A framework for the design of business intelligence dashboard tools

Vast amounts of data are collected on a daily basis, making it difficult for humans to derive at valuable information to make effective decisions. In recent years, the field of Business Intelligence (BI) and Information Visualisation (IV) have become a key driver of an organisation’s success. BI tools supporting decision making need to be accessible to a larger audience on different levels of the organisation. The problem is that non-expert users, or novice users, of BI tools do not have the technical knowledge to conduct data analysis and often rely on expert users to assist. For this reason, BI vendors are shifting their focus to self-service BI, a relatively new term where novice users can analyse data without the traditional human mediator. Despite the proliferation of self-service BI tools, limited research is available on their usability and design considerations to assist novice users with decision making and BI analysis. The contribution of this study is a conceptual framework for designing, evaluating or selecting BI tools that support non-expert users to create dashboards (the BI Framework). A dashboard is a particular IV technique that enables users to view critical information at a glance. The main research problem addressed by this study is that non-expert users often have to utilise a number of software tools to conduct data analysis and to develop visualisations, such as BI dashboards. The research problem was further investigated by following a two-step approach. The first approach was to investigate existing problems by using an in-depth literature review in the fields of BI and IV. The second approach was to conduct a field study (Field Study 1) using a development environment consisting of a number of software components of which SAP Xcelsius was the main BI tool used to create a dashboard. The aim of the field study was to compare the identified problems and requirements with those found in literature. The results of the problem analysis revealed a number of problems in terms of BI software. One of the major problems is that BI tools do not adequately guide users through a logical process to conduct data analysis. In addition, the process becomes increasingly difficult when several BI tools are involved that need to be integrated. The results showed positive aspects when the data was mapped to a visualisation, which increased the users’ understanding of data they were analysing. The results were verified in a focus group discussion and were used to establish an initial set of problems and requirements, which were then synthesised with the problems and requirements identified from literature. Once the major problems were verified, a framework was established to guide the design of BI dashboard tools for novice users. The framework includes a set of design guidelines and usability evaluation criteria for BI tools. An extant systems analysis was conducted using BI tools to compare the advantages and disadvantages. The results revealed that a number of tools could be used by non-experts, however, their usability hinders users. All the participants used in all field studies and evaluations were Computer Science (CS) and Information Systems (IS) students. Participants were specially sourced from a higher education institution such as the Nelson Mandela Metropolitan University (NMMU). A second field study (Field Study 2) was conducted with participants using another traditional BI tool identified from the extant systems analysis, PowerPivot. The objective of this field study was to verify the design guidelines and related features that served as a BI Scorecard that can be used to select BI tools. Another BI tool, Tableau, was used for the final evaluation. The final evaluation was conducted with a large participant sample consisting of IS students in their second and third year of study. The results for the two groups revealed a significant difference between participants’ education levels and the usability ratings of Tableau. Additionally, the results indicated a significant relationship between the participants’ experience level and the usability ratings of Tableau. The usability ratings of Tableau were mostly positive and the results revealed that participants found the tool easy to use, flexible and efficient. The proposed BI Framework can be used to assist organisations when evaluating BI tools for adoption. Furthermore, designers of BI tools can use the framework to improve the usability of these tools, reduce the workload for users when creating dashboards, and increase the effectiveness and efficiency of decision support

Acquisition Data Analytics for Supply Chain Cybersecurity

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsCybersecurity is a national priority, but the analysis required for acquisition personnel to objectively assess the integrity of the supply chain for cyber compromise is highly complex. This paper presents a process for supply chain data analytics for acquisition decision makers, addressing data collection, assessment, and reporting. The method includes workflows from initial purchase request through vendor selection and maintenance to audits across the lifecycle of an asset. Artificial intelligence can help acquisition decision makers automate the complexity of supply chain information assurance.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Using learning analytics to understand esports students

Electronic sports (esports) has advanced to become a media giant and an arena for competitions and career development. Due to this growth, more focus has been given to esports research, implementation of esports throughout the world, and development of esports curriculum. Introducing esports into schools has created huge opportunities for deeper analysis of esport and learning data to provide insight into the learning processes. By applying learning analytics methods, this research analyzes data that originate from students (N=149) in Swedish high schools. The data was divided between activity data and performance data. The analysis is guided by the learning theory concept self-regulation to analyze differences between user groups. Through exploratory analysis, multiple user groups were identified and then compared in their trends and results to measure the impact of self-regulated learning concepts. Furthermore, the student data was used in the design of a mid-fidelity prototype for a student-facing dashboard to provide feedback and recommendations. Findings reveal that concepts of self-regulated learning have a positive impact in terms of higher curriculum interaction, and also higher performance results in game matches. While the research finds that focus on features promoting self-regulated learning concepts is important, it is challenging to generalize the findings to recommend actions such as suggested session lengths. Future work should include a larger population sample and focus on the implementation of a student-facing dashboard tool to test its reception and usage.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Interactive Data Analysis with Next-step Natural Language Query Recommendation

Natural language interfaces (NLIs) provide users with a convenient way to interactively analyze data through natural language queries. Nevertheless, interactive data analysis is a demanding process, especially for novice data analysts. When exploring large and complex SQL databases from different domains, data analysts do not necessarily have sufficient knowledge about different data tables and application domains. It makes them unable to systematically elicit a series of topically-related and meaningful queries for insight discovery in target domains. We develop a NLI with a step-wise query recommendation module to assist users in choosing appropriate next-step exploration actions. The system adopts a data-driven approach to suggest semantically relevant and context-aware queries for application domains of users' interest based on their query logs. Also, the system helps users organize query histories and results into a dashboard to communicate the discovered data insights. With a comparative user study, we show that our system can facilitate a more effective and systematic data analysis process than a baseline without the recommendation module.Comment: 14 pages, 6 figure

Managing Supply Chains Using Business Intelligence

Organizations are deploying business intelligence (BI) systems to enable analysis of data assets for establishing management decisions. Corporate data captured using enterprise systems (ESs) are leveraged through BI to evaluate digital information for deploying business strategies. This study investigates use of BI in organizations for managing supply chain operations. The current BI practices of manufacturing firms are evaluated for transforming transactional data captured through ESs into organizational knowledge in pursuit of realizing supply chain goals. Findings from a case study reveals that although manufacturing firms have identified business analytic as one of the major necessities for organizational effectiveness, these companies often lack clarity in aligning key measurable against their business processes to utilize vital ES data. This results in underutilization of BI tools and the data assets for establishing business decisions. However, more and more companies are now deploying BI strategies for impromptu decision making in managing supply chains.falsePublishedAuckland, New Zealan

Higher Education Meets Business Intelligence

Abstract In an ever-changing market powered by user satisfaction and financial success, Higher Education institutions must focus on data analytics to improve student satisfaction and business processes. This project underlines the importance of using a powerful data analytics tool to accomplish these goals. Many Higher Education institutions already collect the necessary data in order to predict and determine key changes but still pull this information from multiple databases in individual reports without overlapping benefit or any level of efficiency. The previous systems increase the risk of user error and limit the ability for multiple departments to collaborate and gain insights found through the combination of reports pulled from a campus-wide data source. Through a review of case studies and hands-on use of IBM Cognos data analytics tool, this study addresses the already acknowledged, and also personally obtained, benefits of Business Intelligence in real world scenarios unique to Higher Education. Exceptional data management and accessibility create opportunities for improved student retention rates leading to stronger departments and higher graduation rates. While improving student retention, student satisfaction increases and the institution often attracts more motivated and qualified students experiencing an increase in admission rates. Many Higher Education Institutions are also using Business Intelligence (BI) tools to pull reports leading to options for overall cost reduction. These cuts come in the form of smarter buildings and also fewer professionals needed for creating the BI reports. This project includes the following sections: Introduction, Background, Statement of the Problem, Business Component, Technology Component, Results, and Conclusion

A framework for the design of business intelligence dashboard tools

Vast amounts of data are collected on a daily basis, making it difficult for humans to derive at valuable information to make effective decisions. In recent years, the field of Business Intelligence (BI) and Information Visualisation (IV) have become a key driver of an organisation’s success. BI tools supporting decision making need to be accessible to a larger audience on different levels of the organisation. The problem is that non-expert users, or novice users, of BI tools do not have the technical knowledge to conduct data analysis and often rely on expert users to assist. For this reason, BI vendors are shifting their focus to self-service BI, a relatively new term where novice users can analyse data without the traditional human mediator. Despite the proliferation of self-service BI tools, limited research is available on their usability and design considerations to assist novice users with decision making and BI analysis. The contribution of this study is a conceptual framework for designing, evaluating or selecting BI tools that support non-expert users to create dashboards (the BI Framework). A dashboard is a particular IV technique that enables users to view critical information at a glance. The main research problem addressed by this study is that non-expert users often have to utilise a number of software tools to conduct data analysis and to develop visualisations, such as BI dashboards. The research problem was further investigated by following a two-step approach. The first approach was to investigate existing problems by using an in-depth literature review in the fields of BI and IV. The second approach was to conduct a field study (Field Study 1) using a development environment consisting of a number of software components of which SAP Xcelsius was the main BI tool used to create a dashboard. The aim of the field study was to compare the identified problems and requirements with those found in literature. The results of the problem analysis revealed a number of problems in terms of BI software. One of the major problems is that BI tools do not adequately guide users through a logical process to conduct data analysis. In addition, the process becomes increasingly difficult when several BI tools are involved that need to be integrated. The results showed positive aspects when the data was mapped to a visualisation, which increased the users’ understanding of data they were analysing. The results were verified in a focus group discussion and were used to establish an initial set of problems and requirements, which were then synthesised with the problems and requirements identified from literature. Once the major problems were verified, a framework was established to guide the design of BI dashboard tools for novice users. The framework includes a set of design guidelines and usability evaluation criteria for BI tools. An extant systems analysis was conducted using BI tools to compare the advantages and disadvantages. The results revealed that a number of tools could be used by non-experts, however, their usability hinders users. All the participants used in all field studies and evaluations were Computer Science (CS) and Information Systems (IS) students. Participants were specially sourced from a higher education institution such as the Nelson Mandela Metropolitan University (NMMU). A second field study (Field Study 2) was conducted with participants using another traditional BI tool identified from the extant systems analysis, PowerPivot. The objective of this field study was to verify the design guidelines and related features that served as a BI Scorecard that can be used to select BI tools. Another BI tool, Tableau, was used for the final evaluation. The final evaluation was conducted with a large participant sample consisting of IS students in their second and third year of study. The results for the two groups revealed a significant difference between participants’ education levels and the usability ratings of Tableau. Additionally, the results indicated a significant relationship between the participants’ experience level and the usability ratings of Tableau. The usability ratings of Tableau were mostly positive and the results revealed that participants found the tool easy to use, flexible and efficient. The proposed BI Framework can be used to assist organisations when evaluating BI tools for adoption. Furthermore, designers of BI tools can use the framework to improve the usability of these tools, reduce the workload for users when creating dashboards, and increase the effectiveness and efficiency of decision support

USING THE MUTUAL INFORMATION METRIC TO IMPROVE ACCESSIBILITY AND UNDERSTANDABILITY IN BUSINESS INTELLIGENCE TOOLS

The rapidly-growing organizational data resources introduce a growing difficulty to locate and understand the relevant data subsets within large datasets – what can be seen as a severe information quality issue in today\u27s decision-support environments. The study proposes a quantitative methodology, based on the mutual-information metric, for assessing the relative importance of different data subsets within a large dataset. Such assessments can grant the end-user with faster access to relevant subsets within a large dataset, the ability to better understandits contents, and gain deeper insights from analyzing it – e.g., when such a dataset is being used for Business Intelligence (BI) applications. This manuscript provides the background and the motivation for integrating the proposed assessments of relative importance. It then defines the calculations behind the mutualinformation metric, and demonstrates their applications using illustrative examples