Data Quality Improvement

Our innovative Intelligent Data Quality Improver packages turbocharge each step of the data processing at the heart of our analysis and interpretation features

  • Numerical data reliability assessment
  • Detection and correction of gaps, outliers, noise, bias
  • Extraction of further information to drive the dataset analysis and interpretation process
  • Use of analysis results to improve numerical data quality
  • Quantitative metrics to measure dataset quality
  • Use of ML and other AI technologies to drive the data quality improvement process