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Development of an Enterprise Data Analytics and Marketing Automation Platform
  1. case
  2. Development of an Enterprise Data Analytics and Marketing Automation Platform

Development of an Enterprise Data Analytics and Marketing Automation Platform

kandasoft.com
Financial services
Business services

Identifying the Challenges Faced by a Data-Driven Financial Services Firm

The client faces difficulties in integrating vast and complex data sources into a unified, high-performance system that supports real-time querying and analysis. Existing tools are slow, offline-intensive, and unable to handle query speeds of less than a second on databases with millions of records, limiting timely decision-making and customer engagement. Additionally, there is a need for scalable, interactive interfaces that support multiple users and complex analytical features within a distributed environment.

About the Client

A large financial services firm seeking to enhance its data management, client relationship, and marketing automation capabilities to improve operational efficiency and business growth.

Goals for Building an Advanced Data and Marketing Automation Solution

  • Develop a rapid, fully compliant ODBC driver and SQL Translator for seamless data integration and querying.
  • Create an optimized internal analytics engine capable of executing queries on large datasets (up to 10 million records) within less than one second on average.
  • Implement a customizable, user-friendly front-end platform with interactive features such as Family Trees and Campaign Management.
  • Support distributed, multi-client, multi-threaded server environments to ensure high availability and performance.
  • Design and deploy full-text search capabilities across extensive corporate databases, enabling precise, criteria-based queries.
  • Build web-based portals for industry risk assessment and peer trade analysis with support for community features and regular data updates.
  • Ensure the system architecture is scalable, secure, and capable of handling increasing data volume and user load.

Core Functional System Features for Data Management and Analytics

  • Fully compliant ODBC driver facilitating seamless database connectivity.
  • SQL translator tailored to internal data processing engines.
  • Customized analytics engine optimized for large-scale datasets with sub-second query performance.
  • Enhanced front-end user interface with clean design, supporting complex queries and interactive features.
  • Distributed server architecture supporting multiple clients and multithreading for performance scalability.
  • Full-text search service capable of indexing and querying over 20 million records based on multiple criteria.
  • Web portals enabling risk analysis, trade peer insights, and community engagement, with support for scheduled data updates.

Technology Stack and Architectural Best Practices

SQL Server or equivalent RDBMS for data storage
ODBC driver architecture for database connectivity
SQL translation layer for optimized query execution
Distributed multi-threaded server infrastructure
ASP.NET or similar web framework for portal development
MS SQL Server for web portals and data marts

Essential External System Integrations

  • Match algorithms for data matching and deduplication
  • Search software for full-text indexing
  • Proprietary or external data sources for comprehensive data enrichment
  • Customer relationship management (CRM) systems
  • Content management systems for portal updates

Performance, Security, and Scalability Criteria

  • Query response time of less than 1 second for datasets up to 10 million records
  • Support for distributed, multi-client environments with high concurrency
  • System availability with 99.9% uptime
  • Data security and access controls compliant with industry standards
  • Scalable architecture supporting future data volume increases

Anticipated Business Benefits and Value Proposition

The implementation of this enterprise data analytics and marketing automation platform is expected to significantly enhance query performance, reducing analysis times from over 20 minutes to under a second. It will enable the client to deliver timely insights, improve client relationship management, and generate increased revenue streams—potentially making the software solution a major revenue driver for the organization over multiple years. The scalable, high-performance architecture will support ongoing growth and data complexity, ensuring long-term competitive advantage.

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