Submitted by João Gabriel Saraceni Lima da Silva (joaosaraceni@id.uff.br) on 2018-03-28T14:50:00Z No. of bitstreams: 1 Joao Saraceni Dissertacao Revisado Banca _V04.pdf: 1375797 bytes, checksum: 36d698762a610b673b13e6a26444a31c (MD5) / Approved for entry into archive by Debora Nunes Ferreira (debora.nunes@fgv.br) on 2018-04-02T17:28:16Z (GMT) No. of bitstreams: 1 Joao Saraceni Dissertacao Revisado Banca _V04.pdf: 1375797 bytes, checksum: 36d698762a610b673b13e6a26444a31c (MD5) / Approved for entry into archive by Suzane Guimarães (suzane.guimaraes@fgv.br) on 2018-04-02T17:45:56Z (GMT) No. of bitstreams: 1 Joao Saraceni Dissertacao Revisado Banca _V04.pdf: 1375797 bytes, checksum: 36d698762a610b673b13e6a26444a31c (MD5) / Made available in DSpace on 2018-04-02T17:45:56Z (GMT). No. of bitstreams: 1 Joao Saraceni Dissertacao Revisado Banca _V04.pdf: 1375797 bytes, checksum: 36d698762a610b673b13e6a26444a31c (MD5) Previous issue date: 2018-02-28 / A diversificação das fontes de dados utilizadas em processos decisórios nas organizações é um dos elementos que fundamentam o conceito de big data, apontado como o futuro das aplicações de Inteligência Analítica. O desafio das organizações em trabalhar com dados não estruturados e dados externos torna-se importante para as que desejam evoluir suas iniciativas de Inteligência Analítica. A partir da revisão de literatura e entrevistas com profissionais que atuam neste campo, este trabalho explora quais as principais iniciativas para obtenção de novas fontes de dados em sistemas de informação de Inteligência Analítica. A abordagem metodológica utilizada foi o estudo de caso múltiplo. A pesquisa utilizou como perspectiva de análise a Teoria da Capacidade Absortiva, que oferece elementos para avaliar como a empresa obtém informações externas a ela e as utiliza no contexto organizacional, gerando valor a seu negócio. São discutidos os principais desafios para a diversificação das fontes de dados, que pode se dar em função da complexidade de diferentes tecnologias, fornecedores, integração de dados, entre outros fatores. Os resultados são apresentados explorando os constructos de Aquisição, Assimilação, Transformação e Utilização, presentes na teoria da Capacidade Absortiva, aplicado à diversificação das fontes de dados nas organizações. Foi possível notar que existem diferentes níveis de utilização de dados externos nas organizações, bem como diferentes arranjos organizacionais para consumir dados externos. As diferentes formas de consumir dados externos causam impacto na forma de alocação de investimentos, governança de dados, cultura organizacional, relacionada ao uso do dado e, por fim, na maturidade do uso de dados externos de forma sistêmica na organização. / The variety of data sources applied into decision-making processes in organizations is one of the factors that defines the concept of big data, indicated as the future of Business Intelligence & Analytics applications. Handle with not structured and/or external data diversifying data sources became important to organizations that evolve their BI&A initiatives. From the literature review and interviews with field professionals, this work explore the main initiatives of organizations to obtain new data sources in BI&A information systems. The methodology approach used is multiple case study. The research used the Absorptive Capacity theory as analytical perspective, which offers elements to evaluate how organization obtains external information and use it into organizational context. There are discussed the main challenges to diversifying data sources, such as the complexity of new technologies, suppliers, data integration, among others. The results are presented exploring the constructs Acquisition, Assimilation, Transformation and Explotation, built in Absorptive Capacity theory, applied to the diversification of data sources. It was possible to notice that there are different external and/or not structured data usage levels in organization, as well as different organizational arrangements for consuming external data. The ways of consuming external data have effects on the investment allocation, data governance, organizational culture related to the use of data, and ultimately on the maturity of external and/or not structured data usage in a systematic way in the organization.

This diploma thesis deals with the management of information flow of a small company using Business Intelligence tools and the data mart. There is defined the problem of working with information from the point of view of selected company in the first part. The next part presents selected theoretical background on the basis of which was the aim achieved. The fourth part analyses the current situation of the company. There is recommended complex improvement of the current situation in the practical part. Selected information management problem is accomplished factually. There are also introduced suitable software tools, which were used for the solution.

Businesses today are often operating in a highly competitive environment where information is a noticeably valuable asset. Businesses are therefore in need of powerful tools for extracting actionable business knowledge. Research show that SME companies are lagging behind large companies in the use of data analytics; even though they know the potential benefits. We want to study and compare different tools for data analytics and how they can be used by small companies. Our research questions are therefore: what analytical tools are today available on the market, and what are their possibilities and challenges for small companies? And: how can these analytical tools aid in the development of a business, product or service? We conclude in our research that there are several data analytics tools available for small businesses, that their different usages can be applied successfully and without big cost, and that their relevance, both in business development and innovation, depends on the business objectives and goals of their utilization.

Made available in DSpace on 2016-06-02T19:51:39Z (GMT). No. of bitstreams: 1 2385.pdf: 3560559 bytes, checksum: 71e6c5c7aca71d06463f1bc0bc063005 (MD5) Previous issue date: 2009-02-27 / In the past decades companies around the world have implemented their concepts of Lean Manufacture (LM) with their main focus on business. Together with LM s implementation comes the need of continuous improvement as several sources and historical data volume originate from its processing. Whenever those historical data are, for any reason, discarded without generating indicators the oportunity of transforming data into strategic information is missed. Such situation brings about the need of an adequate performance measurement system of LM. Having this constant monitoring need of LM s performance in mind as a strategic mean for a company to achieve competitivity in the market, this paper aims to develop through Information Technology (IT) and the tools of Business Intelligence (BI) a proposal of managing performance indicators of LM present in shoe production, as an alternative to improve decision making. In order to develop this research, an overview of the literature of LM, BI, Information Systems (IS), performance indicators and shoe manufacturing from the basic concept of LM is offered that, on the other hand, has to do with cost reduction and total cut of waste. This study leads to the seven waste groups of LM, which, together with other published works, can guide and focus so as to reach indicators. By setting indicators and pointing strategy, and also collecting data, the next step is modelling and developing data load in dimentional format prepared to use BI s tools as On-line Analytical Processing (OLAP). Finally, the application is tested in a shoe industry with the load of waste indicator for over production. The final result of the research is a series of analyses of waste information for over production as well as the contribution of the proposed method in terms of easiness, flexibility and practical availability in companies. / Nas últimas décadas, empresas do mundo inteiro têm implementado os conceitos da Manufatura Enxuta (ME) com objetivo estratégico para os negócios. Conforme a manufatura enxuta é implementada, surge também a necessidade de melhoria contínua, e conseqüentemente aparecem várias fontes e volume de dados histórico advindos do seu processamento. Quando esses dados históricos por qualquer motivo são descartados sem gerar indicadores, é desperdiçada a oportunidade de transformar os dados em informação estratégica. Esta situação expõe a necessidade de um adequado sistema de medição de desempenho da manufatura enxuta. Entendendo esta necessidade de monitoramento constante do desempenho da Manufatura Enxuta (ME) como um fator estratégico para as empresas obterem competitividade no mercado; este trabalho busca desenvolver através da Tecnologia da Informação (TI) e das ferramentas de Business Intelligence (BI) uma proposta de gerenciamento de indicadores de desempenho da Manufatura Enxuta (ME) presentes na produção de calçados, como uma alternativa para melhorar a tomada de decisão. Para o desenvolvimento do trabalho foi feita uma revisão da literatura de Manufatura Enxuta (ME), Sistemas de Informação (SI), Business Intelligence (BI), indicadores de desempenho e manufatura de calçados com base no conceito da Manufatura Enxuta (ME), que é a redução de custo pela total eliminação de desperdícios. Isto leva aos sete grupos de desperdício da Manufatura Enxuta (ME), que por sua vez, em conjunto com outros trabalhos publicados, servem de guia e foco para encontrar os indicadores. Com a definição dos indicadores e a estratégia de apontamento e carga dos dados, o próximo passo é uma modelagem e desenvolvimento da carga dos dados em formato dimensional, preparado para utilização de ferramentas de Business Intelligence (BI) como On-line Analytical Processing (OLAP). Por último a aplicação é testada numa indústria de calçados com a carga do indicador de perda por super produção. O resultado final do trabalho é uma série de análises das informações de perda por superprodução, assim como a contribuição do método proposto em termos de facilidade, flexibilidade e viabilidade para uso prático nas empresas.

Submitted by Marilene Donadel (marilene.donadel@unioeste.br) on 2017-10-30T21:48:58Z No. of bitstreams: 1 Hermes_J_Inacio_2017.pdf: 1369528 bytes, checksum: 9f1cec87ee533d67c3b814487f544b56 (MD5) / Made available in DSpace on 2017-10-30T21:48:58Z (GMT). No. of bitstreams: 1 Hermes_J_Inacio_2017.pdf: 1369528 bytes, checksum: 9f1cec87ee533d67c3b814487f544b56 (MD5) Previous issue date: 2017-03-30 / This work had the main goal of analyzing the process of implementation of the Business Intelligence tool (BI) in a small company of the retail area of the construction of the West of Paraná (Brazil). As secondary objectives were raised the main limiters that restrict the implementation of the tool, it was evaluated the contribution of the Business Intelligence tool to the performance of the organization, and propose an implementation model for small companies. Therefore, we used the case study method with direct observation and interviews with the managers and collaborators who use the tool. The results obtained in the study demonstrated that there are several limitations to the implementation of Business Intelligence in small companies, among them, hardware and software limitation, process limitations and limitation of human resources. Such limitations can easily be overcome through the use of a model, which contemplates a flow of activities to be followed, demonstrating that the benefits generated by the tool for the performance of these organizations are notorious. Thus, the study was justified by the contribution that Business Intelligence brings to the performance of small companies, besides contributing directly to regional development through the role of employment creation, income and taxes of these enterprises. / Este trabalho teve como objetivo analisar o processo de implantação da ferramenta Inteligência de Negócio (IN) em uma empresa de pequeno porte da área de varejo da construção do Oeste do Paraná (Brasil). Como objetivos secundários foram levantados os principais limitadores que restringem essa implementação, foi avaliada a contribuição da ferramenta Inteligência de Negócio para o desempenho da organização, além de ser proposto um modelo de implementação para empresas de pequeno porte. Para tanto foi utilizado o método de estudo de caso com observação direta e entrevistas com os gestores e colaboradores que utilizam a ferramenta. Os resultados obtidos no estudo demonstram que existem diversas limitações para a implantação da Inteligência de Negócio em pequenas empresas, dentre eles, limitação de hardware e software, limitações de processo e limitação de recursos humanos. Tais limitações podem ser superadas facilmente a partir da utilização de um modelo que contemple um fluxo de atividades a ser seguido, demonstrando que são notórios os benefícios gerados pela ferramenta para o desempenho destas organizações. Desta forma, o estudo justificou-se pela contribuição que a Inteligência de Negócio traz para o desempenho das pequenas empresas, além de contribuir diretamente com o desenvolvimento regional pelo papel de geração de emprego, renda e tributos destes empreendimentos.

Business Intelligence can bring critical capabilities to an organization, but the implementation of such capabilities is often plagued with problems and issues. Why is it that certain projects fail, while others succeed? The theoretical problem and the aim of this thesis is to identify the factors that are present in successful Business Intelligence projects and organize them into a framework of critical success factors. A survey was conducted during the spring of 2011 to collect primary data on Business Intelligence projects. It was directed to a number of different professionals operating in the Business Intelligence field in large enterprises, primarily located in Poland and primarily vendors, but given the similarity of Business Intelligence initiatives across countries and increasing globalization of large enterprises, the conclusions from this thesis may well have relevance and be applicable for projects conducted in other countries. Findings confirm that Business Intelligence projects are wrestling with both technological and nontechnological problems, but the non-technological problems are found to be harder to solve as well as more time consuming than their technological counterparts. The thesis also shows that critical success factors for Business Intelligence projects are different from success factors for IS projects in general and Business Intelligences projects have critical success factors that are unique to the subject matter. Major differences can be predominately found in the non-technological factors, such as the presence of a specific business need to be addressed by the project and a clear vision to guide the project. Results show that successful projects have specific factors present more frequently than nonsuccessful. Such factors with great differences are the type of project funding, business value provided by each iteration of the project and the alignment of the project to a strategic vision for Business Intelligence. Furthermore, the thesis provides a framework of critical success factors that, according to the results of the study, explains 61% of variability of success of projects. Given these findings, managers responsible for introducing Business Intelligence capabilities should focus on a number of non-technological factors to increase the likelihood of project success. Areas which should be given special attention are: making sure that the Business Intelligence solution is built with end users in mind, that the Business Intelligence solution is closely tied to company‟s strategic vision and that the project is properly scoped and prioritized to concentrate on best opportunities first. Keywords: Critical Success Factors, Business Intelligence, Enterprise Data Warehouse Projects, Success Factors Framework, Risk Management

Verksamheter idag har krav på sig att arbeta ekologiskt hållbart. Att arbeta ekologiskt hållbart är i vissa fall en förutsättning för att fortsätta vara verksam på marknaden. Detta har lett till att verksamheter måste se över sitt sätt att arbeta för att minska sin påverkan på miljön. För att göra detta kan verksamheter ta stöd från system vilket har lett till att nya system har implementerats. Ett system för att stödja verksamheter att arbeta ekologiskt hållbart är BI. Med stöd från BI kan verksamheter samla, lagra samt analysera data som leder till att de blir informerade om hur deras processer påverkar den ekologiska hållbarheten. Fler studier kring sambandet mellan BI och ekologiskt hållbart arbete behövs då den teoretiska grunden i området är begränsad. I många fall tittar verksamheter på sitt ekologiskt hållbara arbete åtskilt från resterande delar av verksamheten. Huvudsyftet med studien är att kartlägga hur verksamheter arbetar för att vara ekologiskt hållbara med stöd från BI. Detta kartläggs för att slutligen uppnå delsyftet, vilket är att ta fram och presentera förslag på hur BI kan stärka verksamheternas arbete med ekologisk hållbarhet. Det empiriska materialet i studien samlades in via semistrukturerade intervjuer. Studiens resultat påvisar att tillverkande verksamheter inte tar stöd från sitt BI fullt ut vid sitt arbete med ekologisk hållbarhet. Därför presenteras förslag i studien på hur BI kan stärka verksamheternas ekologiskt hållbara arbete. / Working ecologically sustainable is in some cases essential for a business to continue to be active on the market. There is a larger demand on businesses today to work ecologically sustainable. This has led to businesses reviewing the way they work in order to reduce their environmental impact. The demand on businesses to reduce their environmental impact has led to implementations of new systems with the purpose of supporting their ecological sustainability. A system that is able to support working with ecological sustainability is BI. With the support from BI, businesses can collect, store and analyze data and hence become more informed about how their processes affect the ecological sustainability. However, studies regarding the relationship between BI and ecological sustainability are few. In many cases businesses overview their work with ecological sustainability separate from remaining part of the business. The main purpose of the study is to identify and map how businesses work with ecological sustainability in practice with support from BI. This is mapped in order to finally compose and present proposals on how BI could strengthen the way businesses work with ecological sustainability. The empirical data for this study were collected through semi-structured interviews. The result of the study shows that manufacturing businesses do not take full support from BI regarding their work with ecological sustainability. The study presents proposals of how BI could strengthen businesses work with ecological sustainability

L’intelligence économique (IE) et la veille sont des stratégies d’entreprise qui sont bien implantées dans les grandes entreprises et les grandes PME. Depuis le rapport Carayon en 2003, l’intelligence économique est devenue une politique publique. Ces concepts ont intéressé assez tôt plusieurs disciplines scientifiques, ce qui leur confère un caractère interdisciplinaire. Ce sont des objets empiriques et hybrides car ils sont issus des pratiques des entreprises. Les principales disciplines à s’y être intéressées sont les SIC et les sciences de gestion, pour les principales, mais aussi les sciences économiques. Le sujet a été beaucoup observé, surtout dans les grandes entreprises, notamment par les sciences de gestion car l’intelligence économique et la veille sont principalement des stratégies d’aide à la décision. Aujourd’hui, en SIC, ces objet sont étudiés notamment d’un point de vue informationnel, car l’information se situe au cœur des processus examinés. Il existe des problématiques récurrentes à l’analyse de ces concepts, assez jeunes, qui tournent autour des difficultés d’implémentation de ces stratégies dans les entreprises d’une part, et des obstacles à l’établissement de la politique publique française en la matière d’autre part. Des paradoxes existent à leur sujet. Les petites entreprises et notamment les TPE ont été très peu étudiées par les diverses disciplines scientifiques qui s’y intéressent ce qui paraît surprenant au regard de l’importance de ce type de structure dans l’économie nationale. Par ailleurs, les politiques publiques, assez bien perçues par les grandes entreprises et grandes PME, échouent à percer auprès des petites entreprises. Ce qui nous amène à nous interroger sur les raisons des obstacles constatés au sujet de ces stratégies et politiques publiques auprès des petites entreprises. Les postulats de cette recherche, qui se positionne dans le courant compréhensif, sont fondés sur l’idée centrale qu’il existe des caractéristiques spécifiques aux petites entreprises du fait de leurs contraintes structurelles. Ces petites entreprises mettent en œuvre des stratégies d’IE disruptives, qui leur sont propres, et qui sont fondées sur des processus inverses à ceux qui sont modélisés pour les grandes entreprises. Ces processus sont à caractère communicationnel. L’intelligence économique et la veille dans les petites entreprises s’établissent sur une culture informationnelle collaborative de partage des informations. Ces stratégies sont mises en œuvre dans le cadre d’une structure organisationnelle innovante, réticulaire, la quasi-organisation, à la communication organisante et construite sur une stratégie réseau. Enfin, l’intelligence économique dans la petite entreprise s’appuie sur une sphère de médiation, la biocénose économique, constituée d’un enchevêtrement de relations entre plusieurs acteurs dont les institutions semi-étatiques qui jouent un rôle moteur. Les résultats d’une enquête effectuée auprès de dirigeants de petites entreprises dans le secteur aéronautique spatial défense en Nouvelle Aquitaine, secteur innovant s’il en est, viendront illustrer un modèle d’intelligence économique adapté à la petite entreprise qui sera proposé à la discussion. / Competitive intelligence (CI) and business intelligence are strategies that are well established in large companies and large SMEs. Since the Carayon report in 2003, competitive intelligence has become a public policy. These concepts have been of interest to several scientific disciplines since then, which gives them an interdisciplinary character. They are empirical and hybrid objects because they are derived from the practices of companies. The main disciplines to be studied here are the CIS and the management sciences, for the main ones, but also for the economics. The subject has been much observed, especially in large companies, notably by the management sciences because the competitive intelligence is mainly strategy of decision support. Today, in SIC, these objects are studied in particular from an information point of view, because information is at the heart of the processes examined. There are recurring problems in the analysis of these rather young concepts, which revolve around the difficulties of implementing these strategies in companies on the one hand, and the obstacles to the establishment of French public policy on the matter on the other hand. There are paradoxes about them. Small businesses and especially very small businesses have been little studied by the various scientific disciplines concerned, which is surprising given the importance of this type of companies in the national economy. On the other hand, public policies, which are fairly well perceived by large companies and large SMEs, fail to penetrate small businesses. This leads us to question the reasons for the barriers to these small business strategies and policies. The postulates of this research are based on the central idea that there are characteristics specific to small enterprises because of their structural constraints. These small companies are implementing their own disruptive CI strategies, which are based on processes that are inverse to those modeled for large enterprises. These processes are communicational in nature. Competitive and business intelligence in small businesses are based on a collaborative information-sharing culture. These strategies are implemented within the framework of an innovative organizational structure, reticular, quasi-organization, based on an organizational communication and built on a network strategy. Finally, competitive intelligence in small enterprises is based on a sphere of mediation, economic biocenosis, constituted by an entanglement of relations between several economic actors including state institutions which play a leading role.

Business Intelligence (BI) concept has continued to play a vital role in its ability for managers to make quality business decision to resolve the business needs of the organization. BI applications comes handy which allows managers to query, comprehend, and evaluate existing data within their organizations in order to obtain functional knowledge which then assist them in making improved and informed decisions. Data warehouse (DW) is pivotal and central to BI applications in that it integrates several diverse data sources, mainly structured transactional databases. However, current researches in the area of BI suggest that, data is no longer always presented in only to structured databases or format, but they also can be pulled from unstructured sources to make more power the managers’ analysis. Consequently, the ability to manage this existing information is critical for the success of the decision making process. The operational data needs of an organization are addressed by the online transaction processing (OLTP) systems which is important to the day-to-day running of its business. Nevertheless, they are not perfectly suitable for sustaining decision-support queries or business questions that managers normally needs to address. Such questions involve analytics including aggregation, drilldown, and slicing/dicing of data, which are best supported by online analytical processing (OLAP) systems. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Data in an OLAP warehouse is extracted and loaded from multiple OLTP data sources (including DB2, Oracle, SQL Server and flat files) using Extract, Transfer, and Load (ETL) tools. This thesis seeks to develop DW and BI system to support the decision makers and business strategist at Crystal Entertainment in making better decision using historical structured or unstructured data.

De allra flesta av oss kommer ständigt i kontakt med olika dataflöden vilket har blivit en helt naturlig del av vårt nutida informationssamhälle. Dagens företag agerar i en ständigt föränderlig omvärld, och hantering av data och information har blivit en allt viktigare konkurrensfaktor. Detta i takt med att den totala datamängden i den digitala världen har ökat kraftigt de senaste åren. En benämning för gigantiska datamängder är Big Data, som har blivit ett populärt begrepp inom IT-branschen. Big Data kommer med helt nya analysmöjligheter, men det har visat sig att många företag är oroliga för hur de ska hantera och ta tillvara på de växande datamängderna. Syftet med denna studie har varit att ge ett kunskapsbidrag till det relativt outforskade Big Data området, detta utifrån en induktiv ansats med utgångspunkten ur intervjuer. Den problematik som kommit med Big Data beskrivs oftast ur tre perspektiv; där data förekommer i stora volymer, med varierande data-typer och källor, samt att data genereras med olika hastighet. Det framgick av studiens resultat att Big Data som begrepp berör många olika områden och det kan variera väldigt mycket mellan företag inom olika branscher vad gäller betydelse, förmåga, ambition och omfattning. De traditionella teknologierna för datalagring och utvinning är inte tillräckliga för att hantera data som benämns som Big Data. I samband med att ny teknologi tagits fram och äldre lösningar uppgraderats, har detta dock lett till att det nu går att se informationshantering och analysarbete i helt nya perspektiv. Eftersom Big Data huvudsakligen har samma syfte som området Business Intelligence, kan dessa lösningar lämpligen integreras. En mycket stor utmaning med Big Data är att det inte är möjligt att exakt veta vad som kommer att uppnås med datainsamling och analys. Efter att data har samlats in bör ett business case tas fram med riktlinjer för vad som ska uppnås. Det finns en stor potential i denna uppgående marknad som, trots allt, är relativt omogen. Informationshantering kommer att bli allt viktigare framöver och för företagen handlar det om att hänga med i snabba utvecklingen och skaffa sig en bra förståelse för nya trender i IT-världen.