Synthetic Data
for Analytics
Build your strong data foundation with easy and fast access
to as-good-as-real AI-generated synthetic data
Core Value Proposition
Deliver software solutions easier, faster, and with higher quality with representative
synthetic test data
Be smarter than the competition
Stay ahead by leveraging synthetic data that is as-good-as-real, enabling smarter, faster analytics.
Leverage new and more innovation opportunities
Unlock previously restricted data to explore new avenues for innovation and growth.
Turn unlocked data into valuable insights
Access sensitive data securely while adhering to privacy regulations like GDPR, enabling valuable insights.
Mitigate overhead and bureaucracy
Eliminate barriers with an easy, fast, and scalable solution, reducing delays from internal processes and enabling faster insights.
Realize data-driven innovation
Use synthetic data twins to fuel innovation with statistically identical, privacy-safe data.
Boost your data-strategy and data innovation adoption
Enhance your data strategy with scalable synthetic data solutions that integrate effortlessly across datasets and types.
Analytics challenges
For many organizations, it is challenging and time consuming to access relevant data, required to realize data driven-innovation.
Data access is critical
- Without (timely access to) data, data driven innovation and analytics is not possible
- You miss valuable data-opportunities and momentum due to “locked” data
- Data is critical to be smarter than the competition
Getting access
to data takes ages
- Privacy regulations like the GDPR are strict and limit access to data
- You will be confronted with a lot of bureaucracy and paperwork, causing dependencies and “legacy-by-design”
- Overhead like internal processes, risk assessments, data access requests are time consuming
Anonymization
does not work
- Anonymization destroys your data, making it no longer suitable for analytics (garbage in = garbage out)
- Anonymization does not result in anonymous data. Privacy risks will always be present
- Classic anonymization techniques are not scalable, because they work different per dataset and per data type
Our solution: AI-Generated Synthetic Data
Quality assurance
report
Assess generated synthetic data on accuracy, privacy, and speed
Syntho’s quality assurance report assesses generated synthetic data and demonstrates the accuracy, privacy, and speed of the synthetic data compared to the original data.
External evaluation
by SAS
Our synthetic data is assessed and approved by the data experts of SAS
Synthetic data generated by Syntho is assessed, validated and approved from an external and objective point of view by the data experts of SAS.
Time Series Data
Synthesize time-series data accurately with Syntho
Time series data is a datatype characterized by a sequence of events, observations and/or measurements collected and ordered with date-time intervals, typically representing changes in a variable over time, and is supported by Syntho.
Save your synthetic data guide now
What is synthetic data?
How does it work?
Why do organizations use it?
How to start?
Why do organizations use AI Generated Synthetic Data for Analytics?
Unlock (sensitive) data
- Synthetic data is exempt from privacy regulations, such as the GDPR
- Unlock personal data and have access to more data that was previously restricted (e.g. due to privacy)
As-good-as-real data
- An AI generated synthetic data twin is statistically identical in comparison to the original data
- Use AI generated synthetic data as-if it is original data
Unlock new revenue streams
- Bypass internal bureaucracy, processes, risk assessments, data access requests and similar time consuming overhead
- Scalable solution that works the same for each dataset and for each datatype
