20  Project

20.1 Learning Objectives

By completing this project, you will be able to:

  • Design and implement a customer data integration solution across multiple systems
  • Apply data quality assessment and improvement techniques
  • Resolve customer identity matching challenges
  • Create unified customer views for business analytics
  • Document data lineage and transformation processes
  • Present technical solutions to business stakeholders

20.2 Business Scenario

Company: RetailMax - A mid-sized omnichannel retailer
Challenge: Customer data scattered across multiple systems creating a fragmented customer experience

Background

RetailMax operates both physical stores and an e-commerce platform, serving customers through multiple touch points. Currently, customer data is stored in separate systems that do not communicate effectively, leading to:

  • Inconsistent customer experiences across channels
  • Duplicate marketing communications
  • Inability to track complete customer journey
  • Missed cross-selling and up-selling opportunities
  • Difficulty measuring customer lifetime value
  • Compliance challenges for data privacy regulations

Current System Landscape

E-commerce platform (online store)

  • Customer accounts with email registration
  • Purchase history and browsing behavior
  • Shopping cart abandonment data
  • Product reviews and ratings
  • Digital marketing interaction data

Point-of-sale (POS) system (physical stores)

  • In-store transaction records
  • Loyalty card program data
  • Store employee interaction notes
  • Return and exchange records
  • Payment method preferences

Customer service platform

  • Support ticket history
  • Live chat transcripts
  • Phone call logs and resolutions
  • Customer satisfaction surveys
  • Complaint categories and resolutions

Marketing automation system

  • Email campaign engagement data
  • Social media interactions
  • Newsletter subscriptions
  • Marketing preferences and opt-outs
  • Lead scoring and conversion tracking

External data — demographics service

  • Age, income, and lifestyle estimates
  • Geographic and demographic segments
  • Purchase propensity scores
  • Competitive shopping behavior insights

20.3 Project Tasks

Part 1: Data Analysis and Quality Assessment

Task 1.1: Data exploration

Analyze the provided sample data sets to understand:

  • Data structure and formats across systems
  • Volume and variety of customer information
  • Overlapping and unique data elements
  • Data quality issues and inconsistencies

Deliverables

  • Data profiling report for each source system
  • Identification of data quality issues with specific examples
  • Analysis of data overlap and gaps between systems

Task 1.2: Customer identity challenges

Investigate customer identity resolution problems:

  • Identify different customer identifiers used across systems
  • Analyze duplicate customer records within and across systems
  • Assess name variations, email changes, and address updates
  • Evaluate fuzzy matching requirements

Deliverables

  • Customer identity analysis report
  • Proposed identity resolution strategy
  • Estimation of duplicate rates and matching confidence levels

Part 2: Integration Architecture Design

Task 2.1: Architecture planning

Design the overall integration architecture:

  • Choose appropriate integration pattern (ETL vs ELT)
  • Define data flow and transformation processes
  • Plan for real-time vs batch processing requirements
  • Design error handling and data quality controls

Deliverables

  • Architecture diagram with data flows
  • Technology stack recommendations with justifications
  • Processing schedule and dependency mapping

Task 2.2: Data model design

Create the unified customer data model:

  • Design customer master record structure
  • Define relationship structures for hierarchical data
  • Plan for historical data preservation
  • Consider scalability and performance requirements

Deliverables

  • Logical data model diagram
  • Physical database schema design
  • Data dictionary with business definitions

Part 3: Implementation

Task 3.1: Data integration pipeline

Implement the integration solution using Python/SQL:

  • Extract data from multiple source formats
  • Apply data cleansing and standardization rules
  • Implement customer matching and deduplication logic
  • Load data into unified customer repository

Technical Requirements

  • Handle at least 3 different data formats (CSV, JSON, database)
  • Implement fuzzy string matching for customer names
  • Create data quality metrics and monitoring
  • Generate processing logs and error reports

Task 3.2: Customer matching algorithm

Develop sophisticated customer identity resolution:

  • Implement multi-field matching criteria
  • Handle variations in names, addresses, and contact information
  • Create confidence scoring for matches
  • Manage edge cases and manual review processes

Deliverables

  • Working Python code with comprehensive comments
  • Unit tests for critical functions
  • Sample input and output datasets
  • Performance benchmarks and optimization notes

Part 4: Business Value Demonstration

Task 4.1: Analytics and insights

Create business intelligence capabilities:

  • Customer 360-degree view dashboard
  • Customer lifetime value calculations
  • Cross-channel behavior analysis
  • Segmentation and targeting insights

Task 4.2: Use case implementation

Demonstrate practical business applications:

  • Personalized marketing campaign targeting
  • Customer service interaction history
  • Inventory recommendations based on customer preferences
  • Churn prediction and retention strategies

Deliverables

  • Interactive dashboard or reports
  • Business case study with ROI analysis
  • Recommendations for business process improvements

Part 5: Governance and Compliance

Task 5.1: Data lineage documentation

Document the complete data transformation process:

  • Source-to-target field mappings
  • Transformation logic and business rules
  • Data quality checks and validation procedures
  • Error handling and exception management

Task 5.2: Privacy and compliance

Address data privacy and regulatory requirements:

  • GDPR/CCPA compliance considerations
  • Data retention and deletion policies
  • Access control and audit trail requirements
  • Data masking and anonymization strategies

Deliverables

  • Data lineage diagram and documentation
  • Privacy impact assessment
  • Compliance checklist and recommendations

20.4 Technical Requirements

You will receive realistic sample data sets containing:

  • 10,000+ customer records across all systems
  • Intentional data quality issues and duplicates
  • Various data formats and structures
  • Simulated personally identifiable information (PII)

The solution needs to meet the following performance requirements:

  • Process complete data in under 5 minutes
  • Achieve >95% customer matching accuracy
  • Handle incremental updates efficiently
  • Generate reports and dashboards interactively

20.5 Submission Requirements

Technical Report

Executive Summary: Business problem, solution approach, and key results
Technical Architecture: Detailed design decisions and rationale
Implementation Details: Code structure, algorithms, and optimization techniques
Results Analysis: Data quality improvements, matching accuracy, performance metrics
Business Impact: ROI analysis, process improvements, strategic recommendations
Lessons Learned: Challenges encountered, solutions implemented, future enhancements

Source Code Package

Main Integration Script: Complete working implementation
Supporting Modules: Data quality, matching algorithms, utility functions
Configuration Files: Database connections, processing parameters
Test Suite: Unit tests and integration tests
Documentation: Code comments, README file, setup instructions

Visual Deliverables

Architecture Diagrams: System overview, data flows, technology stack
Data Model Diagrams: Logical and physical database designs
Dashboard Screenshots: Customer analytics and business insights
Data Lineage Maps: Transformation process documentation

Presentation

Business Context: Problem statement and stakeholder impact
Technical Solution: Architecture overview and key implementation decisions
Results Demonstration: Live demo of working system and insights
Business Value: ROI analysis and strategic recommendations
Future Roadmap: Scalability plans and enhancement opportunities

20.6 Assessment Categories

Technical Excellence

  • Code Quality: Clean, documented, maintainable code
  • Algorithm Effectiveness: Accurate customer matching and data quality
  • Architecture Design: Scalable, robust, well-designed solution
  • Performance: Efficient processing and resource utilization

Business Understanding

  • Problem Analysis: Clear understanding of business challenges
  • Solution Relevance: Practical, implementable recommendations
  • Value Demonstration: Quantified business benefits and ROI
  • Stakeholder Communication: Clear presentation to business audience

Data Management

  • Data Quality: Effective cleansing and validation processes
  • Integration Approach: Appropriate techniques for data combination
  • Governance: Proper documentation and compliance considerations
  • Lineage Tracking: Complete transformation documentation

Innovation and Insights

  • Creative Solutions: Novel approaches to challenging problems
  • Advanced Analytics: Sophisticated customer insights and predictions
  • Technology Usage: Effective leverage of tools and techniques
  • Strategic Thinking: Forward-looking recommendations and scalability