<img height="1" width="1" style="display:none;" alt="" src="https://px.ads.linkedin.com/collect/?pid=4958233&amp;fmt=gif">
3 min read
Jon Hanzelka

At an enterprise level, data is essential. When accurate and leveraged properly, it can inspire action, empower innovation or enable organisations to explore trends, patterns and behaviours. 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 in the best interest of their company and customers.


Integrating synthetic data can revolutionise an organisation’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.


What to consider before adopting synthetic data


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:


1. What is the quality of the data we already have?


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 centre 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.


2. How much are you putting into the process?


Synthetic data offers a strategic approach to generating input data for algorithmic development, balancing cost and labour efficiency at scale. Manual data collection typically incurs linear costs, while synthetic data production involves an initial investment that stabilises 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 organisation to handle long-term, especially if resources are limited?


3. What’s your level of expertise?


To maximise the potential of synthetic data, it’s essential to produce data that accurately reflects real-world nuance and complexities, and thoroughly analyse 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 of 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 customers 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.


No video selected

Select a video type in the sidebar.