The aim of the thesis is to identify a roadmap for well-established companies towards business model innovation to explore data analytics value. The business model innovation currently taking place at Caterpillar and Ericsson in order to explore data analytics value is presented to answer the question: “How do established companies explore data analytics to innovate their business models?” Initially, the problem discussion, formulation and purpose are given. Then, the relevant theory is presented covering the importance of data analytics, IT infrastructure challenges due to the increased volume of data created, data analytics methods currently being used, smart connected products and the Internet of Things. The meaning of business model innovation is given, followed by a well-structured business model process which includes the business model canvas for representation purposes. The business areas affected by data analytics value and the barriers of business model innovation are given as well. After that, the theory addressing business model innovation to explore data analytics value is presented and the main industries which are currently on this journey along with the required initial steps and the business models that can come out of this process are identified. The challenges and risks if the option of not following this route is chosen are also shown. The method section follows to explain the case study design, data collection method and way of analysis. The results cover all the information gathered from numerous sources including on-line available information, papers, interviews, videos, end of year reviews and most importantly current Caterpillar and Ericsson mid-level management employee answers to a questionnaire created and distributed by the authors. The business model canvas tool is used to aid the reader understanding Caterpillar’s and Ericsson’s business model innovation. Each company’s business model is given before and after data analytics adoption. Finally, the analysis of the results and the link with the theory is given in order to answer the thesis question.

Sistemas educacionais possuem diversas funcionalidades capazes de apoiar a interação entre alunos e professores de maneira dinâmica, síncrona e assíncrona. Uma das formas de monitorar a eficácia do processo educacional e por meio da utilização dos dados armazenados nesses sistemas como fonte de informação. Pesquisas em Learning Analytics, Academic Analytics e Mineração de Dados Educacionais, buscam explorar os dados de sistemas educacionais utilizando processamento analítico e técnicas de mineração de dados. No entanto, há uma serie de fatores que dificultam a gestão eficiente do processo educacional a partir dos dados de sistemas educacionais. A transformação de dados provenientes de diferentes tipos de sistemas educacionais, como Sistemas de Gestão de Aprendizagem e Sistemas Acadêmicos, e uma tarefa complexa devido a natureza heterogênea dos dados. Dados provenientes desses sistemas podem ser analisados considerando diferentes stakeholders, sob varias perspectivas e níveis de granularidade. Neste cenário, um modelo de referência para a descoberta de conhecimento a partir de dados de sistemas educacionais, denominado Modelo de Referência de Dados Educacionais (EDRM), foi desenvolvido neste trabalho. O EDRM e um modelo dimensional no formato star schema, estruturado em um Data Warehouse, projetado para ser uma fonte única de dados integrados e correlacionados voltada a tomada de decisão. Assim, e possível armazenar dados de diversas fontes, combina-los e, por fim, realizar analises que levem as instituições a desenvolver uma melhor compreensão, rastrear tendências e descobrir lacunas e ineficiências acerca do processo educacional. Neste trabalho, o EDRM foi validado por meio de um estudo de caso, utilizando bases de dados reais coletadas de diferentes sistemas educacionais. Os resultados mostram que o EDRM e eficiente em tarefas com diferentes objetivos, utilizando processamento analítico e mineração de dados. / Educational systems support dynamic, synchronous and asynchronous interaction between students and educators. Researches in Learning Analytics, Academic Analytics and Educational Data Mining explore data from educational systems for knowledge discovery through analytical processing, statistical analysis and data mining. However, there are some factors that hinder an efficient management of the educational process. The transformation of data from different kinds of educational system, as Learning Management Systems and Student Information Systems, can be even more difficult due to data heterogeneity. Data from these systems can be analyzed considering different stakeholders, under different perspectives and under different granularities. Motivated by this scenario, in this work we propose Modelo de Referência de Dados Educacionais (EDRM), a reference data model for knowledge discovery in data from educational systems. EDRM is an analytical model structured under a Data Warehouse architecture following a multidimensional data model. EDRM is projected for being an resource of integrated and correlated data focused in decision taking in the educational process. EDRM was developed considering a deep analysis of data and functionalities from different educational systems. In this sense, data from different kinds of systems and sources can be used unified, integrated and consistently. This allows institutions to better comprehend their data, as well as discover patterns, gaps and inefficiencies about their educational process. In this work, EDRM was validated in a case study using real-world databases from different educational systems. The results indicate that EDRM is efficient in tasks with different objectives, using Learning Analytics and Educational Data Mining techniques, and analyzing different perspectives.

This dissertation deals with issues of online marketing strategy of the company SEVT a.s. The mening part dealts with issues of internet marketing in general including tools of internet marketing. In the next part situation analysis of the company itself is performed. In this dissertation company's websites are closely examined, including visitou analysis, konversion analysis and so on. The next part is dedicated to the SEO and SEM problematics. Company's websites are rated based on their SEO principles and effectiveness of the websites is evaluated. Tools of internet marketing used in the company are analysed in the part SEM. Aim of this dissertation is to evaluate current online marketing strategy and design a new strategy, that will be more effective in the usage of internet marketing tools and in the mening of web presentation of the company itself. This new strategy should lead to the increase in earnings from the online marketing actitivities of the company.

College course enrollment operates as a market under supply cap. Because of the limited number of seats available for any given course some students who have a higher demand for a course are unable to enroll. The current registration system at the Claremont Colleges functions as a random draw system with added time costs. The lack of price signalling in the markets leads to a loss in overall welfare of the student body. By running data through simulated demand curves I am able to determine, on average, how much welfare is being lost by a random draw system. The percent of maximum welfare achieved compared to maximum possible ranges from forty-nine to eighty percent and largely depends on the proportion of enrolled students to the sum of enrolled + enroll requests as well as the demand function type. With price signalling, the student body would be able to reach the maximum achievable welfare.

The use of analytics in human resource (HR) management has proven successful in improving company performance by reducing workforce costs, improving the quality of recruitment, improving talent management and employee engagement, and generally improving productivity. The purpose of this qualitative, multiple-case study was to explore how HR managers use analytics to improve company performance using the contextually based human resource theory as the conceptual framework. The target population comprised a purposeful sample of 5 HR managers in Washington DC; the United States; and Lagos, Nigeria, who had experience using analytics for HR management. Data were collected through semistructured interviews using face-to-face, telephone, and Internet communications and a review of company documents and websites. Data analysis included content and thematic analysis. Four themes emerged from data analysis: the need for HR analytics to align with organizational strategy, the need for understanding HR metrics and how insights derived from HR analytics improve company performance, influencers of HR analytics adoption, and the barriers to HR analytics adoption. The findings and recommendations of this study can assist HR managers in implementing HR analytics successfully. The implications for positive social change include the potential for increased employee satisfaction, improved productivity, and enhanced prosperity in local communities, leading to positive socioeconomic indicators.

Thesis (Ph. D.)--Washington State University, December 2007. / Includes bibliographical references (p. 98-107).

Introduction: The term Business Intelligence arose in the mid-1990s and is a growing share of the IT market. The need of Business Intelligence emerges from an increasing competition and a constantly changing and more complex business climate. Problem discussion: There are only few examples of research dealing with data-driven decision processes. How data are incorporated in decision making processes is crucial for the future use of decision support systems. The literature stress that managers must use more analytics and rationality to make better and more appropriate decisions. However, previously studies have indicated that intuition still plays a major role in decision making, even in organizations using Business Intelligence. With this background the following research question is presented: To what extent are Business Intelligence systems used to support decisions in organizations? Purpose: The purpose of this study is to describe and observe Business Intelligence from a decision making perspective. Method: The primary source of data is personal interviews and one observation study, which implies a qualitative method. The respondents are an organization in the start-up phase, IT-consultants and suppliers and current Business Intelligence users. An abductive approach is applied, and the analyses of data is done simultaneously as the examination of literature and previously made studies. Findings: The system is mainly used for producing reports and as a provider of information. More information and more detailed information are accessible due to the Business Intelligence system. The information itself is valued highly, it is assumed that if the decision maker has enough of information, an appropriate decision will be made. Intuition is still frequently used among the users, yet the Business Intelligence system can to some extent neutralize the user. This is due to that the system is used to confirm and follow up the intuition.

This thesis examines the usage of and attitudes toward data visualization and analytic tools in three genres of online games. Using an online survey, this research analyzes responses from participants regarding their play habits and attitudes online. Several scales are generated identifying different player demographics such as emotional attitudes, competitive attitudes, technological attitudes, spectator involvement, and overall attitudes toward information customization. In addition, several genre specific scales are created for massive multiplayer online games (MMO), real time strategy (RTS) and first person shooting (FPS) games. This research concludes that competitive attitudes are moderately correlated with information customization and implementation of data visualization tools. Additionally, the relationship between the usage of data visualization tools are strongest with the MMO genre compared to the RTS or FPS genres. In addition, my research shows a strong preference between the responses for the usage of data visualization tools amongst those who report higher levels of spectator involvement with online games. Finally, my research concludes that there is a strong relationship between the amount of time players spend playing online games and the attitudes toward and usage of data visualization tools. / text

This thesis makes progress towards the fundamental understanding of heterogeneous and dynamic information systems and the way that we store and process massive data-sets. Reliable large-scale data storage: Distributed storage systems for large clusters typically use replication to provide reliability. Recently, erasure codes have been used to reduce the large storage overhead of three-replicated systems. However, traditional erasure codes are associated with high repair cost that is often considered an unavoidable price to pay. In this thesis, we show how to overcome these limitations. We construct novel families of erasure codes that are optimal under various repair cost metrics, while achieving the best possible reliability. We show how these modern storage codes significantly outperform traditional erasure codes. Low-rank approximations for large-scale data processing: A central goal in data analytics is extracting useful and interpretable information from massive data-sets. A challenge that arises from the distributed and large-scale nature of the data at hand, is having algorithms that are good in theory but can also scale up gracefully to large problem sizes. Using ideas from prior work, we develop a scalable lowrank optimization framework with provable guarantees for problems like the densest k-subgraph (DkS) and sparse PCA. Our experimental findings indicate that this low-rank framework can outperform the state-of-the art, by offering higher quality and more interpretable solutions, and by scaling up to problem inputs with billions of entries. / text

Webserver logfiles are an inexpensive, automatically captured text-based recording of user interactions with a website. In this thesis, a tool, ViDLog, was created to take logfiles and reanimate a user session with the purpose of gaining usability insights. To evaluate the effectiveness and value of reanimating user sessions, 10 usability professionals viewed logfile-recorded website usage using ViDLog and were then asked to infer users’ goals, strategies, successes or failures, and proficiencies; and afterwards, rate, ViDLog across multiple dimensions. ViDLog’s logfile reanimation proved successful for gaining usability insights; usability professionals were able to infer users’ goals, strategies, successes or failures, and proficiencies. Participants were able to do this without ViDLog training, without familiarity of the website being evaluated (Orlando), and without domain knowledge of the subject depicted in the user sessions (women’s literature). However, they were only able to infer users’ overarching goal, not specific goal criteria; and were only able to determine relative proficiencies after viewing both user sessions. They also expended a good deal of mental effort when comprehending ambiguous user sessions, and found inefficiencies in ViDLog’s user interface. / Dr. Susan Brown for The Orlando Project