Skills Requirements for Business Intelligence, Business Analytics, Big Data Analytics, and Data Science: An Analysis of South African Professionals

This study aimed at investigating the requisite skills and their overlaps for Business Intelligence (BI), Business Analytics (BA), Big Data Analytics (BDA), and Data Science (DS) professionals in South Africa. Studies on benefits, challenges, and concepts on BI and Analytics have been conducted, but few contributions have been made on requisite skills for BI, BA, BDA, and DS. A multi-method approach was adopted using firstly online job advertisement analysis followed by the Delphi technique. A total of 482 online job advertisements were collected for analysis by using LinkedIn and Indeed.com. Descriptive analysis was used to analyze data from online job advertisements. Thematic analysis was used for the categorization of skills. For the Delphi technique, the final number of data professionals were 21 (7 experts from BI, 7 from BA/BDA and 7 from DS). Findings show that data professionals are required to have skills in programming languages such as SQL, Python, R, and Java, specifically for BA, BDA, and DS. They are required to be holders of a degree in Information Systems (IS) or Computer Science or Engineering. The significant skills categories which were shown as needed across all the domains were Data manipulation and processing (Manipulate data using SQL, Transform data from different sources and load it to extract insights (ETL), Formulate validation strategies and methods to ensure accurate and reliable data, Ensure correct data and error handling, Perform data analysis and validation, Soft skills (Communicate effectively, verbally and in writing, Exhibit time management skills, Prioritize workload and work well under pressure, Demonstrate problem-solving skills, Demonstrate excellent analytical skills, Be able to work independently, Apply creativity and innovation), Designing and Coding (Apply agile development processes to achieve outstanding data solutions, Perform data modelling with SQL), and Reporting (Extract insights and trends from data). This study contributes to descriptive knowledge by providing insight into BI, BA, BDA, and DS requisite skills in South Africa

High-Order, Implicit Time Integration of Discrete, Chaotic Dynamical Systems

A wide range of implicit time integration methods, including multi-step, implicit Runge-Kutta, and Galerkin finite-time element schemes, is evaluated in the context of chaotic dynamical systems. The schemes are applied to solve the Lorenz equations, the equation of motion of a Duffing oscillator, and the Kuramoto-Sivashinsky system, with the goal of finding the most computationally efficient method that results in the least expensive model for a chosen level of accuracy. It is found that the quasi-period of a chaotic system strongly limits the time-step size that can be used in the simulations, and all schemes fail once the time-step size reaches a significant fraction of that period. In these conditions, the computational cost per time-step becomes one of the most important factors determining the efficiency of the schemes. The cheaper, second-order schemes are shown to have an advantage over the higher-order schemes at large time-step sizes, with one possible exception being the fourth-order continuous Galerkin scheme. The higher-order schemes become more efficient than the lower-order schemes as accuracy requirements tighten. If going beyond the second-order is necessary for reasons other than computational efficiency, the fourth-order methods are shown to perform better than the third-order ones at all time-step sizes.Comment: 38 pages, 15 figure

Implementing an SQL Based ETL Platform for Business Intelligence Solution

Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe exponential growth and development of information technology in the last twenty years has compelled most industry segments to shift from focusing on core business to adopting digitally sophisticated and data-driven processes. Those who have followed its growth have benefited, but unfortunately, just a small percentage of them do. Having information systems that just hold a vast volume of data is no longer sufficient for businesses. To gain a competitive advantage, these businesses must make well-informed decisions. Every firm, regardless of industry, has access to a wealth of data that it can utilize to its advantage. This is where Business Intelligence comes in. Business intelligence enables these companies to make better use of their data by providing previously unusable data in an intelligible and interpretable format. This internship report aims to cover the development of the data warehousing and data analytics for HROps, a product owned by BI4ALL. HROps is being developed with the goal of facilitating, centralizing, and making people management processes in organizations more efficient. I will be working on a low-cost SQL based ETL Framework using T-SQL for developing standard ETL processes. I will also be working and creating Power BI dashboards and reports to gather useful information from the data collected

Development of a Database-Driven Management System for Retail Food Packaging Eye Tracking Studies

Within the past three years, approximately two hundred one-off studies have occurred in developing eye tracking methodologies for quantifying consumer attention on packaging design preference. Due to restricted time and resources, most research analyzes consumer attention regarding a relatively small amount of products within specific planogram. However, larger questions concerning category trends and insights are not possible with these smaller, one-off studies. Broad data aggregation and analysis is important for understanding packaging design per product category in order to understand category-wide design trends and insights. Considering the excessive effort in manual data retrieval, analysis, and reporting, a method that can improve efficiency and data aggregation would be of tremendous benefit to all researchers studying consumer behavior on relevant product categories. Ultimately, the application of the relational database management system fits this need. The work herein describes a procedure of developing a database-driven management system for retail food packaging eye tracking studies and the data analytics. A relational database of eye tracking studies associated with a web portal was designed and created to aggregate, store, access, share, and analyze eye tracking data based on studies in an immersive retail environment. The comparison between this system and the file-based methods was discussed, includes eye tracking study procedure, data usage efficiency, and data analysis methodology. This body of work covers 14 eye tracking projects in aggregate, involving 34 planograms comprised of a total of 421 consumer product goods. When using this database-driven system compared to the traditional file-based process in terms of time, it was found that the developed system reduced the time of the eye tracking study process by 48%

DESIGNING DISTRIBUTED CONTROLLING TESTBED SYSTEM FOR SUPPLY CHAIN AND LOGISTICS IN AUTOMOTIVE INDUSTRY

The arrival of the era of autonomous robots is indisputable. In this paper, innovations in the distributed control systems realized by autonomous guided vehicles in the automotive industry are provided as proof of concept. The main goal of the considered distributed control system design is to bring all-in-one dependent and independent VDA 5050 compliant robots that are easily configurable and manageable with the web-based high-quality user interface responsive business-critical application. Special attention is paid to applying a platform to manage all autonomous IoT based robots in one seamless system. In addition, a "single point of truth" as one of the main issues of modern distributed controlled systems has been considered.