Artificial Intelligence needs data. A lot of it. To give you an estimate the general understanding is if your dataset has 10 columns you need at least a 100 for all the features in the dataset to make the algorithm work.
But data collection, cleaning, refining, and organizing takes time. Not to forget the time it takes to adhere to the data protection laws. However, similar to time, AI waits for no one as the world sees massive innovation in artificial intelligence, especially GenAi. It is becoming important to address the future of synthetic data in developing artificial intelligence.
While using real data is everyone’s dream. It is in fact only a dream. The reality is that you can’t use real data at least without anonymizing it. But that has its problems. Anonymized data has been proven to be ineffective in training large ML models because of the amount of PII that it masks, encrypts, or destroys.
Hence Synthetic data becomes the closest and most viable solution to train ML models. It is not real data with no real Personally identifiable information meaning that privacy laws do not apply to it and it has the same statistical properties as real-world data making it the perfect solution for enhancing the ML life cycle by overcoming some of the most pressing issues in data science, including scarcity, bias, privacy, and drift.
Read The Complete Guide to Synthetic Data Here.
1. The ML Life Cycle and Synthetic Data:
If you are running or have run ML training you should already be aware of some of the biggest challenges but to list them out for clarity sake, they generally are:
- Privacy regulation resulting in low quantities of training data
- Imbalanced and incomplete data resulting in biased ML models
- Data drift is caused by long-term fluctuations in data input as well as real-world changes in data.
- Constraints sharing the models due to data privacy laws
These affect the quality of ML models at different stages of their life cycle depicted in the Illustration below.
2. Synthetic Data Makes your ML Model more Efficient and Effective:
a. 100% Privacy Law Complaint, Access More Data Faster:
The first challenge to running a successful ML model is obtaining high-quality, representative training data. While simple in theory practically one needs to pass through several hoops to acquire such data due to privacy regulations, logistical limitations, or inherent biases.
Therefore organizations are now moving to synthetic data generation, once trained on original datasets, which can produce an unlimited quantity of realistic data that maintains the core characteristics of the original data. This allows organizations to create robust training datasets without compromising on quality or violating privacy standards.
b. Clean Up, Moderate, and Enhance Real Data to High-Quality Training Synthetic Data:
The world is not perfect therefore real-world data is not as well. Missing, erroneous, or imbalanced data is a common problem when collecting data. This data when used creates models that represent these biases making the model ineffective and in most cases racist or sexist.
As innovators, we have to create models that work on future perfectness rather than past deficits. Synthetic data allows us to do that. Data scientists can fill gaps, rebalance class distributions, and create more comprehensive, fair, and accurate datasets. Now that you have fair and balanced datasets, you can train your ML model without worrying about it coming under scrutiny due to ethnicity or gender biases impacting predictive accuracy.
c. Be up-to-date with Real World Changes decreasing Data Drift:
Historical data used to train datasets often becomes outdated with changes in the environment. This causes the model irrespective of how effective it was to lose accuracy as its predictions are now not aligned with what’s happening in the real world. With massive globalization, innovation, and changes in human behavior data is becoming historic real quick.
For data scientists, this means that they have to continuously monitor and update training datasets. Synthetic data eases this process by a thousandfold by generating fresh, representative samples to train models with limited new real-world data in alignment with the latest patterns, keeping ML models accurate and relevant.
d. Share the model freely without sharing sensitive PII:
What is the point of going through months of creating an accurate and fair working ML model if you can’t freely share the results with external and internal shareholders essential for regulatory compliance and collaborative development? But you do not have to think of that because synthetic data does not contain PII it can serve as a secure, privacy-preserving alternative that accurately reflects the characteristics of the original data, facilitating compliance, model validation, and certification.
Read about Why Synthetic Data is the Future of AI here.
3. Ending Thoughts:
Synthetic data offers a game-changing approach to addressing the inherent challenges of the ML life cycle. By providing high-quality, highly available, and privacy-compliant data, synthetic data can significantly enhance each stage of model development, from data collection and preparation to training, evaluation, and retraining. Organizations can leverage synthetic data to build machine learning models that are fairer, more accurate, and better aligned with evolving data patterns, ultimately driving better business outcomes.
