Applied business analytics approach to IT projects – Methodological framework

The design and implementation of a big data project differs from a typical business intelligence project that might be presented concurrently within the same organization. A big data initiative typically triggers a large scale IT project that is expected to deliver the desired outcomes. The industry has identified two major methodologies for running a data centric project, in particular SEMMA (Sample, Explore, Modify, Model and Assess) and CRISP-DM (Cross Industry Standard Process for Data Mining). More general, the professional organizations PMI (Project Management Institute) and IIBA (International Institute of Business Analysis) have defined their methods for project management and business analysis based on the best current industry practices. However, big data projects place new challenges that are not considered by the existing methodologies. The building of end-to-end big data analytical solution for optimization of the supply chain, pricing and promotion, product launch, shop potential and customer value is facing both business and technical challenges. The most common business challenges are unclear and/or poorly defined business cases; irrelevant data; poor data quality; overlooked data granularity; improper contextualization of data; unprepared or bad prepared data; non-meaningful results; lack of skill set. Some of the technical challenges are related to lag of resources and technology limitations; availability of data sources; storage difficulties; security issues; performance problems; little flexibility; and ineffective DevOps. This paper discusses an applied business analytics approach to IT projects and addresses the above-described aspects. The authors present their work on research and development of new methodological framework and analytical instruments applicable in both business endeavors, and educational initiatives, targeting big data. The proposed framework is based on proprietary methodology and advanced analytics tools. It is focused on the development and the implementation of practical solutions for project managers, business analysts, IT practitioners and Business/Data Analytics students. Under discussion are also the necessary skills and knowledge for the successful big data business analyst, and some of the main organizational and operational aspects of the big data projects, including the continuous model deployment

Business Intelligence and Big Data in Higher Education: Status of a Multi-Year Model Curriculum Development Effort for Business School Undergraduates, MS Graduates, and MBAs

Business intelligence (BI), “big data”, and analytics solutions are being deployed in an increasing number of organizations, yet recent predictions point to severe shortages in the number of graduates prepared to work in the area. New model curriculum is needed that can properly introduce BI and analytics topics into existing curriculum. That curriculum needs to incorporate current big data developments even as new dedicated analytics programs are becoming more prominent throughout the world. This paper contributes to the BI field by providing the first BI model curriculum guidelines. It focuses on adding appropriate elective courses to existing curriculum in order to foster the development of BI skills, knowledge, and experience for undergraduate majors, master of science in business information systems degree students, and MBAs. New curricula must achieve a delicate balance between a topic’s level of coverage that is appropriate to students’ level of expertise and background, and it must reflect industry workforce needs. Our approach to model curriculum development for business intelligence courses follows the structure of Krathwohl’s (2002) revised taxonomy, and we incorporated multi-level feedback from faculty and industry experts. Overall, this was a long-term effort that resulted in model curriculum guidelines

Beyond Dashboards? Designing Data Stories for Effective Use in Business Intelligence and Analytics

With the proliferation of business intelligence & analytics, data storytelling has gained increasing importance to improve communicating analytical insights to business users and support decision-making. While conceptual research on data storytelling suggests that these techniques can help improve decision-making, there is a lack of prescriptive knowledge on how to design data stories in business intelligence & analytics. Moreover, it is not understood how data stories can facilitate effective use and support decision-making of business users. To address this challenge, we conduct a design science research (DSR) project. Drawing on the theory of effective use and data storytelling techniques, we propose three design principles that we instantiate in a prototype. The results of two focus groups indicate that enhancing dashboards with data storytelling techniques increases transparent interaction and representational fidelity. Our DSR project contributes novel design knowledge for data stories that facilitate effective use

Business intelligence-centered software as the main driver to migrate from spreadsheet-based analytics

Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNowadays, companies are handling and managing data in a way that they weren’t ten years ago. The data deluge is, as a mere consequence of that, the constant day-to-day challenge for them - having to create agile and scalable data solutions to tackle this reality. The main trigger of this project was to support the decision-making process of a customer-centered marketing team (called Customer Voice) in the Company X by developing a complete, holistic Business Intelligence solution that goes all the way from ETL processes to data visualizations based on that team’s business needs. Having this context into consideration, the focus of the internship was to make use of BI, ETL techniques to migrate their data stored in spreadsheets — where they performed data analysis — and shift the way they see the data into a more dynamic, sophisticated, and suitable way in order to help them make data-driven strategic decisions. To ensure that there was credibility throughout the development of this project and its subsequent solution, it was necessary to make an exhaustive literature review to help me frame this project in a more realistic and logical way. That being said, this report made use of scientific literature that explained the evolution of the ETL workflows, tools, and limitations across different time periods and generations, how it was transformed from manual to real-time data tasks together with data warehouses, the importance of data quality and, finally, the relevance of ETL processes optimization and new ways of approaching data integrations by using modern, cloud architectures

Perspectives of IR Professionals Regarding the Impact of Data Analytic Systems on Institutional Decision- Making.

The capacity for data analytical decision-making is not always optimal in institutions of higher education (Hawkins & Bailey, 2020). Data analytic decision making for this study is defined as any decision utilized to improve the process or outcome for any function of higher educational administration (Nguyen et al., 2020) including but not limited to: state appropriated funding (e.g. Campbell, 2018) improving graduation rates (e.g Moscoso-Zea, Saa & Luján-Mora, 2019), teacher instruction (e.g. Cai & Zhu, 2015), or student success (e.g. Foster & Francis, 2020). Many IR professionals still face obstacles pertaining to their ability to both utilize data analytical software as well as share data analytical findings across their respective clientele units outside of institutional research to impact institutional decision-making (Lehman, 2017). The literature is lacking concerning how IR professionals experience and navigate these critical aspects of data analytical decision-making support in higher educational institutions. The purpose of this study was to address the gap in the research by assessing the perspectives of IR professionals regarding their ability to utilize data analytic systems (e.g., analyzing, interpreting, sharing of data) to impact and strengthen institutional decision-making. The purpose of this study was also to understand how institutional culture (e.g., policies, operational processes, relevancy, conduciveness) influences the ability of IR professionals to utilize data analytic systems when sharing data findings or collaborating across their respective institutions to enhance institutional decision-making. Recommendations based on the study findings included stronger data governance for dashboards and data visualizations, expanding predictive analytics to enhance student success, and data literacy training with both utilizing data analytics software and interpreting data findings according to the context of individual institutions

Investigating a learning analytics interface for automatically marked programming assessments

Student numbers at the University of Cape Town continue to grow, with an increasing number of students enrolling to study programming courses. With this increase in numbers, it becomes difficult for lecturers to provide individualised feedback on programming assessments submitted by students. To solve this, the university utilises an automatic marking tool for marking assignments and providing feedback. Students can submit assignments and receive instant feedback on marks allocated or errors in their submissions. This tool saves time as lecturers spend less time on marking and provides instant feedback on submitted code, hence providing the student with an opportunity to correct errors in their submitted code. However, most students have identified areas where improvements can be made on the interface between the automatic marker and the submitted programs. This study investigates the potential of creating a learning analytics inspired dashboard interface to improve the feedback provided to students on their submitted programs. A focus group consisting of computer science class representatives was organised, and feedback from this focus group was used to create dashboard mock-ups. These mock-ups were then used to develop high-fidelity learning analytics inspired dashboard prototypes that were tested by first-year computer science students to determine if the interfaces were useful and usable. The prototypes were designed using the Python programming language and Plotly Python library. User-centred design methods were employed by eliciting constant feedback from students during the prototyping and design of the learning analytics inspired interface. A usability study was employed where students were required to use the dashboard and then provide feedback on its use by completing a questionnaire. The questionnaire was designed using Nielsen's Usability Heuristics and AttrakDiff. These methods also assisted in the evaluation of the dashboard design. The research showed that students considered a learning analytics dashboard as an essential tool that could help them as they learn to program. Students found the dashboard useful and had an overall understanding of the specific features they would like to see implemented on a learning analytics inspired dashboard used by the automatic marking tool. Some of the specific features mentioned by students include overall performance, duly performed needed to qualify for exams, highest score, assignment due dates, class average score, and most common errors. This research hopes to provide insight on how automatically marked programming assessments could be displayed to students in a way that supports learning

Delivering value on day one with Business Intelligence: A case study of a European insurer company

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe general direction of an organization is directly impacted by the data-driven decisions made to move the organization towards reaching its goals. This scenario is especially true in ABC Inc., a European insurance company subject to this case study. Although reporting tools have traditionally been used in the company, challenges were identified when monitoring business performance. Namely, insufficient visual capabilities that effectively illustrate the desired metrics and data extraction activities delays and bottlenecks. Therefore, there is a need to implement better analytical visualizations and dashboards that enrich the storytelling, exploring solutions that provide information with real-time performance dashboards. This study uses the Design Science Research (DSR) methodology for Information Systems (IS) to develop a Business Intelligence (BI) dashboard through the Microsoft Power BI tool. The developed dashboard adds value to the company by providing a comprehensive visual analytics solution that effectively communicates a story, presents metrics and KPIs, and supports senior executives in their decision-making. In addition, this solution streamlines obtaining, preparing, and sharing business performance insights, thereby improving efficiency, and bringing agility in decision-making