Description

Fundamentals of Big Data & Business Analytics

April 2025 Examination

Q1. On  Christmas  Eve  in  2024,  American  Airlines  faced  a  significant  disruption, grounding all flights on a critical travel day. Discuss how descriptive, predictive, and prescriptive analytics can help the airline normalize operations in the aftermath and prevent  similar  incidents  in  the  future.  Highlight  specific  analytical  approaches  to optimize resource allocation, identify potential risks, and enhance operational resilience. Explain how leveraging historical data, real-time monitoring, and forecasting techniques can improve decision-making during such crises.   (10 Marks)

Ans 1.

Introduction

On Christmas Eve in 2024, American Airlines experienced a significant disruption, grounding all flights on a critical travel day, causing chaos for travelers and substantial financial losses for the airline. In such crises, leveraging Big Data and Business Analytics is crucial to restoring normalcy and preventing future occurrences. Descriptive, predictive, and prescriptive analytics play a vital role in analyzing past disruptions, forecasting potential risks, and recommending optimal strategies. By utilizing historical data, real-time monitoring, and forecasting models, American Airlines can enhance decision-making, optimize resource allocation, and strengthen operational resilience. The integration of

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Q2. The fast fashion industry deals with massive and complex datasets originating from multiple sources, such as social media, e-commerce platforms, manufacturing units, and supply chain systems. These datasets are generated at high velocity and in various formats. Discuss how organizations in the fast fashion industry can effectively manage and process this data using big data technologies. Highlight the role of distributed storage systems, stream processing tools, and machine learning techniques in deriving actionable insights from this data. Suggest a framework for integrating structured and unstructured data to optimize inventory, predict trends, and enhance sustainability efforts. (10 Marks)

Ans 2.

Introduction

The fast fashion industry is characterized by rapid production cycles, dynamic consumer preferences, and an extensive global supply chain. With digital transformation, companies now generate vast amounts of data from e-commerce platforms, social media interactions, inventory systems, and manufacturing processes. Managing and analyzing this high-volume, high-velocity, and diverse data is crucial for maintaining a competitive edge. Big data technologies provide an effective way to handle such complex datasets, enabling fashion retailers to optimize inventory, predict emerging trends, and improve sustainability efforts. Key technologies, including distributed storage systems, stream processing tools,

Q3a. Governments strive to reduce income inequality between urban and rural regions. Suggest the types of datasets required to analyze historical trends and disparities. Explain how descriptive analytics can be used to understand regional inequalities and discuss how data visualization tools or techniques can effectively communicate these insights to policymakers (5 Marks)

Ans 3a.

Introduction

Governments strive to bridge income disparities between urban and rural regions by analyzing historical trends and economic inequalities. Data-driven insights can help policymakers understand the root causes of these disparities. By leveraging descriptive analytics and data visualization tools, governments can effectively identify patterns, track

1. The Mumbai city police department is investigating a series of burglaries reported in different neighborhoods over the past six months. They have collected the following data:

• Crime   Locations: GPS   coordinates   of   burglary   incidents,   along   with timestamps.
• Suspect Profiles: Witness descriptions, behavioral patterns, and prior criminal records.
• Social Media Activity: Posts and discussions in local community groups about suspicious activities.
• Environmental Factors: Weather conditions, lighting, and proximity to high- traffic areas during incidents.
• Neighborhood Metrics: Demographics, foot traffic, and socioeconomic data for the affected areas.

Please propose ways to use social media activity to uncover potential suspects or accomplices.

Additionally, suggest predictive analytics techniques to forecast future burglary hotspots and recommend proactive measures for crime prevention. Outline the visualizations that would best support your analysis and assist the police in their investigation   (5 Marks)

Ans 3b.

Introduction

Mumbai police are investigating a series of burglaries and analyzing various datasets, including crime locations, suspect profiles, and social media activity. By utilizing social media analytics and predictive models, law enforcement can uncover potential suspects, anticipate future burglary hotspots, and implement proactive measures to enhance crime