Background and objectives
World leader in thermal protection for industrial equipment.
Deployment of a new performance management tool (EPM).
Need for flexibility and adaptability in the design and construction of future data for the target tool.
Number of end users: 300
Number of RUs: 70 Reporting units in over 30 countries
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 Integration in Historical Data Projects
Automated Data Extraction :
Pattern Recognition : Identify and automatically extract relevant data from various sources, including unstructured data.
Data Cleaning and Transformation :
Detect and correct anomalies, ensuring high-quality data transfer.
Data Normalization : Automatically convert data into a standardized format suitable for the new system.
Data Migration :
Intelligent Planning : Optimize migration plans by identifying the best strategies and sequences for data transfer.
Automated Transfer : Reduce manual interventions with AI algorithms managing data transfer.
Validation and Verification :
Quality Control : Ensure transferred data was complete and accurate.
Automated Testing : Conduct tests to confirm data functionality in the new system.
Predictive Analysis :
Problem Forecasting : Anticipate potential issues during migration and proactively proposed solutions.
Results
Time Savings: Significantly reduce the time required to extract, clean, and transfer data.
Enhanced Accuracy: Minimize human errors through automation and continuous verification.
Efficiency: Streamline processes leading to fewer disruptions in daily operations.
Adaptability: Handle large and diverse data volumes effectively.
