5 Reasons Why Data-Driven Companies Should Start Using Synthetic Data Any company that depends upon data utilization knows that real-world data is challenging in terms of both cost and overall applicability: How synthetic data is increasingly coming to the rescue.
By Ralph Tkatchuk Edited by Matt Scanlon
Opinions expressed by Entrepreneur contributors are their own.
AI use in business is growing at an exponential rate. Industries as varied as cybersecurity and retail are now leveraging its power to predict patterns and inform business processes. However, even as its application grows, companies are increasingly grappling with a critical challenge: a lack of training data.
As AI becomes more sophisticated, the relative lack of training datasets is apparent and human intervention in edge cases is increasing. Synthetic data generated by simulators and algorithms and mathematically modeled from real world datasets offers the best solution to this problem. Although computer-generated, synthetic data replicates real-world datasets statistically and offers developers a great way of training AI.
Here are the key reasons why companies should consider its use.
1. The competition already uses it
Synthetic data is far from a budding trend. While most companies rely on real-world datasets, synthetic data use is set to increase rapidly. Gartner predicts that by 2024, 60% of training data for AI and analytics projects will be synthetically generated.
One of the perceived knocks against it is that it lacks "realism." After all, how can a dataset generated by an algorithm match the randomness that a real-world one offers? While this objection has some truth to it, the degree of randomness in real-world data is exaggerated. While they do have that component, real-world datasets lend themselves well to pattern analysis and mathematical modeling. Thus, replication and extrapolation is simple.
Synthetic data modeling techniques are highly sophisticated, and thanks to complex statistical models, algorithms can replicate real-world data accurately. (Humans will have to get involved in edge-case scenarios, but that's something that occurs even with real-world data.)
Moreover, synthetic data helps developers overcome a major flaw present in real-world datasets: bias. AI mishaps such as the ones suffered by Meta (formerly Facebook) and Google highlight how biases in real-world data can lead to public embarrassment, not to mention incorrect conclusions.
Synthetic data allows developers to examine their datasets for biases and eliminate them. Thus, AI is trained efficiently and produces the right outcome.
Related: What You Need to Know About Data Modeling
2. Companies often lack AI-development skills
AI development has occurred at a breakneck pace, but most companies still lack deep expertise in implementing associated projects. This situation occurs due to a lack of skilled developers as well as the relatively early stage of its development. The frequent result is an AI program that achieves halting success, and with mixed results.
Gartner highlights a lack of internal data science skills as one of the major roadblocks to companies improving their AI posture. They collect more data than ever before, but cannot place it in the right context. The proliferation of ad-hoc business intelligence tools has also reflected the lack of data science skills at most organizations, with companies routinely reaching incorrect conclusions.
The result is that most real-world data sits unused, or even worse, used incorrectly. Synthetic data offers a solution to this mess by giving companies a chance to examine their biases before generating datasets. This forces employees to learn data science skills and become aware of the biases that might derail their analysis.
Thanks to the mathematical nature in which synthetic data is generated, companies must develop processes to maintain data quality and integrity. As a result, the synthetic data creation process forces companies to learn data science skills and implement data governance processes.
Using synthetic data thus not only improves AI accuracy, it automatically pushes companies to adopt data management best practices. Any company with this posture will benefit in the long run.
Related: How Enterprise Companies Are Changing Recruitment With AI
3. Real-world data is expensive
While real-world data is often pushed as an ideal, it is expensive to source (for some industries prohibitively) and sometimes unavailable. For instance, in the defense and military sectors, real-world data can never account for all possible edge cases; executing them in the real world is simply not an option. But synthetic data offers an elegant and cost-effective solution. The randomness that real-world data offers can be mathematically replicated within synthetic datasets, giving developers more freedom to train their AI models.
Real-world data is also extremely biased. Gartner predicts that by the end of 2022, 85% of AI projects will deliver incorrect results due to biased real-world datasets. Putting all of these factors together, it's easy to see why companies have had issues implementing AI on a broader scale.
4. Scalability
Scaling AI projects is currently difficult due to the challenges previously mentioned. As more use cases are added to a company's AI stack, real-world datasets fall short with regard to providing AI algorithms a complete picture. The result is that human intervention increases as AI projects grow broader in scope. This is the opposite of the intended result. Synthetic data allows companies to scale easily since these datasets can be generated infinitely.
Even better, operations surrounding synthetic data are easier to implement. For instance, HITL processes are simpler to install, since datasets are generated predictably. Labeling, categorizing and annotating datasets is simple, giving companies a repeatable process they can rely on. A knock-on effect is easy filtering: Developers can quickly isolate use cases and deeply train their algorithms without spending time examining the data context. Also, use cases tend to overlap within real-world datasets, something that can be prevented within synthetic data. Thus, AI programs receive deep instead of broad training.
Related: 3 Data Quality Issues That Could Impact Your Judgment
5. Privacy and confidentiality
The healthcare industry possesses among the highest numbers of potential use cases for AI implementation. However, privacy is a stumbling block. Patient treatment and other medical records cannot be used without permission. Besides, a patient is highly unlikely to approve the use of private information in this manner.
Synthetic data helps companies bypass these issues, since they aren't generated from real-world cases. Instead they replicate such cases and extrapolate data mathematically. Thus, confidentiality is preserved. In addition, all of the previously mentioned advantages of using synthetic data play out here as well.
A no-brainer
AI use holds massive potential for industries worldwide, but the lack of data is presenting serious stumbling blocks. Synthetic data offers the best solutions, thanks to a combination of removing biases, easy annotation and lack of privacy issues.
