Data Cleansing

In smaller cleansing requirements, we use statistical patterns and clustering algorithms/techniques to identify outliers, chart confidence levels, and normalize your datasets. In larger solution needs, we use advanced statistical algorithms and techniques to optimally streamline your data inflows and outflows and improve the overall data quality from the source. Data Quality is a survival issue and we strive to follow standards such as TDQM (Total Data Quality Management) in our approach.