How do you generate synthetic data for machine learning and why do you need it?

 Engineers all over the globe get instant headaches and feel seriously unwell when they hear the “Data is the new oil” phrase. Well, if it is, then why don’t we just go to the nearest data pump and fill up our tanks for a nice, long ride down machine learning valley? 

It’s just not that easy. Data is messy. Data needs to be cleaned, transformed, anonymized and most importantly, data needs to be available. All in all, that data oil well is pretty tricky to get a good flow of compliant and ready-to-use data out of. 

Synthetic oil or rather, synthetic data to the rescue! But what is synthetic data today? AI-generated synthetic data is set to become the standard data alternative for building AI and machine learning models. Originally a privacy-enhancing technology for data anonymization without intelligence loss, synthetic data is expected to replace or complement original data in AI and machine learning projects. Synthetic data generators can open the taps on the proverbial data well and allow engineers to inject new domain knowledge into their models. 

Synthetic data companies, like MOSTLY AI offer state of the art generative AI for data. Choosing the right platform or opting for open source synthetic data must be a hands-on process with a lot of experimentation. To get the most out of this new technology, it’s a good idea to keep in mind some of the principles necessary for synthetic data generation:

• You need a large enough data sample.
  Your data sample or seed data, that is used for training the synthetic data generating algorithm should contain at least 1000 data subjects, give or take, depending on your specific dataset. Even if you have less, give it a try – MOSTLY AI’s synthetic data generator has automated privacy checks, so you won’t end up with bad quality data or a privacy leak.

• Separate your static data – describing subjects – and dynamic data – describing events – into separate tables. If you don’t have any time series data in your dataset, use only one table for synthesization.
• If you want to synthesize time-series data and run a two-table setup, make sure your tables refer to each other with primary and foreign keys.
• Choose the right synthetic data generator. MOSTLY AI’s free synthetic data generator comes with built-in quality checks and allows you to assess the accuracy and privacy of your synthetic data closely. 

 

Performance boost for machine learning

 
A lot of people tried and failed to build synthetic data themselves. The accuracy and privacy of the resulting datasets can vary considerably and without automated privacy checks, you could end up with something potentially dangerous. But that’s not everything. The synthetic data use case for machine learning goes way beyond privacy. 

Algorithms are only as good as the data that is used to train them. Synthetic data offers a machine learning performance boost in two ways: simply providing more data for training and by using more synthetic samples of minority classes than what is available. The performance of machine learning models can increase as much as 15%, depending on the exact dataset and model. 

 

Fairness and explainability

 
According to some estimates, as much as 85% of algorithms are erroneous due to bias. AI-generation can be used to enforce fairness definitions and to provide insight into the decision making of algorithms through data that is safe to share with regulators and third parties. High quality AI-generated synthetic data can be used as drop in placement for local interpretability in validating machine learning models.  

Of course, you won’t know until you try. MOSTLY AI’s robust synthetic data generator offers free synthetic data up to 100K rows a day with interactive quality assurance reports. Go ahead and synthesize your first dataset today. If you have questions related to data prep, read more about how to generate synthetic data on our blog.