Products M 5
Intelligent data preparation and data processing
Our platform enables comprehensive exploratory data analysis (EDA), including:
- Identification of data structure and key characteristics ;
- Detection of outliers and anomalies ;
- Identification of relationships and correlations between variables ;
- Preparation of data for subsequent analysis stages ;
Objective
The module provides comprehensive preprocessing of industrial data to enable effective machine learning model training. It structures heterogeneous data sources, builds databases, and automatically eliminates anomalies to improve data quality and ensure the reliability of AI models.
Key features
Automated exploratory analysis
Performs structural data analysis, identifying key characteristics such as variable distributions, data types, and general patterns, allowing rapid understanding of industrial datasets.
Anomaly detection and handling
Using machine learning algorithms, the module automatically identifies outliers and anomalies in real time, offering options for correction or exclusion to prevent model distortion.
Data structuring and database creation
The module organizes raw data into structured formats, including the creation of relational databases, with support for integrating heterogeneous data sources.
Relationship and correlation identification
Automatic calculation of correlations and causal relationships, along with visualization of dependencies between variables (e.g., energy consumption and productivity), enabling the discovery of hidden patterns for process optimization.
Data preparation for ML model training
Includes normalization, scaling, and missing-value imputation specifically adapted for neural networks, ensuring compatibility with forecasting and optimization models.
Expected Results
Improved ML model accuracy
High-quality data preparation reduces training errors, leading to more reliable forecasts and optimizations in production processes.
Increased overall efficiency
Clean and structured data minimizes false alarms in monitoring systems, reducing downtime and improving resource utilization.