Synthetic Data for AI Modeling
Fast prototyping and hypothesis validation before real data request performance
Introduction to AI modeling with synthetic data
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.
AI modeling challenges
- No data availability for pilot projects
- Limited data understanding leads to incorrect decisions in the analysis
- Complexity of data sharing internally and externally
Proof of business use case
- It's difficult to prove a business use case without understanding the data, yet to access the data, you must first demonstrate the business use case
Data scarcity
- Many constraints on the data available for training AI models
- Data partly not available due to privacy risks
Our solution: AI-generated synthetic data
What makes Syntho's approach unique?
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.
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.
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.
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Synthetic data opportunities
Fast Prototyping with Synthetic Data
- Allow data scientists to quickly access synthetic datasets, which are as good as real. It significantly shortens the RnD development cycle
Hypothesis validation before making a data access request
- Before requesting access to sensitive or proprietary datasets, data scientists can use synthetic data to validate their hypotheses and prove the business use case for data access
High-quality data
- By ensuring full data access and upsampling rare observations, organizations can improve the performance and reliability of their AI models
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Value
Build your strong data foundation with AI-generated synthetic data
- Improve the process from ideation to production
- Cost-effective alternative to real data
- Data diversity by accessing whole datasets
- Upsamling and data balancing for underrepresented data
- Speed up innovation projects
- Bias reduction: by carefully designing synthetic datasets
Save your synthetic data guide now!
- What is synthetic data?
- Why do organizations use it?
- Value adding synthetic data client cases
- How to start
