Data Retention

Data Retention is a process of retaining data for a set time to comply with legal, administrative, operational, or business requirements. Data retention ensures that information is kept safe and available for intended use while complying with privacy and data protection laws.

Data retention involves establishing data retention policies that clearly define data storage and processing based on its value and legal requirements. It includes data identification and classification, setting retention periods, and implementing storage and deletion processes. This structured approach promotes effective data management by ensuring that data is retained when required and disposed of when no longer required for the purposes it was initially obtained.

Data Retention and Backups in DOKA SAAS 

In DOKA SaaS the Data is retained for as long as you are a customer. 
If you have Party data for a customer that has left you, then that customer's data can be archived and deleted using the Cleaning of Historical Data services.

DOKA SAAS, the application, database and web servers are backed up daily using AWS images and data disk volumes.
Backups are stored on AWS servers and access is limited only to Surecomp Cloud support team .
Data retention periods can always be customized as stated below .
Backups are taken on the first day of the month every month and retained for 1 year
Backups taken on 31st of Dec every year and retained for a year
Daily 'morning' backup taken every 12 hours at the start of the day and retained for 24 hours
Daily 'evening' backup taken every 12 hours at the end of the day and retained for 30 days
Disaster recover backup taken once daily and copied to a different region and retained for 24 hours ,can be restored if required

Audit History and cleaning of Historical Data  :

Every Static Data Maintenance transaction retains an audit snap shot of the 'before' values .
You can walk through these versions to see the modification history if needed. 
See the below gif to for an example.  

See also the article Cleaning of Historical Data for how to tidy up old data.