Today, the world of machine learning is reaching its maturity. Auto ML tools are making it possible to try numerous algorithms, choose the best ones, and dramatically increase the quality of your insights and results.
That’s why it’s more important than ever to spend time focusing on your data. As new innovations make models more effective and accurate, the data they depend on is your last barrier to success.
A synthetic data platform gives you new data at scale which shares the characteristics of an original data set. But, more than that, it unlocks a new level of flexibility and optimization. From feature engineering and enrichment to data augmentation, the right platform can help you achieve superior insights.
A synthetic data platform lets you create the data you need, when you need it. But what can you expect from a complete synthetic data solution? And how does the right platform empower you to uncover superior results and use data in innovative new ways?
Enriching your data with hidden features
The quality of data you feed into your ML model will directly affect the quality of your insights. With a synthetic data platform, you’re in complete control of that quality.
In part, it’s about generating new data sets that share the characteristics of your source data. But it’s also possible to go even further, generating features and traits that didn’t exist before.
A synthetic data platform empowers you to catch the essence of your data, even if this isn’t wholly reflected yet. So you can influence the quality of your model – and generate superior insights – on the most intricate, nuanced level.
Synthetic test data is more accurate and unbiased
An effective synthetic data platform does more than create new records. Sophisticated, AI-driven platforms can also increase the overall quality, balance and composition of your data, maximizing your predictive capabilities and driving better decision-making.
Using a specialized platform for synthetic test data generation adds automated checks and balances, including:
- Automatic labeling
- Auto-completed values
- Tools to visualize and manage data quality and bias
As a result, you can feel confident about the data you’re using and remove the many pitfalls associated with live, real-world datasets.
Enhance data for innovative new use cases
Your use cases for real-world data are limited by scale, quality and privacy. Your use cases for synthetic data are only limited by your AI ambitions.
Take the financial services sector – an industry that can benefit greatly from smart use of data. Revenue streams are left undeveloped because people can’t collaborate with confidence, or ML models are stifled by biased, inaccurate data of insufficient scale.
The right synthetic data platform removes those barriers to innovation, not just by supplying you with data that poses no disclosure risks, but also with built-in tools for integration with data sources, sharing data sets, and more.
Synthetic data in financial services is the fastest, most effective way to capitalize on the value hidden in your data with new, enriched and unbiased records to fuel your model.
Access training data quickly and affordably
Finally, a synthetic data platform allows you to generate data at scale, with absolute accuracy, at a much lower cost than capturing real records.
Fintech software development is just one example. To create a banking app, developers may need massive amounts of data in order to test and validate their solution. However, manually sourcing records comes at a great time and money expense. A synthetic data solution gives developers a way to get the data they need, quickly, affordably, and earlier in the development process.
A synthetic data platform isn’t just a way to create data. It’s a way to transform quality and balance as well as scale — and generate superior insights from your ML model.