Data Lifecycle

Get high quality data and rationalise its use

In an era of exponentially accelerating data production and consumption, managing inactive data, securing and duplicating your data in non-production environments is a particularly time-consuming task.



Manage your documentation and campaigns

Data Quality

Guarantee high quality data suitable for use in operations, decision making and planning

Data Archiving / Purging

Archive your inactive data and destroy those that need to be destroyed

Data Anonymization

Anonymise your sensitive data

Data Subsetting

Create integrated and coherent data subsets

Data Mart

To meet a specific business or domain specific need

Data Refresh

Update your data to keep it meaningful and reliable

Data Reversibility

Transfer and recover data from one environment (Cloud…) to another by guaranteeing their integrity while meeting regulatory requirements

Data Retirement

Decommission your applications while accessing system data for declaration and conformity purposes

Data Comparison

Detect gaps in structure and content between your environments


  • Reduce time spent on functional and technical specification through retro-documentation
  • Data quality (automated data profiling and quality campaigns)
  • Minimize implementation and maintenance costs of the archiving and anonymization system
  • Automatically take into account any changes in the data model
  • Guarantee the integrity of the data in your different environments
  • Optimize and secure the creation of your non-production databases

Cas d’usage

  • Implement a data quality project
  • Build datasets for your digitisation and lifecycle management projects to archive, destroy, anonymise, manage reversibility, merge, compare, decommission, etc
  • Implementation of a dynamic & recurrent archiving system (RGPD)
  • Creation of non-production environments
  • Datamart creation and feeding project (Data warehouse)
  • Retirement of the database

Discover other functionalities of the Systnaps solution :

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