Solving Real Business Problems with Data Science – Case Studies
Table of Contents
- jaro education
- 9, December 2023
- 11:00 am
Data science is an interdisciplinary field that involves extracting insights and knowledge from structured and unstructured data. It combines various techniques from statistics, mathematics, and computer science to analyse and interpret complex datasets. Data science for business has grown in popularity in recent years because it allows firms to make better use of data. Data science is increasingly used by organisations such as hospitals, banks, and universities to assist them with various tasks.Â
If you want to take your career one step ahead and delve into the dynamic world of data-driven decision-making in the world of business, IIM Kozhikode’s Professional Certificate Programme in Data Science for Business Decisions is your gateway to a transformational journey. This data science course gives you the opportunity to realise the full potential of data science for your career and industry by providing training from eminent IIM faculty and industry experts, equipping you with critical and analytical thinking skills.
*data-flair.training
Learn Problem-Solving with Data Science - Case Studies
Case Study 1- E-commerce Personalisation and Recommendation Systems
eBay
eBay uses data science to improve customers’ experiences, which in turn, enhances their search results with recommended products, providing better search quality.
Results
- An increase of 12% in the average order value.
- The platform has experienced a 20% decrease in bounce rates.
- Personalised marketing campaigns have led to an average of 18% higher conversion rate.
Amazon
Amazon takes into account customers’ tastes and preferences while personalising their shopping experience using data science. Also, they use customers’ browsing and purchase history to offer personalised products.
Results
- +29% Increase in the average order value.
- Click-through rate on recommended products increased by 68%.
- Response time reduced by 40%.
2. Case Study 2 - Preventing and Detecting Fraud in the Financial Sector
Capital One
Capital One, with the help of data science fights credit card fraud. The company’s machine-learning models interpret the transaction pattern and history to show alerted activities that might be an act of fraud.
Results
- A 97% fraud detection rate.
- This translated into an annual savings of about $50 million.
- Nearly all Capital One customers are more confident in making their financial transactions.
PayPal
PayPal employs advanced data science techniques to detect and prevent fraudulent transactions in real time. In order to do so, they go through transaction records, user actions and other relevant factors, for instance, network intelligence.
Results
- Zero tolerance for fraudulent transactions, with only 0.1% of cases going unnoticed.
- In one year, users were able to save more than 2 billion dollars that could have been lost in case of unauthorised transactions.
- A detection and response time of milliseconds to fraudulent transactions.
Case Study 3 - Smart Cities and Urban Planning
Singapore
Singapore uses data science to optimise urban planning and public services like managing traffic flow, cutting energy costs, and enhancing the living standards of the residents.
Results
- 25% decrease in traffic congestion at peak hours.
- Reduction of 15% of public building’s energy consumption and street lighting.
- All complaints that have been recorded were resolved in 48 hours on feedback platforms.
Barcelona
Data Science has transformed Barcelona into a smart city. They use data analytics in monitoring and managing services of waste management, parking and public transport.
Results
- Reduction in waste collection frequency by 20% in some areas.
- Reduced time spent searching for parking space by 30%.
- A 10% increase in daily ridership and reduced waiting times for commuters.
Case Study 4 - Healthcare Diagnostics and Treatment Personalisation
PathAI
PathAI utilises machine learning to assist pathologists in diagnosing diseases more accurately. By analysing digitised pathology images, the system can identify patterns and anomalies that might be missed by the human eye.
Results
- Improved diagnostic accuracy by 25%.
- 50% decrease in the time taken to analyse and report findings.
- A 20% reduction in misdiagnoses.
IBM Watson Health
With data science solutions, patients at IBM Watson Health receive personalised diagnostics for improving their health care. Furthermore, it sorts through huge volumes of medical records to bring together similar evidence as it empowers physicians with information for their decision-making.
Results
- 15% increase in the accuracy of cancer diagnosis.
- Time for personalised cancer treatment has been reduced from several weeks to days.
- IBM Watson has witnessed a 30% decrease in medication errors.
Case Study 5 - Environmental Conservation and Data Analysis
WWF
The World Wildlife Fund (WWF) employs data science to support conservation efforts. They use data to track endangered species, monitor deforestation, and combat illegal wildlife trade.
Results
- A 25% increase in the accuracy of endangered species tracking.
- A 20% reduction in illegal logging rates.
- $100 million in donations and grants over the past five years.
NASA
NASA collects and analyses vast amounts of data to better understand Earth’s environment and climate. Their satellite observations, climate models, and data science tools contribute to crucial insights about climate change, weather forecasting, and natural disaster monitoring.
Results
- 0.15°C reduction in the uncertainty of global temperature measurements.
- 95% accuracy in predicting sea level rise.
- 35% increase in the accuracy of hurricane track predictions.
Case Study 6 - Transportation and Route Optimisation
Uber
Uber uses data science for optimising ride-sharing and delivery routes. It takes into account real-time traffic situations, available drivers and passenger needs.
Results
-  Reduction in passengers’ travel time by an average of 20%.
- 30% savings in fuel costs.
- 25% decrease in average passenger waiting time.
Lyft
Lyft also relies on data science to enhance ride-sharing experiences. The prediction analytics of Lyft makes it possible to match a passenger effectively with a driver.
Results
- Average wait time was reduced by 20%.
- The salary of drivers was increased by 15%.
- Peak hours were predicted with 98% accuracy.
Case Study 7 - Predictive Maintenance in the Manufacturing Sector
Siemens
Similarly, another industrial leader– Siemens, follows a path of implementing predictive maintenance through the use of machine learning algorithms. They monitor and analyse data from manufacturing machines to identify wear and tear patterns, as well as schedule maintenance precisely when required.
Results
- 20% decrease in unplanned downtime for Siemens.
- Annual savings of $25 million on maintenance costs.
General Electric (GE)
Through sensor data analysis on devices like jet engines and wind turbines, GE could foresee future maintenance requirements even before breakdowns occur and helps to reduce downtime and maintenance costs.
Results
- 30% decrease in unscheduled maintenance for its aviation division.
- The operational efficiency of wind turbines rose by 15%.
- Â Savings were made in maintenance costs at different departments of GE over a year, amounting to $ 50 million.
Case Study 8 - Optimising Energy Consumption
EnergyOptiUS
Energy OptiUS optimises the energy consumption of commercial buildings and makes use of Data Science to monitor their heating, cooling, and lighting systems.
Results
- 15% reduction in maintenance costs.
- 25% increase in occupant comfort.
CarbonSmart USA
CarbonSmart USA assists businesses in reducing their carbon footprint. They provide actionable insights and recommendations based on data analysis.
Results
- Reduced carbon emissions by 15% in the first year.
- Savings of approximately $5,000,000 per annum through efficient use of energy and waste reduction.
- Improved sustainability scores of businesses by nearly 30%.
Case Study 9 - Customer Service and Natural Language Processing in Customer Support
Zendesk
Zendesk has also applied natural language processing (NLP) to improve its customer support. They have NLP algorithms that can classify the customers’ queries into different categories and direct them accordingly to the appropriate customer service agent.
Results
- Nearly 40% decrease in overall response time and a 25%Â boost in productivity for customer support staff.
- Reduction of support tickets misrouted by 30%
Case Study 10 - Agricultural Yield Prediction
Caterpillar Inc.
Caterpillar Inc. uses machine learning data analysis to support the agriculture industry through the use of heavy machinery. It enables farmers to identify maintenance needs and prevent breakdown costs and thus limit expenses that arise at vital periods.
Results
- Reduction in unplanned equipment downtime by 30%.
- An average reduction of 15% in maintenance costs.
- +10% operational efficiency growth.
John Deere
John Deere predicts crop yields by analysing data from sensors, weather data, and soil conditions, which helps farmers optimise planting and harvesting schedules.
Results
- The use of GMOs results in a 15% increase in crop yields over traditional farming methods.
- 20% decrease in water consumption.
- 25% reduction in the use of chemical fertilisers and pesticides
Thus, data science is on the cutting-edge when it comes to handling significant challenges such as prediction of machine failures, personalisation of healthcare treatment or optimisation of energy consumption.
To begin your enlightening journey into data science for business decisions, IIM Kozhikode’s Professional Certificate Programme in Data Science for Business Decisions offers an excellent opportunity as it empowers individuals to harness the full potential of data, driving innovation and progress in an ever-evolving technological landscape.
Trending Blogs
