Synthetic Data
The Era Of Synthetic Data Is Here … New Realms Of Market Research?
By Chris Githaiga
Synthetic data, artificial data generated from models trained on real-world data, is rapidly gaining traction across various industries. In the research world, it has captivated attention due to its potential impact on the industry, from removing bias from data collection to speeding up the fielding process.
The new phenomenon has the potential to accelerate product innovation and development due to its numerous advantages. Businesses that leverage benefits of synthetic data will no doubt gain competitive edge in their industry in this era where cost, speed and efficiency are important factors.
Everyone wants near instantaneous results for something they’ re working on, and synthetic data has the promise of making research faster and cheaper. Due to external pressures for time and money, it has the potential to streamline and accelerate traditional data generation.
Synthetic data can also create and supplement large and diverse data sets, where real-world data is difficult to come by, deepening the richness and insights from the analysis, as well as supporting model and proof of concept testing for new methodologies.
It also holds immense potential in proofof-concept analysis, where it enables faster iterations. Moreover, synthetic data generation offers a unique advantage of consistency over human-generated responses. Unlike human participants who may exhibit fatigue or varying responses based on factors like time of day, Artificial Intelligence( AI) remains steadfast in its responses, ensuring reliability in data collection.
Since synthetic data does not have oneto-one correlations with real data, it is used for training machine learning models, testing software applications, and filling gaps in datasets when working on analytics projects.
Synthetic data is vital for finance, healthcare, and insurance industries, where data privacy and security requirements limit access to real-world datasets. In market research, synthetic data offers new possibilities, particularly in product testing. However, many businesses remain uncertain about its quality and evaluation.
To get a deeper understanding of how synthetic data can help boost product testing, it will be good if we first delve into how it is created. There are several different methods of creating synthetic data with Machine Learning( ML-based) models, depending on the use case and data requirements. Some of the most common ones include the Generative Adversarial Network( GAN) models, Variational Auto Encoders( VAE), Gaussian Copula( Statistics based) and Transformer-based models.
Under the GAN models, synthetic data generation happens using a two-part neural network system, where one part works to generate new synthetic data and the other works to evaluate and classify the quality of that data. This approach is widely used for generating synthetic time series, images, and text data.
Synthetic data, artificial data generated from models trained on real-world data, is rapidly gaining traction across various industries. In the research world, it has captivated attention due to its potential impact on the industry, from removing bias from data collection to speeding up the fielding process.
The VAEs are a class of generative models that have gained significant traction for generating high-quality synthetic data due to their capacity to learn rich, latent data representations. This approach uses a generative adversarial network system with an additional encoder to generate synthetic data that is highly realistic and similar in structure, features, and characteristics to real data. Despite their benefits, VAEs face challenges related to the quality and diversity of generated data, computational complexity, and evaluation metrics. Recent advancements, including conditional VAEs and hybrid models such as VAE-GANs, however, offer promising
10 MAL65 / 25 ISSUE