Confidential
Manufacturer | Construction
Improve the performance of the analytical environment by reducing the load on the ERP while enabling data archiving.
Context
The organization already has several reports used by different internal teams, accessible through the Power BI service. Some users even have direct access to the underlying data to address ad hoc needs. A small two-person team is responsible for supporting and maintaining these reports. The team is also mandated to support the organization in creating new reports and evolving existing ones to align with strategic objectives.
The reports were developed by the Business Intelligence team by connecting directly to the source ERP system. Strong user adoption led to a significant increase in the number, size, and complexity of reports. With direct data access, the load on the ERP system became excessive, creating a bottleneck that impacted the entire organization.
Additionally, the BI team lacked structured development processes and standardized practices. In the past, certain errors were not detected early on, and subsequent changes made it more difficult to identify and resolve issues.
To sustain its growth and further leverage data to define and monitor its strategy, the organization aims to modernize its practices and implement an analytics environment capable of supporting this evolution.
Objectives
1
Reduce data preparation efforts.
2
Implement a modern data architecture.
3
Reduce the load on the ERP system.
4
Enable data historization.
Solutions Proposed by Neos
Analytics Roadmap
Conducted an in-depth analysis of business needs and the current environment to design an optimized infrastructure on Microsoft Azure.
This phase included defining a target architecture and producing effort and cost estimates.
Migration to Microsoft Azure
Implementation of a Data Lake on Microsoft Azure to centralize data sources, simplify the management and analysis of structured and unstructured data, and significantly reduce the load on the ERP system.
- Data loading processes implemented through Azure Synapse Analytics.
- Daily data historization enabled through the integration of the Data Lake and Azure Synapse.
Implementation of Azure DevOps
To improve development and deployment management, Azure DevOps was implemented.
- Configuration of the Azure DevOps environment.
- Definition of rules, roles, and responsibilities based on best practices.
Results Achieved
Reduced ERP System Load
Through the use of Azure Synapse and a Data Lake on Microsoft Azure, the ERP system’s impact was reduced to a single daily data load process lasting approximately 30 minutes, executed overnight. All other operations now take place in Azure, allowing ERP servers to focus exclusively on transactional operations.
Daily Data Historization for the Finance Team
A daily historization process was implemented, providing the Finance team with detailed visibility into project margin evolution over time. This capability enables in-depth analysis of client project performance and helps identify key success and failure factors.
Collaborative Development for the BI Team
The implementation of Azure DevOps enabled the creation of code repositories (repos) with structured change tracking. The two team members can now work asynchronously without risk of conflict or overwriting each other’s work, improving both development quality and speed.
Conclusion
By implementing a modern architecture on Microsoft Azure, our manufacturing client transformed its analytical environment to meet its strategic challenges. Migrating to a data lake and integrating Azure Synapse drastically reduced the load on the ERP system while providing advanced logging and analysis capabilities. The adoption of Azure DevOps has established collaborative and structured development practices, improving the quality and speed of deliverables. These initiatives now position the organization to fully leverage its data, support its growth, and strengthen its decision-making.