Relational databases constitute the basis of business intelligence platforms and are the most common type of databases. In such systems, the tables are linked to each other, allowing the users to access related data with ease. Therefore, the primary advantage of the RDBMS is the simplicity of the system, convenience, and ease of use, making it a perfect model for beginners. Relational databases are also associated with increased accuracy, data integrity, normalization methods, and high security, which explain the immense popularity of the system.
Business Intelligence and Business Analytics refer to similar tools of data analysis and interpretation used to improve the company’s business performance. One of the most popular modern business analytics tools is Tableau – a visual platform for data analysis that provides a highly intuitive user experience. It is a commercially-oriented client which helps companies make weighted decisions and analyze their business performance. Similar to many BA tools, it includes various statistical instruments, predictive modeling, and other means to interpret data.
Metadata is a unique report that contains information about actual data in the database. In other words, metadata concerns various descriptive facts about the file, such as size, format, data, author, and other details. Numerous types of metadata cover information not only in databases but also in social media, computer files, and website searches. In other words, metadata is essential to contemporary data analytics, but it does not refer to the actual data in databases.
Data warehousing refers to data management functions that allow experts to extract and structure information and, consequently, analyze it. On the other hand, data mining concerns the process of finding patterns to efficiently store information in a data warehouse. The two techniques are distinct but supplementary in the sense that they are both necessary for data management, including extraction, analysis, and performance improvement.
The first major topic concerning the history of General Motors is the problems that eventually led to the necessity of data integration. After the spin-off of Electronic Data Systems (EDS) Corporation in 1996, GM’s data systems were fragmented and significantly varied across its divisions. The company continued its collaboration with EDS; however, it was challenging for EDS to navigate GM’s internal data management, further complicated by political issues. Various business units had conflicting ideas about the future of the company, and the disparity in IT divisions was slowing down the business development. From these considerations, GM formed Information Systems & Services (IS&S) as a matrix organizational system that could promote data integration. In theory, IS&S allowed business and IT units to focus on local tasks, significantly simplifying the communication process between various divisions. Therefore, the creation of a matrix system could potentially lead to a more efficient data integration system and resolve GM’s problems.
As a result of multiple IT transitions, the GM’s end result focuses on the collaboration among IT innovation, management, and finance units. In 2009, GM opened four additional research centers with emphasis on technology innovation and, thus, selected software development as its competitive advantage. While it might be a productive strategy, the current state of the company is dimmed by bureaucracy and conflicting goals in finance. Nevertheless, the recent addition of Cruise automation and Galileo, which consolidated outsourced data centers into insourced, imply the company’s objective to focus on IT.