Synthetic data has emerged as a powerful tool for overcoming the limitations of real-world data. The future holds great promise for accelerated innovation.
With synthetic data, companies can now generate financial transactions, medical records or customer behavior patterns that maintain statistical relevance like real data. This emerging technology can help train and test models, preserve privacy and fill gaps where real data is scarce.
“Synthetic data generation is critical to the success of many AI deployments, especially in highly regulated industries like health care and finance,” said Bryan Harris, Chief Technology Officer at SAS. “It offers benefits like lowering the cost of acquiring data, increasing the privacy of analyzing data, and improving model performance.”
To realize the benefits of synthetic data it's crucial to ask the right questions to ensure its effectiveness and reliability. Here are six essential questions to consider:
1. What is the purpose of generating synthetic data?
Understanding the primary objective behind generating synthetic data is the first step. Are you looking to augment your existing dataset, create data for rare scenarios or preserve privacy? For instance, synthetic data can be used to train and validate machine learning models when real data is insufficient or to simulate rare events that are not well-represented in the original dataset. This is valuable across industries. Clearly defining the purpose will guide the entire data generation process and help in selecting the appropriate methods and tools.
2. What methods will you use to generate synthetic data?
There are various methods to generate synthetic data, each with its own advantages and limitations. First, and most simply, rules can be applied to generate data following known patterns such as statistical distributions or selection from a known list or catalog of possible values. Rules can also be coded to enforce generation following specific domain or business logic. The challenge with rules is that they don’t scale well across many attributes, particularly when complex relationships need to be maintained. This is where algorithmic or AI-based approaches excel. Common techniques include Generative Adversarial Networks (GANs), Synthetic Minority Oversampling Technique (SMOTE), and agent-based modeling. GANs are deep learning models that are particularly useful for generating realistic data by training two neural networks against each other until real data cannot be discriminated from generated data. SMOTE is effective for balancing class distributions in imbalanced datasets by intelligently interpolating between real data points.
3. How will you ensure the quality and validity of the synthetic data?
Quality and validity are foundational when it comes to synthetic data. The generated data should accurately represent the statistical properties of the original data, including the correlation among attributes/columns, without compromising its integrity. This involves using visual and statistical evaluation metrics to assess the quality of the synthetic data. Additionally, it's essential to validate the synthetic data by comparing it with real data (distributions and relationships) to ensure it meets the desired criteria and serves its intended purpose effectively. Synthetic data must look like real data; otherwise, it cannot be trusted. Failure to do so can have dire consequences for training, validating and deploying models.
4. How will you address privacy and security concerns?
One of the significant advantages of synthetic data is its ability to preserve privacy. However, you must ensure that the synthetic data does not inadvertently expose sensitive information or allow tracing back to real source data. Techniques such as differential privacy can be employed to add noise to the data during the training and generation process, making it nearly impossible to re-identify individuals. Additionally, implementing robust security measures to protect synthetic data from unauthorized access is essential to maintain data privacy and security.
5. What are the potential biases in the synthetic data?
Bias in synthetic data, just as in real data, can lead to inaccurate and unfair outcomes, especially in machine learning models whose predictions are used to make decisions that impact people. It's important to identify and mitigate any biases that may be present in the original data and ensure they are not amplified in the synthetic data. This involves analyzing the data for underrepresented segments or groups and purposely focusing the generation of synthetic data to balance the data distribution. Addressing biases will help in creating fair and unbiased synthetic data that can be used for reliable decision-making.
6. How will you integrate synthetic data with real data?
Integrating synthetic data with real data can enhance the overall dataset and improve model performance. In some cases, this involves merging the synthetic data with real-world data to create a comprehensive dataset for development and/or testing. In other cases, it will be more effective to focus the use of synthetic data more specifically on validation to test the robustness of using models for decision making.
In any event, it's essential to ensure that the synthetic data complements the real data without introducing inconsistencies. Proper integration will enable you to reap the benefits of both synthetic and real data, leading to more robust and accurate models – and, ultimately, better decisions.
By asking these six questions before generating synthetic data, you can ensure that the data you create is high quality, preserves privacy, and serves its intended purpose effectively. Synthetic data holds immense potential in the world of data science and machine learning, and with careful consideration, it can be a valuable asset for your AI development efforts.
