At an enterprise level, data is essential. When accurate and leveraged properly, it can inspire action, empower innovation or enable organizations to explore trends, patterns and behaviors. But when information lacks transparency, accuracy or diversity, business actions and outcomes can become compromised.
Visual synthetic data, crafted using techniques common to the visual effects and gaming industries, is fabricated imagery grounded in real-world phenomena that—when connected with machine learning—yields capabilities and insights that help leaders in every industry act with the best interest of their company and customers in mind.
Integrating synthetic data can revolutionize an organization’s approach to computer vision. It enables the development of new capabilities and allows for rapid adaptation to the evolving business environment. Identifying specific applications, such as hardware development or automated visual inspections, where synthetic data can significantly improve outcomes, is crucial for effective implementation and tangible results.
With synthetic data, businesses can gain a level of control previously unheard of in data pipelines—and it’s this versatility that makes synthetic data’s potential limitless. According to Gartner, by next year, 60% of AI data will be synthetic.
Answer these queries before incorporating synthetic data into an upcoming initiative:
Systems powered by machine learning capabilities rely on carefully prepared datasets. When datasets are incomplete, it can lead to a host of issues ranging from misleading insights to bias and fairness issues.
So, ask if your data fully represents the problem you’re tackling. According to additional research by Gartner, not working with high-quality data can cost businesses worldwide an average of $9.7 million annually, meaning a comprehensive look into the information that’s on hand is vital.
An additional consideration should center on how balanced the data pool is. Is there a broad enough range of edge cases so that the findings aren’t biased? Too many findings from too few demographics or segments can skew results and not benefit the machines trying to learn these patterns. Ensuring your dataset is not only clean but also varied and well-labelled helps train an algorithm to operate with clarity.
Synthetic data offers a strategic approach to generating input data for algorithmic development, balancing cost and labor efficiency at scale. Manual data collection typically incurs linear costs, while synthetic data production involves an initial investment that stabilizes over time. This means there’s a specific dataset size at which synthetic data becomes more cost-effective than manual methods.
Beyond just cost, synthetic data brings additional advantages that, while achievable manually, would significantly increase expenses. These benefits include enhanced scalability, diversity and complexity of data, which are crucial for robust algorithm training.
Collecting sufficient and appropriate data to train a machine learning model can take weeks or months to complete. So, it’s worthwhile to ask: will the time-intensive process of acquiring, annotating, cleaning and testing real-world data be too much for your organization to handle long-term, especially if resources are limited?
To maximize the potential of synthetic data, it’s essential to produce data that accurately reflects real-world nuance and complexities, and thoroughly analyze and understand its impact on machine learning applications. This requires a skilled team proficient in a blend of technical artistry and data science to ensure the data not only mimics reality but also enhances machine learning model training.
A clear ‘yes’ or ‘no’ to any of these questions should serve as a signal whether synthetic data is the best option for this initiative. But, if the negatives outweigh the positives, that doesn’t have to be the end of your exploration of the topic.
We understand that synthetic data is a relatively new frontier for many companies. We connect with our clients to understand their business objectives and deploy an agile-minded team to build a solution that helps your team find the answers it seeks. Visit us here to learn more.
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