Skip to content Skip to footer

Predictive Census – Modernization & Optimization

Business Challenge

The existing Predictive Census application had several critical limitations.

Complex and Fragmented Architecture

Data pipelines are complex with significant ETL logic buried in MongoDB, leading to maintenance challenges and potential for errors. Multiple technologies and teams are involved, hindering efficient development and support.

Limited Scalability and Agility

The current architecture and infrastructure limits scalability and flexibility.

Operational Inefficiencies

Manual processes and lack of automation lead to increased operational overhead and potential for human error. Inadequate monitoring and alerting capabilities hinder proactive issue resolution.

Integration Challenges

Integration with other client systems is complex and requires significant efforts. Difficulty in adapting to evolving client needs and regulatory requirements.

Advisory & Engineering Approach

NStarX proposed a phased approach to re-platform and enhance the Predictive Census application:

Phase 1 - Discovery and Design

Conduct in-depth analysis of existing architecture, data flows, and business requirements. Evaluate potential target architectures, considering factors such as scalability, cost-effectiveness, and integration with Azure. Develop detailed design specifications for the re-platformed system, including data pipelines, ML infrastructure, and operational processes.

Phase 2 - Development and Implementation

Migrate data and application to the Azure platform. Develop and deploy new data pipelines and ML models using Azure services. Implement robust monitoring, alerting, and logging capabilities. Integrate Predictive Census with other client's systems.

Phase 3 - Testing and Deployment

Conduct rigorous testing of the re-platformed system to ensure data integrity, performance, and reliability. Deploy the enhanced system to production.

Technology & Frameworks

We recommend an approach leveraging Azure managed services:

Data Platform

Azure Data Factory (ADF) for data ingestion and transformation, data storage, and PostgreSQL for relational data storage.

ML Platform

Azure ML for orchestrating and managing the ML lifecycle, including model training, experimentation, and deployment.

Other Services

Azure Pipelines, Azure managed Grafana for monitoring and alerting, Azure Key Vault for secure storage of credentials and secrets.

Business Outcomes

Improved Scalability and Performance

Leverage Azure's scalable and high-performance infrastructure.

Enhanced Operational Efficiency

Streamline data pipelines, automate processes, and reduce maintenance overhead.

Accelerated Development

Enable faster model development, experimentation, and deployment.

Improved Integration

Seamlessly integrate with other systems and leverage Azure's ecosystem.

Reduced Costs

Optimize costs through managed services and pay as you use services tools.