How Generative AI Can Be Used for Data Augmentation to Improve Data Quality in Enterprise Analytics

Data quality plays a major role in enterprise analytics to generate accurate and reliable insights. This requires clean, comprehensive and diverse data sets to make informed business decisions. But even today, many organisations struggle with incomplete or unstructured data and the result of this will be poor performance of analytical models. With the rise of Gen AI and its applications, it is increasingly being used to provide solutions to data augmentation in order to improve data quality.

What is Data Augmentation?

Data Augmentation is a process of expanding or enriching a dataset by adding modified or synthetic versions of the original data. Generative AI can create synthetic data mimicking real data but anonymized or containing missing values. Traditionally, it has been used in fields like image processing or natural language processing (NLP) to generate variations of input data, such as flipping or rotating images, or replacing words in a sentence. With Gen AI’s Generative Adversial Networks and transformers, data augmentation has expanded to structured and unstructured data. This helps in training data sets to be robust and complete and in turn improving the accuracy of AI models relying on that data.

How generative AI enhances data quality through augmentation

Generative AI models can produce new data samples that maintain the underlying patterns of the original dataset. This helps in filling gaps, creating balance and addressing data biases.

1. Filling Missing Data

Missing data has become one of the major issues in organisational data analytics. Incomplete datasets lead to incorrect analysis and this will reduce the accuracy of predictive models. Generative AI can analyze the patterns within the existing data and generate synthetic values to fill the gaps.

For example, in customer databases, when a few fields are missing, like income or age, a generative AI model trained on similar customer data can fill these missing fields ensuring that the analysis doesn’t get skewed due to incomplete information.

2. Handling Imbalanced Datasets

Enterprise data sets often has an imbalance – one category or class will be underrepresented compared to others. For example, in a fraud detection model, fraudulent transactions may be far fewer than legitimate ones. This imbalance can lead to biased models that overlook the minority class. Generative AI, such as GANs or Variational Autoencoders (VAEs), can generate synthetic samples of the minority class, thus balancing the dataset. This allows for accurate training of machine learning models, improving their ability to detect fraud or other rare events.