free for commercial and open source use. This paper presents various tools free and/or open source that provide several features to develop BI solutions without technological costs, features that offer will be listed and a comparative analysis of them will be made.
With the emergent volume of student data handled by HEAC in fast system structure changing environment to each schema based on the yearly requirements. However, the emergence of the project idea came from the difficulties and obstacles experienced by database management specialists during report generation required by other departments and staff; such reports are needed for decision making on issues related to students.
Before circulating the survey to the appropriate survey group, there were some adjustments made. Since the questionnaire topic is quite specific, the goal was to make the specific questions as understandable as possible. Further on, the efforts were directed into making the questionnaire shorter and easier to be fulfilled, but still retain its all important parts. Finally I had a meeting with two experts from the field of BI that come from two different companies. The first meeting took place at the company, that is BI system provider and the second one took place at the company who uses, and is therefore a customer of such system. These two meetings were important, because at this point, together with the BI experts, we thoroughly looked at each individual pre-cleaned question and made some new adjustments. Finally the questionnaire was ready enough to test it. In order to perform the testing phase, I distributed the 6 questionnaires by e-mail. Three of which were distributed to the organization which is BI system provider and three to the organization who is BI system user or customer. All of these six questionnaires were validly completed and sent back. The results of these questionnaires were satisfactory and there were no issues when completing the questionnaire reported. Since these six questionnaires were only for purpose of testing the responses were excluded from the main research.
É inegável a importância do presente estudo, pois a aplicabilidade das múltiplas técnicas e métodos de BusinessIntelligence a uma rede prosopográfica, revela-nos informação histórica sobre eventos biográficos, relacionais e de parentesco entre diversos protagonistas históricos (Ferreira, Caldeira, e Olival, 2012). É objetivo central, que este projeto possa ser uma componente útil e relevante para o CIDEHUS, que proporcione inclusive aos seus utilizadores a descoberta de padrões de dados insólitos, oferecendo em suma maior flexibilidade na exploração e análise dos dados. O desenvolvimento de uma aplicação de análise de dados de tipo OLAP, vai contribuir de modo significativo para o desenvolvimento do projeto. Entre outras componentes do objeto sobre o qual versa o estudo, podemos referir a criação de data marts sobre ocupação, permitindo descobertas de novos padrões de dados relacionados com a ocupação.
However, apart from defining the context ofbusiness facts, dimensions may be perceived as collections of properties along which various analyses of numerical data may be performed. As direct consequence of the hierarchical structure of dimensions, measures may be aggregated above their initial granularity, at a lower or higher level, depending on the actual business needs, in order to calculate various indicators. For example, calculation of the full amount of examination fees corresponding to a specific day, month, semester etc. or, summarizing marks of students in the form of general averages per student, per year, or per faculty. In fact, in order to improve query performance, multidimensional applications need to pre-compute some of the summary data and explicitly store such aggregates.
As empresas necessitam de captar informação e transforma-la em conhecimento, pelo que o modelo de negócio utilizado é essencial à sustentabilidade financeira desta, permitindo a execução das tarefas operacionais, optimização de estratégias, planeamento e uso de ferramentas BI. É com o apoio das tecnologias informáticas que podem aumentar e desenvolver o negócio nos mercados tendo sempre como foco o cliente. O “valor do negócio” perde-se quando o investimento em TI é puramente económico, e é aqui que se deve focar o Stakeholder e ou a equipa responsável pelo investimento em recursos humanos e financeiros. O cliente deve ser o objectivo principal, a angariar e a perdurar para empresa, a fidelização deve ser realizada pela prática de uma cultura organizacional focada nas suas necessidades, sendo essencial o seu envolvimento económico e social. A BusinessIntelligence quando bem implementado, permite a redução de custos para a empresa e para os seus clientes, pela aquisição de produtos de qualidade superior a preços mais baixos, assim como o efectuar de previsões acerca de tendências de mercado, a curto, médio e longo prazo, não esquecendo que é necessário incorporar/interiorizar os processos de negócio no seio da organização (Chesbrough, 2002; Rosenbloom, 2002).
O presente projeto tem como objetivo desenvolver uma solução de BusinessIntelligence de suporte ao PO SEUR - Programa Operacional Sustentabilidade e Eficiência no Uso de Recursos, doravante designada de BISEUR, dada a necessidade por parte da gestão do PO SEUR em prestar informação, quer internamente, à Comissão Diretiva e aos secretários técnicos, quer a entidades externas. No PO SEUR, a falta de informação disponível, viável e atempada, muitas vezes provocada pelo grande volume de dados gerados diariamente, dificultam o processo de análise e de tomada de decisão. O âmbito deste trabalho será identificar a arquitetura a ser adotada pelo sistema a implementar, especificar o modelo de dados para o Data Warehouse e definir o processo de ETL, bem como, identificar um conjunto de indicadores de desempenho a considerar. Os resultados obtidos permitirão criar um sistema capaz de captar informações relevantes para o processo de decisão de forma rápida e eficiente.
A importância dos metadados para um sistema de BusinessIntelligence, é crucial para a compreensão e controlo sobre os dados durante os vários processos que sofrem no sistema. De acordo com Sá (2009, pg. 19), os metadados “constituem informação sobre os registos informacionais armazenados no Data Warehouse e nos Data Marts, identificando a origem de cada registo informacional, o processo de transformação e limpeza que sofreu, e o seu significado”. Os autores Turban et al. (2017, pg. 38) e Kimball & Ross (2013), afirmam que, os metadados são “dados sobre os dados” (discordado por Loshin 2012, pg. 120, por não transpor a total essência dos metadados), estes, descrevem a estrutura e algum significado sobre os dados, contribuindo assim para o seu uso efetivo ou ineficaz.
paper presents a BusinessIntelligence (BI) system built to allow the study of nosocomial infection incidence in the Medicine Units of Centro Hospi- talar do Porto (CHP), a hospital centre in the north of Portugal. This BI platform is responsible for presenting nosocomial infection indicators. This platform enables to query important information and to analyze it, supporting healthcare professionals in their decisions. The knowledge obtained with this analysis allows preventing, monitoring and reducing nosocomial infections. So, the system acts as a Clinical Decision Support System (CDSS) capable of increasing patient's safety and well-being. The platform developed shows that, for example, in 2013 the rate of nosocomial infection in CHP Medicine Units varied between 9.43% and 12.95% and the respiratory and the urinary tract infections were the most frequent nosocomial infections. This work and the platform developed demonstrate that BI technology can be applied to health- care with great utility and success.
O conceito atual de BusinessIntelligence começou na década de 70, quando foram disponibilizados no mercado alguns produtos de BusinessIntelligence para a análise de negócios, passando a estar presente nas organizações através de diferentes sistemas. Com a evolução das tecnologias e sistemas de informação e as consequentes mudanças nas organizações surgiu, na década de 80, o conceito Executive Information Systems (EIS). Este conceito corresponde a uma tecnologia computorizada que permite ajudar os executivos na tomada de decisão estratégica, fornecendo o acesso fácil e rápido a dados relevantes, necessários para atingir os objetivos estratégicos de uma organização, como o controlo e acompanhamento da mesma (Turban, Sharda, Aronson, & King, 2009).
A primeira proposta oficial do termo BusinessIntelligence foi em 1989, por Howard Dresner, analista da Gartner Group, que descreveu como "conceitos e métodos para melhorar a tomada de decisões empresariais suportado em sistemas de apoio" (Power, 2007). Mais recentemente aponta-se como um conjunto de arquiteturas, ferramentas, bases de dados, aplicações e metodologias, com o objetivo de possibilitar um acesso interativo aos dados e manipulação destes, dando aos gestores e analistas a possibilidade de realizar análises adequadas, (Raisinghani, 2003). No entanto a referência vem desde as primeiras tecnologias de informação em 1958 por Luhn, citando “a capacidade de apreender as inter-relações dos factos apresentados de forma a orientar as ações em direção a um objetivo” (Luhn, 1958).