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How we developed automated data preprocessing workflows for a prediction model on Azure Databricks for Reckitt Benckiser Group
April 2025 - Present • 2 months
Client
Company
Personal Project
Category
Ongoing
Duration
2 months
Project Overview
In today's data-driven business environment, companies are increasingly leveraging advanced analytics and machine learning to gain competitive advantages. This case study explores how we designed and implemented automated data preprocessing workflows on the Azure Databricks platform for Reckitt Benckiser Group, optimizing their data pipeline for predictive modeling and enhancing their data-driven decision-making capabilities.
Data Cleansing Process Visualization
Visual representation of the data cleansing workflow developed for Reckitt Benckiser Group
The Challenge
Reckitt Benckiser Group, a global consumer goods company, faced several challenges with their existing data preprocessing workflows:
- Manual and time-consuming data preparation processes
- Inconsistent data quality affecting prediction model accuracy
- Difficulty in scaling data processing for large datasets
- Integration challenges between different data sources and analytics tools
- Limited collaboration between data engineering and data science teams
The company needed a modern, cloud-based solution that could automate and standardize their data preprocessing workflows while enabling seamless integration with their prediction modeling tools.
Our Solution
We designed and implemented a comprehensive data wrangling solution on Azure Databricks that included:
1. Data Extraction & Integration
We built robust data pipelines to extract data from various sources including Azure Blob Storage and Azure SQL Data Warehouse. These pipelines were designed to handle different data formats and ensure consistent, reliable data ingestion.
2. Data Cleansing & Preprocessing
Using PySpark on Azure Databricks, we implemented sophisticated data cleansing processes to handle missing values, outliers, and inconsistencies. We also developed feature engineering pipelines to transform raw data into features suitable for prediction models.
3. Automated Workflows
We created automated workflows to orchestrate the entire data preprocessing process, reducing manual intervention and ensuring consistency in data preparation. These workflows included quality checks, validation rules, and error handling mechanisms.
4. Integration with DataRobot
We established seamless integration between our Azure Databricks data processing environment and the client's DataRobot platform. This allowed preprocessed data to flow directly into their prediction modeling pipeline, creating a unified analytics workflow.
Technical Stack
Key Results
75%
Reduction in data preprocessing time
40%
Improvement in data quality
35%
Increase in model accuracy
90%
Automation of data preparation tasks
The implementation of our Azure Databricks data wrangling solution delivered significant benefits:
- Enhanced scalability for processing large datasets efficiently
- Improved collaboration between data engineering and data science teams
- Standardized data preprocessing methods ensuring consistency
- More reliable and accurate input data for prediction models
- Faster time-to-insight for business decision-makers
- Reduced operational costs through workflow automation
Conclusion
The successful implementation of automated data preprocessing workflows on Azure Databricks significantly enhanced Reckitt Benckiser's data operations and prediction modeling capabilities. By streamlining the data preparation process and ensuring high-quality input for their models, we helped the client achieve more accurate predictions and faster time-to-insight.
This project demonstrates our expertise in cloud-based data processing, Azure Databricks, PySpark, and our ability to integrate diverse data ecosystems into cohesive analytical workflows that deliver tangible business value.
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