Case Study: Mikoyan Gurevich $-$ Business Intelligence Implementation Introduction Mikoyan Gurevich, a fictional mid$-$sized technology firm, specializes in advanced aerospace engineering and high$-$tech manufacturing. The company has recently embarked on a journey to integrate business intelligence $($BI$)$ tools to enhance its decision$-$making processes. This case study explores the concepts of data, information, and knowledge within Mikoyan Gurevich, the role of analysts in the Business Analytics $($BA$)$ model, the four information domains of analytical methods, and the creation of absolute knowledge. Data, Information, and Knowledge at Mikoyan Gurevich Data, information, and knowledge form the backbone of Mikoyan Gurevich's BI system. The firm collects vast amounts of data from its manufacturing processes, customer feedback, and market research. This raw data includes metrics like production time, defect rates, and customer satisfaction scores. To transform this data into actionable information, Mikoyan Gurevich applies various BI tools that organize, filter, and analyze the data. For example, production data is analyzed to identify trends in defect rates, which is then used to inform management about potential issues in the production line. This information is further processed to create knowledge, such as understanding the root causes of defects and predicting future production challenges. Thus, Mikoyan Gurevich's decision$-$makers are empowered with knowledge that guides strategic decisions, such as optimizing production processes and improving product quality. The Analyst$$s Role in the Business Analytics Model At Mikoyan Gurevich, analysts play a pivotal role in the BA model by bridging the gap between raw data and strategic decisions. These analysts possess a blend of technical, methodological, and business acumen, enabling them to extract meaningful insights from complex data sets. They are responsible for selecting appropriate analytical methods, ensuring data integrity, and presenting findings in a way that is understandable and actionable for management. $2$ For instance, analysts at Mikoyan Gurevich use predictive analytics to forecast demand for aerospace components. By analyzing historical sales data and market trends, they provide management with projections that inform production planning and inventory management. This proactive approach helps the firm minimize costs associated with overproduction or stockouts. Four Information Domains of Analytical Methods Mikoyan Gurevich's BI system is structured around four key information domains: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. $1.$ Descriptive Analytics Descriptive analytics at Mikoyan Gurevich focuses on summarizing historical data to provide a clear understanding of what has happened in the organization. The firm uses this domain to generate detailed reports on various aspects of its operations, such as production efficiency, sales performance, and customer satisfaction. Application Example: Mikoyan Gurevich uses descriptive analytics to track production line metrics, including daily output, defect rates, and downtime. This data is compiled into dashboards that management reviews to assess the overall performance of the manufacturing process. By understanding these patterns, the company can identify areas of improvement, such as reducing defects or increasing production speed. $2.$ Diagnostic Analytics Diagnostic analytics helps Mikoyan Gurevich understand why certain events occurred by examining the relationships between different data sets. This domain involves drilling down into data to uncover the causes of observed patterns and trends. Application Example: After noticing an increase in product returns, Mikoyan Gurevich's analysts use diagnostic analytics to investigate the root cause. They analyze data from the production process, customer feedback, and supply chain records to identify that a specific batch of materials from a new supplier was defective. This insight allows the firm to address the issue by switching suppliers and implementing stricter quality control measures. $33.$ Predictive Analytics Predictive analytics enables Mikoyan Gurevich to forecast future events by analyzing historical data and identifying trends. This domain is critical for making informed predictions about customer behavior, market demand, and operational risks. Application Example: Mikoyan Gurevich employs predictive analytics to forecast demand for its aerospace components. By analyzing historical sales data, market trends, and economic indicators, the firm can predict future demand levels. This allows them to adjust production schedules, manage inventory, and align supply chain activities to meet anticipated market needs, thereby minimizing waste and maximizing profitability. $4.$ Prescriptive Analytics Prescriptive analytics is the most advanced domain, providing actionable recommendations to optimize decision$-$making.