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How Effective Is AI-Generated Synthetic Data for Businesses

Synthetic data

Everyone is looking for a new way to use artificial intelligence (AI) to improve operations. It will never be able to replace a human worker fully, but techniques are evolving for humans and AI to work together and optimize workflows. Such is the growing case for AI-generated synthetic data, which could one day replace organic information for businesses.

However, this recent development begs an essential question — is this synthetic data effective? It’s natural to want to take advantage of the most recent technology when you run a company, but it’s critical to learn how it works first. Doing so will ensure you conserve the most funds while adapting to the ever-changing economic landscape.

What Is Synthetic Data?

Synthetic data is generated by computers or AI for organizations to rely on instead of real-world information. Rather than collecting info through in-person occurrences or surveys, a program formulates it based on genuine sets. This can be extremely valuable as more data privacy laws are enacted, allowing companies to continue analyzing things but not risk compromising their customers. It may become a necessary solution as governments crack down on the ever-growing threats to personal information.

You may even benefit from using synthetic data sooner rather than later. Gartner estimates 60% of the information you would use for analysis or training AI models will be synthetic by 2024. Keeping information safe becomes critical as people care more about staying private and cybercriminals find innovative ways to breach your security. Using synthetic data instead of regularly obtained information will likely become vital.

Can Synthetic Data Replace Real-World Data?

Synthetic data is a useful replacement for real information but can have some inaccuracies. In one study from 2020, scientists selected 19 data sets to create it, then tested AI models they trained on the synthetic and genuine information to see which was more accurate. About 92% of the models trained on synthetic data were less accurate than the ones trained on real data, but the scientists stated those were minor deviations. They may become smaller as time passes until they’re just as accurate.

Another research team from the Massachusetts Institute of Technology and Boston University conducted their own study and came to different conclusions. They created a synthetic data set with 150,000 videos and trained an AI model with it. Their results found the algorithms that learned from synthetic data were more accurate than those that utilized real information. As this research was published in 2022, you can see how technology has evolved to produce even better synthetic data in just a few years.

Synthetic data has various benefits you could bring to your organization. However, it’s essential to note the current flaws you could also experience. It is quite accurate compared to genuine data, but creating a synthetic data set that inaccurately reflects the real world is possible. Additionally, it could still incorporate the biases it learns from the organic information. Thus, verifying the data’s truthfulness will take work before you can utilize it.

Benefits of Using Synthetic Data

You might wonder what positives synthetic data can provide to your business since it can be a valuable replacement for genuine information. Here are a few examples of how these data sets can improve your and your employees’ workflows.

1.   Reducing Staff Turnover

Currently, around 50% of workers care more about their workplace culture than their salary. People want to work at a job that challenges them and offers a space where they can thrive. However, 62% of IT employees say they’re physically and emotionally drained, and 42% experience such high burnout that they’re contemplating quitting their jobs in the next six months.

Synthetic data can generate information in the blink of an eye, enabling team members to do fewer repetitive tasks. According to IBM, Deloitte reduced the preparation time of management reports from five to eight days to one hour, giving five analysts more time for high-value work. Giving your employees more time to perform critical tasks rather than sort through data for AI training can reduce their burnout, improve company culture and minimize turnover.

2.   Enhancing Scalability

Because synthetic data can generate information so quickly and vastly, it is highly scalable. AI models take thousands of pieces of information to train, often needing a thousand at minimum for each category. Having your employees manually gather all this data can take an incredibly long time. Synthetic data can populate much faster, enabling you to scale at a much better rate than with organic information.

3.   Enabling Data Privacy

Data scarcity is becoming a significant issue as countries pass regulations on how companies can collect information from consumers. This is needed to train future AI models and perform analyses to aid marketing efforts. Synthetic data only resembles the original information, removing people from the equation and allowing you to utilize their data without risking exposing them.

This can serve two beneficial purposes — helping you comply with privacy laws and assuring customers a hacker can’t compromise their sensitive information. That can help you save money by avoiding legal fees associated with improper practices, entice more people to use your business and prevent losses from departing consumers following a breach.

Utilizing Synthetic Data to Improve Business Operations

Synthetic data is relatively new, but it’s already proving effective for many corporate workflows. It can improve the employee experience, make your organization more scalable and ensure compliance with ever-growing privacy laws. As technology expands, it’s critical to investigate the developments and how they

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