BrainPredict Data

Assesses overall data quality across dimensions with XGBoost + statistical analysis

Identifies and merges duplicate records using fuzzy matching + ML clustering

Intelligently fills missing values using statistical methods + ML predictions

Identifies anomalies and outliers using isolation forests + statistical methods

Validates data against business rules, schemas, and constraints

Standardizes dates, addresses, phone numbers, currencies across formats

Enriches records with additional data from external sources and cross-platform data

Generates comprehensive data profiles with statistics, distributions, and patterns

Aligns schemas across different systems and platforms

Resolves entities across systems (customers, products, suppliers)

Maps taxonomies and classifications between systems

Tracks data lineage and transformations across systems with LSTM

Manages master data entities and golden records

Manages reference data (countries, currencies, units)

Maps relationships between entities across platforms

Assesses data readiness for AI/ML models

Generates features optimized for AI models

Balances datasets for ML training (handles class imbalance)

Intelligently splits data for training/validation/testing

Augments training data using synthetic data generation

Optimizes data specifically for each BrainPredict platform

Detects personally identifiable information (PII) with BERT + spaCy

Anonymizes and pseudonymizes sensitive data

Manages data consent and preferences (GDPR)

Manages data retention policies and automated deletion

Audits data compliance with regulations (GDPR, CCPA, HIPAA)

Controls data access based on roles and policies

Audits data lineage for compliance and traceability

Tracks all data operations, transformations, and access for compliance