By Bobby Carlton
77% of companies are either using or exploring synthetic AI data to fill gaps in training, improve marketing and sales, and much more.
A computer creates synthetic AI data by taking advantage of sampling techniques to obtain new information or by performing simulation scenarios that involve interacting with actual processes and models. This type of data can be used to improve AI models and protect sensitive information.
Organizations can benefit from the use of synthetic AI data due to its ability to replace historical information and fill gaps in their data sets. Compared to real-world data, this type of data performs better and is often used in the development of AI models. In addition, it can be utilized to test an AI model’s integrity by gathering data points that are rarely seen in the real world, for instance.
By 2023, Gartner anticipates that synthetic AI data will surpass real-world data within AI models. As its prevalence continues to increase, it will have a major impact on various industries, which will significantly change the economics of data.
Although synthetic AI data can look realistic, it’s not always possible to tell if it accurately represents the underlying trends in real-world information. This is because training a model on this type of data can’t guarantee its accuracy.
What is Enterprise AI?
Machine learning and artificial intelligence are commonly used in enterprise AI to solve business problems. They are typically utilized for areas such as supply chain management, customer service, and process automation.
AI for enterprise is a subset of AI that’s focused on driving business value within large organizations. It enables companies to integrate AI methodology into their data strategy.
Around 77% of companies are currently exploring or using Synthetic AI Data.
How Big is Enterprise AI?
The global AI market reached a value of over $15 billion in 2021. It is expected to grow at a robust rate of 23.0% during the next few years.
In 2022, the global AI market was valued at $136.6 billion. It is expected to reach a value of almost $200 billion by the end of this year. North America generated more than 43% of the market share in 2022. The Asia Pacific market is expected to expand at the highest CAGR of 42% from 2023 to 2032.
The global AI software market is expected to expand the scope of applications by 2024, and reach a value of $126 billion by 2025. It can be categorized into various applications such as machine learning and natural language processing.
How Does Synthetic AI Data Help Your Business?
In the context of enterprise AI, synthetic data holds numerous applications. In particular, it can serve as a substitute for real-world data in certain applications.
Train Models When Real-World Data is Lacking
ML and AI systems require a lot of data to train properly. For some applications, there isn’t enough available data for certain use cases. This can be due to the lack of historical information or the infrequent occurrence of use cases. With synthetic AI data, costs can be lowered when compared to buying real-world data.
Fill Gaps in Training Data
Some data sets can also contain insufficient information for certain applications. For instance, a system that’s trained to identify phone numbers might not be able to handle international numbers.
Balance Out a Data Set
Another common issue when it comes to training data sets is the balance between the non-fraudulent and fraudulent elements. For instance, a historical data set might contain only 1% fraudulent transactions.
“Long tail” Data
Due to the increasing number of use cases involving AI, many companies are now running out of training data. Once these projects are successful, the next step is to implement the same training methods for other use cases.
Speed Up Model Development
One of the most common factors that can affect the development of AI models is the time it takes to collect and process real-world training data. This process can prevent the development of new models. With the use of synthetic AI data, training data can be processed and calibrated before it is available in the market.
Simulate the Future
When fashion trends change, the value of historical data can quickly become outdated. For instance, if people switched from using headphones with a wired connection to wireless ones, the data in the training set might no longer be relevant. Instead, it can be replaced with synthetic data that reflects the new fashion.
Simulate Alternate Futures
Companies can prepare for scenarios that simulate alternate futures if they’re uncertain which direction consumers will go. With the use of simulated data, they can then run scenarios that simulate these options.
Simulate “Black Swan” Events
Black swan events are situations that rarely occur in historical data. However, if they do happen, then organizations need to be ready for the potential impact they could have on their operations. With the use of synthetic data, they can simulate the effects of these scenarios.
Immersive and Interactive Data
The creation of the immersive content and spatial internet, which consists of virtual, 3D representations of social, business, and gaming environments, requires a lot of content. Creating all of this content from scratch would be very expensive. With the use of synthetic AI data, organizations can fill in the gaps that would otherwise be left.
Marketing and Sales
In order to promote their products, advertisers are currently using synthetic images to show off their offerings. For instance, a photograph of an individual wearing a certain color can be transformed into a representation of a model wearing several versions of the same garment. There are also tools that allow users to generate realistic portraits or exhibit different furniture arrangements.
When it comes to marketing and sales, it’s important that the sales team uses samples that are close to the actual use cases of the products or services that they’re presenting. Doing so can prevent the unauthorized use of other customers’ data. With the use of synthetic AI data, the sales team can also speed up the development of the products or services by gathering information that’s similar to the customer’s experience.
Testing of Software
Security and privacy concerns are usually raised when testing new software. Doing so with real data can expose the sensitive information of the users. With synthetic AI data, which doesn’t look like real data, testing software can be performed on a wide range of applications without exposing the sensitive information.
Digital Twins
In court cases, the concept of a shadow jury can be used to test arguments. Companies can also use synthetic data to create digital twins. For instance, Norway’s Labor and Welfare Administration used a method in 2019 to create a complete population that’s been replicated daily. According to Sicular, the agency uses the data for various applications.
Medical and Financial Uses
In the realm of medical and financial data, the use of patient or customer information for training AI models can be very risky. Doing so can expose the sensitive information of the users. However, according to Andy Thurai, the vice president of research at Constellation Research, reverse engineering the data can allow organizations to gain access to valuable insights.
Testing AI Systems for Bias
When AI models discriminate based on illegal considerations, such as religious or racial biases, they can create a public relations disaster or a compliance liability. With the emergence of new AI technologies, such as neural networks, it’s hard to determine how an AI makes recommendations. Through testing the models against synthetic AI data sets, it can be possible to identify hidden biases.
Why Should Your Company Use Synthetic AI Data?
With the ability to create, distribute, and discard synthetic data, organizations can improve the quality of their data used for marketing, sales and operations, and can modify existing sets to remove biases.
According to the ISG, both digital twins and synthetic data can coexist and complement one another in certain applications. In terms of output, the former is derived from real-world data, while the latter is made up of ML-generated information. The infrastructure needed for both will eventually converge and aid companies in their evolution.
A complementary use case can be created depending on the requirements of a particular application. For instance, if the data is unpredictable or if privacy or logistics issues prevent the organization from using real-world data, synthetic AI data is preferred. On the other hand, digital twins are ideal for applications that need a closed loop between their digital and real-world counterparts.
Both synthetic AI data and digital twins can complement each other. For instance, in some applications, the sharing of models with other users can be a requirement. With that in mind, a synthetic data framework can serve as a proxy for digital twins. Conversely, a model that’s close to the real world should be as realistic as possible. This can help accelerate the development of such models.
Through the use of these techniques, organizations can improve their decision-making capabilities and reduce risk by simulating various scenarios. As organizations expand their digital footprint, more secondary data will be generated and utilized.
