Data quality analysis
Profile and cleanse your legacy data to ensure efficient data transition.
“We want to understand your data at the most granular level.” - Andrew Boswell, Director.
The standard of any data migration is partially determined by the quality of the initial legacy data. It is vital for both you and us to fully understand how complete and comprehensive your data is, and to use the migration opportunity to correct any anomalies. We do that through data profiling and data cleansing.
As part of our data migration planning, we advocate creating detailed profiles of your legacy data, so we all go into the transition process armed with a solid understanding of your current legacy data structures, formats and content, and know exactly what the content means. For example, if an application has two account balance entities, what are they used for, etc.
Data cleansing actively resolves and reconciles any anomalies in your data and, as such, is a vital part of any high-quality data transition solution. Part of our job is to identify data cleansing requirements and processes and to develop and test electronic data cleansing routines and queries. Data cleansing tackles issues such as:
- Missing or default data (e.g. a customer’s date of birth being set to 01/01/1900 if no data entered).
- Corrupt Data (e.g. special characters).
- Data health (e.g. quality of customer addresses or email addresses).
Data tidying (e.g. with older applications, users often record data in fields never intended for that purpose, such as comments or contact details).