Why use real (sensitive) data when you can use synthetic data? Our clients realize data driven innovation via various synthetic data use cases. Explore here the most valuable ones for you!
Synthetic data for model development and advanced analytics
Having a strong data foundation with easy and fast access to usable, high quality data is essential to develop models (e.g. dashboards [BI] and advanced analytics [AI & ML]).
Typicall challenges
However, many organizations suffer from a sub-optimal data foundation resulting in 3 key challenges:
Getting access to data takes ages due to (privacy) regulations, internal processes or data silos
Classic anonymization techniques destroy data, making it no longer suitable for analysis and advanced analytics (garbage in = garbage out)
Existing solutions are not scalable because they work different per dataset and per data type and cannot handle large multi-table databases
Our solution
Develop models with synthetic data with maximized data quality to:
Minimize the use of original data, without hindering your developers
Unlock personal data and have access to more data that was previously restricted (e.g. due to privacy)
Easy and fast data access to relevant data
Scalable solution that works the same for each dataset, datatype and for massive databases
Business value
This allows our clients to:
Higher NPS scores
Be stronger than (and even beat) the competition
Leverage new and more innovation opportunities
Spend time on development instead of overhead and internal processes
Retaining and attracting talent that was previously deterred
Build your strong data foundation with easy and fast access to usable, high quality data now!
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