What is Data Cleansing?

 

  1. What is Data Cleansing?
  • In Data Management, Data Cleansing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database.

 

  1. What happens after data cleansing?
  • After the data is cleansed, a data set is expected to be consistent with other similar data sets in the system. The inconsistencies detected or removed can be a result of user error, by corruption in transmission or storage.

 

  1. What are the steps taken for Data Cleansing in relation to some BSI projects such as RedPrairie and ATLAS?
  • For RedPrairie in the Philippines, we worked together with the market by kick starting SKU cleansing in IMS. During this process, we identify active, discontinued and obsolete local items with a goal to remove and correct SKU lists. Every SKU must be checked individually for validity or compliance. Once this is completed, the updated data is migrated to JDE.
  • For ATLAS, the scope is way bigger. We work together with the market to drive through the supply master, ABO and item data cleansing. We also ensure that data cleansing will be tasked to the market data stewards, as we want to stress that ownership should be taken by respective markets.

 

  1. What is Data conversion?
  • One of the keys to successful system implementation is data conversion.
  • Simply put, data conversion entails moving all of the approved data from your legacy systems into the new data management system.
  • In order to achieve successful data conversion, we have to support different test cycles and every cycle goes through data conversion to support that test cycle.
  • For ATLAS APAC1 (Philippines), we achieved over 90% of successful data conversion for all the conversion objects.

 

  1. What is used to measure the success of data migration?
  • From Amway’s standpoint, we use the Informatica Scorecard as a tool or a checkpoint to measure the success of data migration. The scorecard is used to spearhead pervasive data quality, enabling data stewards and analysts to participate in data quality processes and validate data by drilling down to the root cause, which has failed certain migration process.