Background and objectives
Our mission
Analysis of existing data: nature, scope, quality and volume of data to be migrated; identification of different sources, dependencies…
Recovery planning: definition of a migration schedule with milestones, including validation and testing stages.
Data preparation: cleansing and transformation of data to meet the requirements of the new target system and new business rules
Extraction and loading of data representing the last 24 months (Balance sheet, P&L as well as details by plant and cost center)
Validation and testing: data integrity verification, business validation and end-to-end testing of critical business processes
Post-migration support
At every stage of our intervention, artificial intelligence (AI) was able to play a crucial role in making the process more efficient, precise and less laborious.
Results
AI can accelerate and secure the data migration process by providing intelligent automation, advanced analysis, and the ability to adapt to different evolutions in target tool construction and real-time monitoring.
By integrating AI tools, we can reduce risk, improve the quality of migrated data, and ensure a smoother transition to the new EPM system.
AI's contribution to historical data recovery projects
Automated data extraction:
Pattern recognition: automatic identification and extraction of relevant data from various sources, even if unstructured.
Data cleansing and transformation:
Anomaly detection: identification and correction of errors or inconsistencies in data, thus improving the quality of transferred data.
Data normalization: automatic conversion of data into a standardized format suitable for the new system.
Data migration:
Intelligent planning: optimize the migration plan by identifying the best strategies and sequences for transferring data.
Transfer automation: reduce manual intervention by using AI algorithms to manage data transfer.
Validation and verification:
Quality control: checking that the data transferred is complete and accurate.
Automated testing: running tests to ensure that the data works correctly in the new system.
Predictive analysis:
Problem forecasting: anticipate potential problems during migration and propose proactive solutions.
Results
Time savings: Significantly reduce the time needed to extract, clean and transfer data.
Greater accuracy: fewer human errors thanks to automation and continuous verification.
Efficiency: Smoother processes and less disruption to day-to-day operations.
Adaptability: Ability to manage large and diversified data volumes.
