There is not much point in making information available to decision-makers if that information is not correct. Clearly, flawed data (or as Data Warehouse expert John Tromble calls it - "accuracy challenged data") can result in flawed decisions. Data Transformation is the process we use to refine and correct or cleanse data in preparation for storage and use. This process exists so that raw data stored in a data warehouse has consistent meaning to users that access it.

Data Transformation acts to ensure that the data in a data warehouse or data mart is both accurate and up to date. The success or failure of a data warehouse often hinges on data accuracy. To make sure that your implementation brings real value to the enterprise, we standardize and cleanse data, ensuring its integrity.

The process of Data Transformation works to verify the availability, consistency and accuracy of the data elements identified during Discovery. The project team will determine whether data elements will be facts and attributes and map each data element to its source. It is important to evaluate the source data as to its timeliness, completeness, accuracy, and efficiency of access and suitability to purpose. Finally, the Data Transformation team helps to define rules for processing each source data element including rules for editing, cleansing, transformation, extraction, and combination with other elements. All this in order to define movement to the Data Warehouse so that raw data stored there has consistent meaning to users that access it.