Case Studies

Real-World Data & Digital Solutions in Action

At Katto Corporation, we apply data engineering, analytics, and automation to solve complex business challenges. Below are selected projects that demonstrate our technical expertise and strategic execution.

- Jane Smith

Business Intelligence & Data Visualization

Challenge
Transforming complex datasets into actionable business insights.

Solution
Designed interactive dashboards analyzing sales performance, customer segmentation, and global data trends.

Technologies Used
Power BI • Tableau • Looker • Data Visualization • Analytics

Impact
Converted raw data into intuitive visual reports supporting strategic decision-making.

Data Pipeline & Analytics on GCP

Challenge
Organizations often struggle with fragmented data sources and manual reporting processes.

Solution
Designed and implemented an end-to-end ETL pipeline using Google Cloud Platform, including BigQuery, Data Fusion, and Looker. Automated data ingestion, transformation, and visualization workflows.

Technologies Used
Python • SQL • BigQuery • Google Data Fusion • Looker

Impact
Enabled scalable data processing and real-time reporting capabilities, improving operational visibility and decision-making efficiency.

Sentiment Analysis – Social Media Data

Challenge
Understanding public sentiment from unstructured social media data.

Solution
Built a sentiment analysis system using Python, Tweepy, and TextBlob to classify tweets and extract insights from social conversations.

Technologies Used
Python • NLP • Twitter API • TextBlob

Impact
Delivered structured sentiment insights from unstructured data, enabling better understanding of trends and audience perception.

Machine Learning Model – Health Data Classification

Challenge
Classifying medical data accurately at scale requires efficient distributed processing and feature engineering.

Solution
Developed a predictive classification model using PySpark to analyze large datasets and determine hepatitis status. Implemented full pipeline including data cleaning, feature engineering, model training, and evaluation.

Technologies Used
PySpark • Machine Learning • Python

Impact
Demonstrated scalable ML workflow capable of handling large-volume datasets while maintaining strong predictive performance.