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Customizable Data Warehouse Implementation for Enhanced Sales Performance and Business Intelligence

future-processing.com

Advertising & marketing

Information technology

Business services

Business Challenges

Existing data storage solutions suffered from high maintenance complexity, inconsistent client-specific versions, slow data processing speeds, and limited integration capabilities for business intelligence tools. Required improved scalability and faster adaptation to client-specific requirements while maintaining system coherence.

About the Client

Leading communication agency specializing in sales performance, business analytics, and training solutions with integrated service divisions focused on cross-channel conversion optimization

Project Goals

Create unified Data Warehouse architecture with client-specific customization capabilities
Optimize data processing performance and system maintainability
Enable seamless integration with BI tools and customer systems
Improve sales team management through enhanced data visibility
Implement automated reporting for faster client responsiveness

Core System Requirements

Multi-source data extraction (SQL Server, Salesforce, Excel)
Configurable ETL transformation workflows
Client-specific schema customization engine
Real-time data integration with QlikSense BI platform
Automated report generation and distribution system

Technology Stack

Microsoft SQL Server

QlikSense

Salesforce Integration Cloud

Python ETL Frameworks

Azure Data Warehouse

System Integrations

CRM systems
BI visualization tools
Cloud storage platforms
Customer-specific data sources

Operational Requirements

Horizontal scalability
Sub-second query response times
Enterprise-grade data encryption
99.9% system uptime SLA
Automated failover capabilities

Expected Business Impact

Anticipated 40-60% reduction in report generation times, 30% improvement in sales team productivity through better data visibility, 50% faster implementation of client-specific requirements, and significant operational cost savings through automated data processing workflows.