Synthetic data use cases
Use synthetic data instead of real (sensitive) data
Our clients utilize data smarter via various synthetic data use cases. Explore here the most valuable synthetic data use cases for you!
Example use case 1
Synthetic data as test data
Testing and development with representative test data is essential to deliver state-of-the-art software solutions.
Challenge
Using personal data or original production data as testdata is not allowed and alternative methods are outdated and introduce “legacy-by-design”.
Our solution
Deliver and release state-of-the-art software solutions faster and with higher quality with AI generated synthetic test data.
- Production-like data
- Privacy by design
- Easy, fast and agile
Example use case 2
Synthetic data for analytics
We are in the middle of the digital revolution and data-driven solutions are about to change our entire world. However, those data-driven solutions are only as good as the data that they can utilize. This is challenging due to strict data / privacy regulations.
Challenge
No data = no analytics. Many organizations have a sub-optimal data foundation where data cannot simply be used and shared.
Our solution
Build your strong data foundation with easy and fast access to as-good-as-real AI generated synthetic data.
- Unlock (sensitive) data
- As-good-as-real data
- Easy, fast and scalable
Example use case 3
Synthetic data for product demo’s
Seeing is believing: you will need “demo data” for product demo’s to astonish your prospects with next-level product demos.
Challenge
You potentially miss opportunities, because your demo data is suboptimal for product demo’s.
Our solution
Astonish your prospects with next-level product demos, tailored with representative AI generated synthetic demo data.
- Errorless, high quality demo data
- Tailor your product demo
- Easy, fast and agile
Example use case 4
Synthetic data for data sharing
Many organizations aim to achieve data-driven innovation. Here, data is essential and typically needs to be shared internally or even externally with third parties as a starting point. It is relatively simple: without data, there is no data-driven innovation and no collaboration opportunities. Specifically for the realization of data-driven innovation, having a strong foundation to access and share relevant data is essential.
Challenge
Data-sharing challenges include time-consuming legal processes, untapped valuable data, lack of a solid sharing framework leading to project halts and demotivation.
Our solution
Share synthetic data as alternative for sharing real data. This allows our customers to eliminate those aforementioned data-sharing challenges. Ultimately, this creates a strong foundation to realize data-driven innovation, but then, in an agile way where data can be accessed and shared freely.
- Release faster access to data
- Share the data with different parties without privacy concerns
- Faster innovation
- Increase customer retention and acquisition
Example use case 5
Synthetic data for data monetization
Unlike traditional methods like data anonymization, synthetic data offers a faster and more aligned approach, granting access to the entire dataset while preserving individual privacy.
Challenge
Data anonymization does not always lead to anonymized data and decreases data quality.
Our solution
Use synthetic data to streamline processes, and enhance the quality of insights derived, enabling more effective and ethical data monetization strategies.
- Unlock new revenue streams
- Secure synthetic data marketplace
- Data catalog with synthetic data
Example use case 6
Synthetic data for AI modeling
Utilizing synthetic data for AI modeling presents a unique opportunity for the AI model development process. Fast prototyping, hypothesis validation, and accelerating research and development processes before making real data requests. It enables data scientists to overcome many of the traditional challenges associated with real-world data access.
Challenge
Navigating data scarcity, privacy constraints, and the difficulty of proving business use cases without prior data access, leading to potential missteps in analysis and complex data-sharing issues.
Our solution
Synthetic data enables rapid prototyping and hypothesis validation, allowing data scientists to quickly access high-quality datasets, shorten R&D cycles, and improve AI model performance before requesting sensitive data access.
- Fast Prototyping with Synthetic Data
- Hypothesis validation before making a data access request
- High-quality data
Save your synthetic data guide now!
- What is synthetic data?
- Why do organizations use it?
- Value adding synthetic data client cases
- How to start
