This Master Thesis deals with the use of modern information technologies in business man-agement, business data processing and measurement of business performance, in order to achieve better management decisions. This thesis aims to: introduce a business intelligence principle and business performance management within a certain designated area, their us-age in management systems and, last but not least, their usage in creation of modern presentation tools, dashboards. The theoretical part of this thesis is divided into three chapters. The first deals with busi-ness intelligence, which not only explains this concept and the basic principles of this tech-nology, but also the importance of quality in processing data. The next chapter describes business performance management. The subchapters provide information about the types of control systems and their performance, balanced scorecard method, and types of indicators to measure performance. The chapter dealing with management applications for decision support describes how the above-mentioned technologies and processes can be used for managerial praxes. The practical part is divided into two chapters. The first chapter provides a suggestion of indicators for measuring the selling performance of retail companies. The second part deals with a selection of indicators for a prototype of a dashboard, which is incorporated into a Competence Center of Retail Analytics.

The thesis's primary focus is the visualization of data in Business Intelligence with broader ramifications because the framework defined here will be useful for making any design layout. It describes the cognitive aspects of perception for visualization and with this understanding the thesis will be able to summarize the rules which are necessary for right and functional design. The goal of design is to provide the information in the most understandable way. The author has surveyed individuals to find out how people are familiar with the rules for designing and the style of visualization that people prefer. The last part of this thesis is focused on the implementation of the findings with the ultimate goal to suggest a new BI reporting system. Being able to list and verify the rules of visualization on the path to implementation is the main benefit of this work.

The aim of the work is create and describe solution of automation and visualization of project boards in the specific manufacturing company. Methods and tools of Business Intelligence are used in this process. Project board is arranged summary of information, which is part of the monitor, in this context. There is for example information about produced pieces, the cost of poor quality, project Scorecard, downtime analyses, bottleneck of project, and so on. The theoretical part is dealing with description methods and components of Business Intelligence and serves to explanation basic principles on the basis of which are formed data staging area, data warehouse, OLAP cubes and dashboards. The main emphasis is on the practical part, in which is detail describe process of data elaboration from source data, which are taken primarily from MS Excel files, till data visualization in the form of dashboard. As software workspace were selected Microsoft products, specifically SQL Server Management Studio, Visual Studio and Excel which has been mentioned before. The main output is solution that will gradually fully replace paper project boards. This solution also brings both financial and time savings in the creation and managing project boards, because this process is automated.

This thesis solves the area of data visualization in Business Intelligence. We live in a time when the amount of data grows turbulently. These data need to be processed into a form with added value for management and business objectives of the company. The first task is to adjust the data into the required format, the second task is to choose the appropriate visualization so that the data are providing the highest value. This work deals with data visualization and its main aim is to introduce a different approach to data visualization, which has a higher added value than conventional visualization methods used in dashboards and reports. First of all, theoretical part is describing data area (including the trend of Big Data) and reporting (chapter 3). Followed by chapter 4, which is enriching the reader with the information about the need to deal with data visualization and also with the design theory of visualization components. The theoretical part is finished with fifth chapter. It deals with current trends in the field of data visualization and Business Intelligence and also introduces D3.js library as a tool for creating interactive and efficient visualization components. The practical part is offering suggestions for interactive and dynamic visualization of input data (chapter 6). It presents a convenient way to choose a visualization component with greater value than the traditional static component in the form of a graph or table. Proposals contained in D3.js library are presented to the reader. Chosen components according to the author are interesting from the view of design and, if properly used, offer an innovative approach to data visualization. For purposes of practical examples of visualization, author is using D3.js library and open source tool for Business Intelligence from Pentaho. Practical example is part of the final chapter 7.

The aim of this master's thesis is to design methodics for measuring contribution of subsidiaries on economic value added EVA. The next objective is a design of reporting of subsidiaries. The report is in the form of a dashboard to the Chief Financial Officer. The dashboard consists of Financial Performance Indicators FPIs and Key Performance Indicators KPIs. The design is created on the base of financial analysis, analysis of the Economic Value Added EVA and according to recommendations of Business Performance Management, which describes what is best way to obtain KPIs.

Since there is limited research on the use of data dashboards in the evaluation field, this study explores the integration of a data dashboard in a knowledge network, the Centre for Research on Educational and Community Services (CRECS) as part of its program evaluation activities. The study used three phases of data collection and analysis. It investigates the process of designing a dashboard for a knowledge network and the different uses of a data dashboard in a program evaluation context through interviews and focus group discussions. Four members of the CRECS team participated in one focus group; two other members participated in individual interviews. Data were analyzed for thematic patterns. Results indicate that the process of designing a data dashboard consists of five steps that indicate the iterative process of design and the need for sufficient consultations with stakeholders. Moreover, the data dashboard has the potential to be used internally, within CRECS, and externally with other stakeholders. The data dashboard is also believed to be beneficial in program evaluation context as a monitoring tool, for evaluability assessment, and for evaluation capacity building. In addition, it can be used externally for accountability, reporting, and communication. The study sheds light on the potentials of data dashboards in organizations, yet prolonged and broader studies should take place to confirm these uses and their sustainability.

abstract: The purpose of this study was to increase microlearning training module usage and completions by 10–15% over a 30-day period by including evaluation in the design and development of a new microlearning training module in the golf equipment industry. Evaluation was conducted using a bespoke evaluation tool, which was designed and developed using design thinking methodology. The evaluation tool was applied to two previously designed microlearning modules, Driver Distance B and Driver Distance C, both of which served as comparisons for the new module’s completion data. Evaluation reports were generated that informed the development of the new module, named Golf Software. This action research study was grounded in constructivist learning theory, design thinking, and dashboards research. A nested, case study-mixed methods (CS- MM) design and a sequential qualitative to quantitative design were used. Research was conducted with the Knowledge Management Department at Ping, an original golf equipment manufacturer (OEM) in Phoenix, Arizona. Participants included three eLearning Designers, which included the researcher as a participant observer. Qualitative data included interviews, reflective researcher journal, and artifacts such as the new microlearning training module and evaluation reports. Quantitative data included completion numbers collected from the organization’s learning management system (LMS) and email campaign service. Findings from this study were mixed, with the new module’s completion numbers 20.27% greater than Driver Distance C and 7.46% lower than the Driver Distance B. The objective of this study was not met, but outcomes provided valuable information about incorporating evaluation in the Knowledge Management Department’s instructional design process. / Dissertation/Thesis / Doctoral Dissertation Educational Administration and Supervision 2020

Microservice-Architekturen haben sich mittlerweile etabliert und werden von immer mehr Firmen übernommen. Die erhöhte Komplexität der Microservice-Architektur aufgrund der Verteilung des Systems hat jedoch zur Folge, dass die effiziente und erfolgreiche Administration des Systems erschwert wird. Ziel der Arbeit ist es, alle notwendigen Metriken für ein effizientes und erfolgreiches Microservice-Monitoring zu identifizieren und auf Basis dieser Erkenntnisse das Linkerd-Dashboard prototypisch weiterzuentwickeln. Hierfür wurde eine Literaturrecherche durchgeführt. Darüber hinaus wurden Praktiker mittels eines Online-Fragebogens befragt. Abschließend wurde die prototypische Weiterentwicklung mithilfe eines halbstrukturierten Interviews evaluiert. Die Literaturrecherche ergab, dass Central-Processing-Unit (CPU)- und Random-Access-Memory (RAM)-Nutzung, Antwortzeit, Workload, Fehlerrate und Service-Interaktion eine Metrik-Menge sind, mit der Microservice-Architekturen effektiv überwacht werden können. Außerdem konnte konstatiert werden, dass die Darstellung der Metriken hauptsächlich mit Visualisierungen realisiert wird. CPU- und RAM-Auslastung sind eine sinnvolle Erweiterung des Linkerd-Dashboards, da diese in der Literatur sowie im Fragebogen als wichtige Kennzahlen deklariert und alle anderen als essenziell eingestuften Metriken bereits vom Linkerd-Dashboard abgedeckt werden. Der Prototyp wurde als gelungen eingestuft, benötigt aber einige kleinere Verbesserungen der Visualisierung, bevor er in der Produktion eingesetzt werden kann.

<div>Equipment health is the root of productivity and profitability in a company; through the use of machine learning and advancements in computing power, a maintenance strategy known as Predictive Maintenance (PdM) has emerged. The predictive maintenance approach utilizes performance and condition data to forecast necessary machine repairs. Predicting maintenance needs reduces the likelihood of operational errors, aids in the avoidance of production failures, and allows for preplanned outages. The PdM strategy is based on machine-specific data, which proves to be a valuable tool. The machine data provides quantitative proof of operation patterns and production while offering machine health insights that may otherwise go unnoticed.</div><div><br> </div><div>Purdue University's Wade Utility Plant is responsible for providing reliable utility services for the campus community. The Wade Utility Plant has invested in an equipment monitoring system for a thirty-megawatt turbine generator. The equipment monitoring system records operational and performance data as the turbine generator supplies campus with electricity and high-pressure steam. Unplanned and surprise maintenance needs in the turbine generator hinder utility production and lessen the dependability of the system.</div><div><br> </div> The work of this study leverages the turbine generator data the Wade Utility Plant records and stores, to justify equipment care and provide early error detection at an in-house level. The research collects and aggregates operational, monitoring and performance-based data for the turbine generator in Microsoft Excel, creating a dashboard which visually displays and statistically monitors variables for discrepancies. The dashboard records ninety days of data, tracked hourly, determining averages, extrema, and alerting the user as data approaches recommended warning levels. Microsoft Excel offers a low-cost and accessible platform for data collection and analysis providing an adaptable and comprehensible collection of data from a turbine generator. The dashboard offers visual trends, simple statistics, and status updates using 90 days of user selected data. This dashboard offers the ability to forecast maintenance needs, plan work outages, and adjust operations while continuing to provide reliable services that meet Purdue University's utility demands. <br>

Innovation Management Software contains complex data with many different variables. This data is usually presented in tabular form or with isolated graphs that visualize a single independent aspect of a dataset. However, displaying this data with interconnected, interactive charts provide much more flexibility and opportunities for working with and understanding the data. Charts that show multiple aspects of the data at once can help in uncovering hidden relationships between different aspects of the data and in finding new insights that might be difficult to see with the traditional way of displaying data. The size and complexity of the available data also invites analyzing it with machine learning techniques. In this thesis it is first explored how machine learning techniques can be used to gain additional insight from the data and then the results of this investigation are used together with the original data in order to build a prototypical dashboard for exploratory visual data analysis. This dashboard is then evaluated by means of ICE-T heuristics and the results and findings are discussed.