Context
As part of its IT transformation and in response to increasing regulatory requirements, the client initiated major upgrades to its applications dedicated to risk management and financial reporting. Two critical systems were involved: SCREAM, used to calculate risk indicators under Solvency II, and OLISFA, dedicated to financial reporting and the integration of processing scripts. To secure these evolutions and ensure the reliability of regulatory processes, the client relied on Avaliance to support technical developments, modernize deployment processes, and stabilize production environments.
Challenges
The client needed to ensure compliance with Solvency II requirements while improving the reliability of risk calculation and financial reporting systems.
Key challenges included automating deployments, reducing technical debt related to legacy scripts, improving the quality of financial reports, and stabilizing application environments in a highly regulated context.
Achievements
-
evolution and maintenance of the SCREAM application for Solvency II risk indicator calculations
-
implementation of automated deployment processes using Jenkins
-
development and enhancement of financial reports using Actuate
-
decommissioning of Groovy scripts and integration of processing logic into the OLISFA application
-
analysis and resolution of production incidents to stabilize financial reporting environments
Technologies Used
Results
Context
The client launched a strategic program to structure its data capabilities at a group level and establish the foundations of a data-centric organization. This initiative involves coordinating several key entities — including the Cloud Center of Excellence, Data Management Department, and DataLab — to align practices, architectures, and governance. To drive this transformation and ensure the successful deployment of an Azure-based Enterprise Data Platform, the client relies on Avaliance to organize initiatives, define standards, and support teams in increasing their maturity.
Challenges
The client needed to structure Data Management at scale while deploying a modern data platform capable of supporting business and analytical use cases.
Key challenges included unifying Data and Cloud practices, defining core architectural patterns (ingestion, modeling, governance, serving), establishing a robust data governance framework, aligning teams under a common agile framework, and successfully launching the group-wide data platform.
Achievements
-
structuring Data Management activities and defining governance roles, responsibilities, and processes
-
implementing and supporting teams on the SAFe framework to industrialize delivery
-
designing Azure-based data architecture patterns for ingestion, modeling, and exposure
-
defining the Enterprise Data Platform scope including roadmap, MVP, and target architecture
-
coordinating business, IT, and support teams and overseeing overall program governance
Technologies & Frameworks
Results
Context
As part of its global digital transformation, the client, a large international group, established a Cloud Center of Excellence to structure, secure, and accelerate the adoption of Azure Cloud across its international entities. This initiative aims to support business-driven projects while ensuring architectural consistency, compliance with global standards, and business value creation. To support this cross-functional initiative and industrialize usage, the client relies on Avaliance to structure Cloud & Data practices and support projects end-to-end.
Challenges
The client needed to accelerate the time-to-market of Cloud and Data projects while ensuring robustness, security, and compliance with global standards.
Key challenges included strengthening Cloud governance, integrating security (SecOps), cost control (FinOps), and automation (DevOps) practices, defining standardized, reusable, and compliant Cloud & Data architectures, and supporting strategic projects across their entire lifecycle.
Achievements
-
design of Cloud & Data architectures on Azure, including Data Lakehouse platforms (Synapse, Databricks, ADLS Gen2)
-
industrialization of batch and streaming ingestion pipelines using Azure Data Factory and Synapse Link
-
definition of reusable Cloud & Data blueprints and reference architectures
-
integration of DevSecOps practices (Infrastructure as Code with Terraform and Bicep, CI/CD via Azure DevOps)
-
implementation of a Cloud & Data governance framework (security, cost management, naming, and tagging)
-
facilitation of architecture reviews and coordination across IT, Data, Security, and Business stakeholders
