As artificial intelligence (AI) has grown increasingly sophisticated in recent years, companies are struggling with a lack of training data. A recent Entrepreneur article suggests that synthetic data is the answer to this challenge and outlines five reasons why organizations should consider incorporating synthetic data into their environments.
1. Your Competition is Already Using it
Within the next two years, Gartner predicts that 60 percent of training data for AI and analytics projects will be synthetically generated. According to the firm, synthetic data can help organizations in numerous scenarios, including:
- When estimation or forecast models based on historical data no longer work
- If assumptions based on past experience fail
- When algorithms can’t accurately model all possible events due to gaps in real-world data sets
In addition, synthetic data can help developers overcome the bias challenges that plague real-world data sets.
2. Many Companies Lack AI-development Skills
The data science skills gap is well documented, and this is a chief impediment for many organizations looking to maximize AI. Using synthetic data can solve this issue because the mathematical nature in which synthetic data is generated forces companies to develop processes to maintain data quality and integrity. Or, to put it another way, using synthetic data compels organizations to educate themselves on data science skills and data governance best practices.
3. Real-world Data is Expensive
Real-world data is expensive to source and can also be unavailable in sectors such as military and defense. Synthetic data, on the other hand, is a cost-effective way of replicating the randomness of real-world data.
Since synthetic datasets can be generated infinitely, companies can efficiently scale their AI projects. In addition, operations surrounding synthetic data are easier to implement. For example, human-in-the-loop (HITL) processes are easier to install because datasets are generated predictably.
5. Privacy and Confidentiality
In industries such as healthcare, privacy concerns have historically presented a roadblock for many potential AI use cases. Because synthetic data isn’t generated from real-world cases, companies can bypass many of the confidentiality concerns.
For more on these and other reasons to consider synthetic data head over to Entrepreneur.