The main purpose of this thesis is to investigate the use of Big Data for the methods and procedures of Competitive Intelligence. Among the goals of the work is a toolkit for small and large businesses which is supposed to support their work with the whole process of Big Data work. Another goal is to design an effective solution of processing Big Data to gain a competitive advantage in business. The theoretical part of the work processes available scientific literature in the Czech Republic and abroad as well as describes the current state of Competitive Intelligence, and Big Data as one of its possible sources. Subsequently, the work deals with the characteristics of Big Data, the differences from working with common data, the need for a thorough preparation and Big Data applicability for the methods of Competitive Intelligence. The practical part is focused on analysis of Big Data tools available in the market with regard to the whole process from data collection to the analysis report preparation and integration of the entire solution into an automated state. The outcome of this part is the Big Data software toolkit for small and large businesses based on their budget. The final part of the work is devoted to the classification of the most promising business areas, which can benefit from the use of Big Data the most in order to gain competitive advantages and proposes the most effective solution of working with Big Data. Among other benefits of this work are expansion of the range of resources for Competitive Intelligence and in-depth analysis of possibilities of Big Data usage, designed to help professionals make use of this hitherto untapped potential to improve market position, gain new customers and strengthen the existing user base.

Background: Big data analytics (BDA) is important to reduce healthcare costs. However, there are many challenges. The study objective was high performance establishment of interactive BDA platform of hospital system. Methods: A Hadoop/MapReduce framework formed the BDA platform with HBase (NoSQL database) using hospital-specific metadata and file ingestion. Query performance tested with Apache tools in Hadoop’s ecosystem. Results: At optimized iteration, Hadoop distributed file system (HDFS) ingestion required three seconds but HBase required four to twelve hours to complete the Reducer of MapReduce. HBase bulkloads took a week for one billion (10TB) and over two months for three billion (30TB). Simple and complex query results showed about two seconds for one and three billion, respectively. Interpretations: BDA platform of HBase distributed by Hadoop successfully under high performance at large volumes representing the Province’s entire data. Inconsistencies of MapReduce limited operational efficiencies. Importance of the Hadoop/MapReduce on representation of health informatics is further discussed. / Graduate / 0566 / 0769 / 0984 / dillon.chrimes@viha.ca

The fact is that every individual leaves behind vast amounts of data, companies collect this data and use the knowledge gained from it in a variety of ways. One area that is lucrative for the use of Big data is the financial sector. A prominent example of the use of Big data is real-time stock market insights. However, there are still industries in which Big data is not yet used for various reasons. One of these industries is the tax consulting sector, which will be the focus of this research. With its high entry hurdles, direct dependence on the legislator, and the associated atypical data sets, the tax consulting sector represents a special use case within the financial sector.   Because big data has not been used in the tax consulting sector yet and that the setting is atypical compared to other sectors, a closer analysis of potential influences on services, the working environment, and quality is of particular interest here. This analysis is the core of this study and was carried out using an interpretative qualitative approach in the form of a case study. In this case study, the three most important stakeholders of Austrian tax consultancies- employers, employees, and clients - were interviewed on the one hand through interviews and on the other hand through a survey with open-ended questions. The results were then compared in the discussion with the changes that studies in other fields have identified.   The results of the study showed that the stakeholders predominantly assume that the quality of services will improve significantly through the use of big data, especially in accounting and business management services. Stakeholders also predicted a positive development concerning the range of services offered. It was also predicted that the range of services offered could increase on the one hand and that services of a business management nature could benefit enormously on the other. In the area of the working environment, employees said that increased training activity and process adaptation would be the only significant changes. In the area of risks, all three stakeholder groups agreed and mentioned data protection. Interesting differences between the three stakeholder groups were on the one hand that the employers gave very detailed answers, which allows the assumption that they have already thought carefully about the topic of big data. On the other hand, in contrast to the other two groups, the employees did not primarily think of their area (work environment) in the analysis, but of that of the clients and thus of the provision of the service. This underlines the strong focus on client satisfaction and encourages a more intensive involvement in the design process.   In contrast to other studies, this thesis analyses the influences on the areas not from a retrospective point of view, but a prospective point of view. This approach allows an unbiased look at the opinions of stakeholders and thus provides the best possible information for the design of big data tools for the tax consulting sector. Besides, by comparing this with changes found in other studies, it is possible to estimate how the use of big data in the tax consulting sector differs from other sectors.