A evolução pela qual passou o chão-de-fábrica no último século transformou-o numa área estratégica para as empresas, por meio da qual é possível atender e satisfazer as necessidades dos seus clientes. Esse novo chão-de-fábrica gera hoje uma grande quantidade de dados nos controles do seu processo produtivo, os quais, em muitos casos, após seu uso imediato ou de curto prazo, acabam descartados ou armazenados inadequadamente, impossibilitando ou dificultando seu acesso. Esses dados, no entanto, podem vir a ter uma importante utilidade como matéria-prima para a geração de informações úteis à gestão do negócio. Aliado à necessidade que as empresas hoje têm de possuir um adequado sistema de medição de desempenho, é possível obter, a partir dos dados históricos do chão-de-fábrica, um bom conjunto de indicadores de desempenho para a área. Para isso, esses dados precisam ser modelados em sistemas especialmente projetados para esta função. Esses sistemas vêm sendo tratados como sistemas de apoio à decisão (SAD) ou Business Intelligence (BI). Como solução para os problemas acima, foi feita então uma revisão sobre os temas: sistemas de informação, ERP, sistemas de medição de desempenho, qualidade da informação, SAD/BI, bem como uma revisão sobre os trabalhos científicos relacionados ao tema da tese. Uma vez tendo sido esses conceitos consolidados, partiu-se para o desenvolvimento de um modelo dimensional de BI que se utilizou das ferramentas de Data Warehouse, On Line Analytical Processing (OLAP) e Data Mining. O software utilizado foi o Analysis Services, pertencente ao banco de dados Microsoft SQL Server 2000. Em seguida, o modelo foi testado com dados reais de uma empresa do ramo metal-mecânico, tratada aqui como “empresa A”. Por meio do modelo e dos dados reais, uma série de análises foram realizadas com o intuito de mostrar a contribuição, capacidade, flexibilidade e facilidade de uso do modelo, atingindo o objetivo proposto / The shop floor evolution in the last century has transformed it in a strategic area for the companies, through which is possible to reach and to satisfy the customers needs. This new shop floor generates today a great amount of data by the productive process controls, the ones which, in many cases, after its immediate use or in a short period, are discarded or stored inadequately, disabling or making impossible its access. These data, however, can come to have an important use as raw material for the production of useful information to the business administration. Together to the need that the companies have today to possess an appropriated performance measure system, it is possible to obtain, from the historical shop floor data, a good performance indicators group for the area. For that, these data must be modeled by systems specifically designed for this purpose. These systems have been treated as decision support systems (DSS) or Business Intelligence (BI). As solution for the problems above, it was made a review over the following themes: information systems, ERP, performance measure system, information quality, DSS/BI, as well as a review about the scientific works related to this thesis theme. Once consolidated these concepts, started the development of a BI dimensional model, that used Data Warehouse tools, On Line Analytical Processing (OLAP) and Data Mining. It was used the Analysis Services software, belonged to the Microsoft SQL Server 2000 database. In the following, the model was tested with real data from a metal-mechanic company branch, called here as “company A”. Through the model and of the real data, a series of analyses was accomplished with the intention of showing the model contribution, capacity, flexibility and use easiness, reaching the proposed objective

Syftet med denna studie är att utreda begreppen big data och analytics. Utifrån vetenskapliga teorier om begreppen undersöks hur konsultföretag uppfattar och använder sig av big data och analytics. För att skapa en nyanserad bild har även en organisation inom vården undersökts för att få kunskap om hur de kan dra nytta av big data och analytics. Ett antal viktiga svårigheter och framgångsfaktorer kopplade till båda begreppen presenteras. De svårigheterna kopplas sedan ihop med en framgångsfaktor som anses kunna bidra till att lösa det problemet. De mest relevanta framgångsfaktorer som identifierats är att högkvalitativ data finns tillgänglig men även kunskap och kompetens kring hur man hanterar data. Slutligen tydliggörs begreppens innebörd där man kan se att big data oftast beskrivs ur dimensionerna volym, variation och hastighet och att analytics i de flesta fall syftar till att deskriptiv och preventiv analys genomförs. / The purpose of this study is to investigate the concepts of big data and analytics. The concepts are explored based on scientific theories and interviews with consulting firms. A healthcare organization has also been interviewed to get a richer understanding of how big data and analytics can be used to gain insights and how an organisation can benefit from them. A number of important difficulties and sucess facors connected to the concepts are presented. These difficulties are then linked to a sucess factor that is considered to solve the problem. The most relevant success factors identified are the avaliability of high quality data and knowledge and expertise on how to handle the data. Finally the concepts are clarified and one can see that big data is usually described from the dimensions volume, variety and velocity and analytics is usually described as descriptive and preventive analysis.

Submitted by Ana Lucia de Queiroz Tourinho (analucia.tourinho@gmail.com) on 2017-07-27T15:24:24Z No. of bitstreams: 1 Tese_Ana Lucia Tourinho_Final_Entrega.pdf: 2587062 bytes, checksum: 3a12b24dad9c3cf4ced309dcabea48f9 (MD5) / Approved for entry into archive by Pamela Beltran Tonsa (pamela.tonsa@fgv.br) on 2017-07-27T15:25:56Z (GMT) No. of bitstreams: 1 Tese_Ana Lucia Tourinho_Final_Entrega.pdf: 2587062 bytes, checksum: 3a12b24dad9c3cf4ced309dcabea48f9 (MD5) / Made available in DSpace on 2017-07-27T17:03:10Z (GMT). No. of bitstreams: 1 Tese_Ana Lucia Tourinho_Final_Entrega.pdf: 2587062 bytes, checksum: 3a12b24dad9c3cf4ced309dcabea48f9 (MD5) Previous issue date: 2017-06-29 / O crescimento maciço da quantidade de dados que as empresas, organizações e a sociedade foram obrigadas a lidar, reforça a necessidade de estudos sobre Business Intelligence, Business Intelligence Analytics e Big data. Este assunto permanece na agenda das empresas como uma das prioridades e vem se tornando cada vez mais relevante, visto que os dados e as informações compreendem a matéria-prima a partir da qual as empresas desenvolvem as suas estratégias de negócios para competir em um mundo mais complexo e rápido. Portanto, entender como explorar melhor os dados disponíveis é hoje uma disciplina de grande importância, não só como uma questão de negócios, mas também como um tema de pesquisa acadêmica. Neste cenário, onde se busca o melhor uso dos dados, o presente estudo tem como objetivo desenvolver uma pesquisa científica que permita medir a capacidade de uma organização transformar os dados em valor para o negócio. Embora certos aspectos do tema Business Intelligence, Business Intelligence Analytics e Big data já estejam bem definidos entre os pesquisadores, ainda há uma falta de compreensão e consenso sobre quais são os antecedentes do construto Business Intelligence Analytics (BIA) e quais combinações de variáveis levam a um melhor uso dos dados. Além disso, embora vários pesquisadores tenham endereçado o tema sucesso em BIA com diferentes abordagens, evidenciamos a falta de estudos que comprovem sua eficácia. Neste estudo, estamos propondo um modelo conceitual integrado para Business Intelligence Analytics Capability (BIAC), com escalas que possibilitem testar sua eficácia. Em última análise, o modelo proposto BIAC poderá ser posteriormente aplicado pelas empresas que desejem avaliar seu nível de maturidade na BIA. / The massive growth in the amount of data that companies, organizations, and society have been compelled to deal with, reinforce the need for studies on Business Intelligence, Business Intelligence Analytics and Big data. This subject remains among business agenda of priorities and has become more and more relevant since data and information comprise the raw material from which business strategies are developed to compete in a more complex and fast world. Therefore, understanding how to better exploit the available data is today a matter of great importance, not only as a business issue but also as an academic research topic. Within this scenario, where the best use of data is sought, the present study aims at developing a scientific research that enables to measure the capability of an organization to transform data into business value. Although certain aspects of the theme Business Intelligence, Business Intelligence Analytics and Big data are already established between researchers, there is still a lack of understanding and consensus on which are the antecedents of a Business Intelligence Analytics construct (BIA), and how to combine variables to enforce a better use of data. In addition, although researchers have addressed success in BIA by different approaches, we have also identified a lack of studies proving its effectiveness. In this study, we are proposing an integrated conceptual model for Business Intelligence Analytics Capability (BIAC) with proper scales to test its effectiveness. Ultimately, the BIAC may later be applied by firms to assess their level of maturity in BIA.

As with many new technology implementations before, Business Intelligence (BI) implementations have come under scrutiny in terms of the value added to organisations. The latest academic research uses various different ‘traditional financial methods’ such as the Return on Investment (ROI) calculations to determine the value of investments. In some instances customized measurement tools are proposed as a result of empirical studies conducted. The main objective of this study is to perform qualitative investigation on the theoretical value of BI implementations in organisations. If it is assumed that BI does add value to organisations, the primary research question aims to investigate how this value is added. A balanced scorecard second generation approach is used as theoretical framework in order to address the question. This approach is also known as strategy mapping. The research process starts with an extensive literature review on the topic of BI value evaluations and BI related balanced scorecards and strategy maps. As a result, various theoretical and practical research contributions are produced. One of the theoretical contributions includes an inventory of previous academic literature on the topic of BI value evaluations. This inventory is also presented diagrammatically. A preliminary version of the BI balanced scorecard is developed and used as foundation for a semi-structured interview template to investigate the value of BI in organisations.\ The semi-structured interview template was used as primary data generation instrument to gather data for this multiple-case study project. Four organisations participated in the study. The data obtained from the study was analysed and presented using an updated version of the theoretical framework of the study and was labelled as the intermediate version of the scorecard. The intermediate version of the BI balanced scorecard provided valuable input towards investigating how value was achieved as a result of BI implementations. These results were verified as part of a final verification process through a consultation process with the same study participants. A final version of the BI balanced scorecard is presented and included in a complete BI value solution. This version of the balanced scorecard is also a valuable practical contribution of the study. The verified results of the investigation indicated the following:- (1) BI adds value to organisations in all four perspectives of the organisation, namely the business value perspective, user orientation perspective, operational excellence perspective as well as the future orientation perspective. (2) Furthermore, the study confirmed that BI adds value to organisations through a number of benefits including an increase in sales and compliance to regulatory requirements (to name a few). (3) However, the study also indicated that value-related studies must adopt a holistic approach in an attempt to cater for the many facets of the topic. (4) Despite the challenge of implementing BI in organisations, the perceived value of BI implementations amongst senior management remained positive. This was supported by the continuous implementation of new BI projects in organisations. (5) There was a definite relationship between BI implementations and organisational performance. This is evident through the positive impact on sales figures, risk and compliance management and operational management. The study contributes to the broader field of Information Systems (IS) in numerous ways. Firstly the research discovers gaps in existing research, then proposes a method to investigate the research question as a result of the gaps identified, and -iiiuses this method as a tool to investigate the problem. The main contribution of the study is to contribute to the body of knowledge by investigating the value of business intelligence to organisations. / College of Engineering, Science and Technology / D. Litt et. Phil. (Information Systems)

Made available in DSpace on 2015-05-08T14:53:30Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2082485 bytes, checksum: 0c5cd714d0a43bac80888cfc1dd4e7cb (MD5) Previous issue date: 2010-09-03 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This search has as goal to validate the hypothesis of the applicability of data mining techniques in Bidding and Contracts database managed by the Account Court of Paraiba State, enabling the generation of rules and discovery of hidden knowledge or implicit, contributing to the process of decision making, supervision and celerity in this Court of Auditors. To the best comprehension of this work, It was made a literature revision bringing at first place a historic vision about the decision process, as well as this theme evolution studies and the relation between the tender processes sent to Account Court of Paraiba State and the fraud indication discovery process and irregularities through the data mining process using. We will bring to light the concept of Business Intelligence (BI) and for it`s main components, as well as the concepts of knowledge discovery in database, and a comparing between the using of the instruments of data mining. We expect from this implant of the data mining an increase in the productivity and also an increase in speed of lawsuit process from the public accounts analysis and public money fiscal control. / Esta pesquisa tem como objetivo validar a hipótese da aplicabilidade das técnicas de mineração de dados na base de dados de Licitação e Contratos gerenciada pelo Tribunal de Contas do Estado da Paraíba (TCE-PB), possibilitando a geração de regras e descoberta de conhecimento oculto ou implícito, contribuindo desta forma com o processo de tomada de decisão, fiscalização e celeridade processual no âmbito desta Corte de Contas. Para melhor compreensão desse trabalho foi realizada uma revisão de literatura abordando primeiramente um histórico sobre o processo de decisão, bem como a evolução dos estudos deste tema e da relação entre os processos licitatórios enviados ao TCE-PB e o processo de descoberta de indícios de fraudes e irregularidades através do uso de mineração de dados. São abordados os conceitos sobre a tecnologia de Business Intelligence (BI) e dos seus principais componentes, bem como os conceitos de Descoberta de Conhecimentos em Bases de Dados (Knowledge Discorevy in Databases), e uma comparação das funcionalidades presentes nas ferramentas de mineração de dados. Espera-se com a implantação desta ferramenta de mineração de dados, um ganho de produtividade e um aumento na celeridade do tramite processual decorrentes da análise das contas públicas e na fiscalização do erário.

Oavsett vilken bransch ett företag tillhör finns idag stora möjligheter att samla in data relaterad till företagets kunder, processer, varor och tjänster. Om man har resurser och intresse finns oanade möjligheter att ta tillvara på all data, analysera den och sedan använda den för att vidareutveckla och optimera företaget. Syftet med denna uppsats är att undersöka vilka utmaningar som finns när det kommer till att använda lagrad data inom Business Intelligence, samt att undersöka vilka faktorer som påverkar processen att omvandla lagrad data till användbar information som sedan kan användas vid beslutsfattande inom marknadsföring. Jag har valt att genom en fallstudie fokusera på organisatoriska aspekter samt kopplingar mellan Business Intelligence och marknadsföring. Detta sker med hjälp av en kvalitativ forskningsansats och ett abduktivt förhållningssätt. Studiens empiri består av kvalitativa semistrukturerade intervjuer samt datainsamling i form av klustring. All empiri har samlats in från ett stort svenskt IT-företag. En tematisk analys av insamlad empiri leder fram till ett resultat som visar att ett flertal utmaningar finns när det kommer till att använda lagrad data inom Business Intelligence hos ett stort Svenskt IT-företag. Dessa utmaningar består av otydliga visioner och mål, ekonomiska prioriteringar, oklar begreppsstruktur samt förhållningssätt till organisationsstrukturer. Studiens resultat visar även på ett flertal faktorer som påverkar processen att omvandla lagrad data till användbar information som sedan kan användas vid beslutsfattande inom marknadsföring. Dessa faktorer är: gamla vanor och anpassning till ny teknik, ledning och kommunikation samt tidsbrist och kunskapsförmedling. Jag har med denna studie kunnat se att det krävs ett grundläggande syfte för att analysera något och att det inte alltid är självklart att ett sådant syfte existerar. Om detta syfte inte finns kan det vara svårt att omvandla data till värdeskapande information och även att använda data till att optimera beslut. Studien belyser även företags medvetenhet om deras problem att hantera stora datavolymer och/eller deras erkännande att de har en lång väg att gå när det gäller hantering av dessa volymer. Jag har med denna studie kunnat se att detta forskningsproblem kan vara betydelsefullt och är högst relevant inom ett stort svenskt IT-företag. / Today there are great opportunities to gather data related to a company’s customers, processes, goods and services no matter what line of business they are in. If you have resources and are interested there are endless opportunities to gather data, analyze it and then use it to develop and optimize the company. The purpose of this study is to examine challenges when it comes to the use of stored data in business intelligence and to look into which factors that affects the process of turning stored data into usable information that can be used when making marketing decisions. I have chosen to focus on the organizational aspects and connections between business intelligence and marketing with the help of a case study. The study uses a qualitative research approach and the empirical data consists of qualitative semi-structured interviews and data collection in terms of clustering. All empirical data has been gathered from a large Swedish IT-company. A thematic analysis of gathered empirical data leads to a result showing that there are a number of challenges when it comes to using stored data in business intelligence at a large Swedish IT-company. These challenges consist of; unclear visions and goals, economic priorities, unclear terminology and the approach to organizational structures. Results from the study also points out several factors that lies behind why all available data at a large Swedish IT-company is not used to its fullest potential within marketing. These factors are; old habits and adaptability to new technology, management and communication and finally time constraints and knowledge management. I have been able to see with this study that it takes a fundamental purpose to analyze something and that it is not certain that such a purpose always exists. It is hard to transform data into valuable information and also to optimize decision making if this purpose does not exist. There are many studies which demonstrate either the awareness by companies of the problems facing them in dealing with high volumes of data and/or their admission that they have a long way to cope with these volumes. I have with this study been able to see that this research problem may be useful and that it is highly relevant within a large Swedish IT-company.

Syftet med uppsatsen är att kunna erbjuda små till medelstora företag förslag på lämpliga modeller och IT-stöd att använda till omvärldsanalys. Genom att föreslå modeller och IT-stöd samt hur de används kan företag upprätta ett omvärldsanalysarbete utan att hyra in extern hjälp. Större företag har ofta en större budget och även resurser som aktivt arbetar med att bevaka omvärlden. Den sortens resurser finns inte i mindre företag eftersom kostnaden att hyra in konsulter för hjälp med omvärldsanalysarbete är för hög. Det i kombination med att kunskap saknas om hur ett omvärldsanalysarbete kan genomföras resulterar i att det ofta inte blir av. Fyra omvärldsanalysmodeller valdes ut och testades vid workshoppar på två olika företag. Vid sidan av det har kompletterande intervjuer genomförts på tre företag samt att en studie av lämpliga IT-stöd för omvärldsbevakning upprättats. Undersökningen visade att en manual är den mest lämpliga presentationsformen för hur små till medelstora företag kan använda sig av utvalda modeller och IT-stöd vilket skulle bidra till att de inte behöver hyra in extern hjälp för att starta ett omvärldsanalysarbete. I manualen framgår också vilka omvärldsanalysmodeller och IT-stöd som funnits mest lämpliga för små till medelstora företag. / The purpose of this thesis is to offer models and IT-supportive tools for external business intelligence for smaller to medium sized businesses. Larger businesses often have more funds and resources that continuously work to monitor the surrounding environment. However, due to the fact that small businesses lack the time and knowledge to investigate external business intelligence sufficiently, and lack appropriate funds to hire a consultant, as they are too expensive, smaller businesses rarely ever have access to these resources. Four models for external business intelligence were chosen and tested in workshops with two different companies, three different companies were approached and participated in interviews, and a study on useful IT-supporting tools for external business intelligence was conducted. The study has shown that the most suitable way to display models and IT-supportive tools is with a manual that would assist smaller to medium sized businesses with external business intelligence without the need to hire external assistance.

Idag skapas och lagras enorma mängder data, samtidigt som endast en liten del av datan analyseras och används. Big data är ett begrepp som cirkulerat i flera år, men på senare år har det fått allt större innebörd. Allt fler företag börjar få upp ögonen för big data, samtidigt som få verkligen vet hur det ska användas. Vissa frågar sig till och med: finns det någon affärsnytta? Med fokus på detaljhandelsbranschen undersöker vi huruvida det finns en affärsnytta med big data, och framförallt vilka utmaningar och möjligheter som finns kopplade till det. Begrepp som business intelligence och analytics diskuteras i sambandet. I denna kvalitativa studie har tre experter från olika företag; IBM, Knowit och Exor, intervjuats. Resultatet från intervjuerna har kopplats till den teori som tagits fram ur litteratur kring ämnet, och jämförts i analysen. Samtliga identifierade utmaningar och möjligheter har listats, och bland slutsatser ses att de etiska och mänskliga faktorerna har stor betydelse, och att affärsnyttan kan vara beroende av ett företags storlek och marknad. Uppsatsen är skriven på svenska. / Enormous amounts of data is created and stored today, all the while only a small amount of data is being analysed and used. Big data is a term that has circulated for years, however in recent years its meaning have been increased. More enterprises are starting to open their eyes for big data, while few understand how to actually use it. Some even ask themselves: is there a business benefit? With a focus on the retail industry, we examine whether there is a business benefit with using big data, and above all what challenges and opportunities are connected to it. Terms such as business intelligence and analytics are discussed in the relationship to big data. In this qualitative study, three experts from different enterprises; IBM, Knowit and Exor, have been interviewed. The results from the interviews has been connected to the theory from the literature around the subject, and has been compared in the analysis. All identified challenges and opportunities have been listed, and among the conclusions can the ethical and human factors be seen to have a major importance, and that the business benefits can be dependent of an enterprises’ size and market. The essay is written in Swedish.

This diploma thesis deals with usability of the SAS system and its components for building and running data warehouse and complete solution of Business Intelligence. In the beginning it introduces the meaning and benefits of putting on Business Intelligence and its spot in an organization. It especially focuses on the running BI project in Kooperativa, a.s., insurance company. The main goal of this thesis is to aim on ETL processes of data warehouse, their specificity, characteristics and regular tasks solved across data layers, measuring their performance and feasibility of ETL optimalization. This optimalization can be considered from two different points of view – the first is a creation and maintenance of the ETL source code, the second is tuning for faster data processing. Log files, which are are the main source for performance monitoring, are processed by macroprogram specially tailored to this particular reason. Gained results are analyzed and on that basis I outline spots that need attention. The last part offers comparison of some alternatives to data transformation process typically solved by ETL tasks. Acquired results could be taken as hints used in designing and tweaking other akin ETL processes.

The main objectives of this work are determining Self-Service Business Intelligence as one of Business Intelligence approaches, description and characterization of Qlik Sense and finally development of particular solution using the above tool. In the first part thesis deals with the theoretical side of the approach, describes its advantages and disadvantages and users coming into contact with the respective technologies. Subsequently, thesis specifies requirements for Self-Service Business Intelligence and mentions different requirements and sizes of solutions in organizations. At the end recommendations for the implementation of the approach and technologies are mentioned. The second part is devoted to Qlik Sense platform, as one of the representatives based on the Self-Service approach. At first basic features, options and benefits are described. Then the development of solutions and principles of work with this tool are explained. Finally, architecture of enterprise server solution is described together with installation steps and basic configuration of a Qlik Sense site. The third part is focused on mobile virtual operator GoMobil, its current state, Business Intelligence needs and last but not least the requirements and development of solution using Qlik Sense platform.