What Is the ROI of Synthetic Data?

As more companies discuss the benefits and challenges of data management, synthetic data solutions are becoming a more frequent topic. After all, artificially generated data without personally identifiable information (PII) sounds like a solution to real data problems like privacy concerns. But what is the ROI of synthetic data? Is it a good idea to invest in synthetic data?

Understandably, there is no definitive ROI figure for generating synthetic data, as it’s still an emerging technology with different use cases across industries. However, the potential benefits synthetic data brings are huge in terms of faster innovation cycles, cost savings, and scalability.

The global synthetic data generation market is expected to grow from $351.2 million in 2023 to $2.3 billion by 2030 at a 30-35% CAGR. And, according to Gartner, nearly 60% of data used for machine learning, AI, and analytics projects will be synthetically generated by 2024. As far as use cases are concerned, right now Nvidia data teams are using synthetic data to fill data gaps while developing and testing its infrastructure for autonomous vehicles.

Continue reading to discover the costs associated with synthetic data, how to measure its success, and its business feasibility. We’ll also discuss the savings and additional revenue streams you can make with synthetic data.

The cost of protecting sensitive data

Businesses which concider to utilize synthetic data for data anonymization can anticipate a return on investment across two dimensions. The first dimension is straightforward to measure, involving tangible benefits such as increased revenue or decreased costs resulting from their enhanced capacity to utilize data. The second dimension is more challenging to quantify but remains crucial, involving the mitigation of risks and costs associated with inadequate data protection.

Real data incurs risks that synthetic data does not—the overwhelming responsibility for privacy protection and the constant fear of breaches. That’s why companies are investing so much in cybersecurity. In fact, Gartner predicts that global spending on security and risk management will increase by 14% in 2024. Real-world data protection is a multi-layered problem: on the one hand, sensitive data must be secured, hidden, or masked from malicious actors; on the other hand, it must be accessible to the vetted individuals who need to work with it. And maintaining many data protection mechanisms, meeting regulations, and consulting experts is expensive. These are the costs we’re talking about:

• Data discovery and classification. Companies need to identify and classify sensitive data across their systems, applications, and databases. And the larger the company, the more expensive this process can be.
• Security measures. These include encryption, data masking, access controls (role-based access, multi-factor authentication, etc.), and data anonymization. These approaches require investment in software, hardware, and ongoing maintenance.
• Data management policies. You must invest in developing and implementing data governance policies and procedures to ensure compliance with data privacy regulations. This also includes expenses for legal advice and audits.
• Data compliance. Companies working in regulated industries (healthcare, finance, etc.) must comply with various data privacy regulations, such as HIPAA, GDPR, or PCI DSS. Compliance efforts, including audits, assessments, and reporting, can be costly.
• Incident response and risk mitigation costs. If a data breach happens, organizations may face substantial costs related to the investigation, notification, remediation, and potential legal fees or fines.
• Employee training. This includes employing data engineers and training other team members to handle sensitive data. You’ll also need to provide ongoing training programs, awareness campaigns, and support to your employees. And they may still end up suffering from phishing or other social engineering attacks.

Of course, companies should protect sensitive data at all costs. But with synthetic data, the costs are much lower. 

Costs associated with synthetic data

Like any technology, synthetic data requires investment. Most of your organization’s synthetic data budget will be spent on the following:

• Software tools. You will primarily need to invest in software tools or platforms for generating synthetic data. Depending on the complexity of the data generation task, these tools can range from simple scripting libraries to advanced AI-driven platforms (such as Syntho).
• Computing resources. This includes the price of cloud computing instances or dedicated hardware for generating and processing synthetic data.
• Validation and testing. The expenditure associated with validating and testing the quality and effectiveness of synthetic data to ensure that it accurately reflects the real-world data distribution.
• Infrastructure and maintenance. These are ongoing costs that include software licenses, server maintenance, and updates to data synthesis algorithms.
• Integration costs. The expenses associated with integrating synthetic data into existing data pipelines, applications, or machine learning workflows. This may include modifying existing systems, developing new interfaces, or retraining models to work with synthetic data.

While this may sound like a big investment, creating synthetic data is actually cheaper than alternative solutions. And that’s just one of the benefits.

• Cost reduction. Data generation doesn’t require much time or special skills compared to collecting and labeling real data, so you probably won’t need a data engineer.
• Time-saver. Generating or anonymizing synthetic data is fast because it is not subject to real-world constraints. For example, you don’t have to wait five months for the ever-busy data engineer to anonymize data or wait several weeks for 10,000 cars to be captured by the camera.
• Scalability. Synthetic data generation can be easily scaled to create large datasets for training machine learning models, enabling faster model development and deployment.
• Data diversity. Synthetic data can help solve problems related to data scarcity and imbalance by creating diverse datasets that better represent the real-world population. Using synthetic data can also reduce the risk of bias or error in real data.
• Maintaining data quality. Generated data meets predefined rules and specifications, so its quality is consistently high across datasets. No inconsistencies, errors, or missing values, which also means there’s no need for cleaning and preprocessing data.

As organizations face the challenge of efficiently managing and accessing their large data lakes (especially for AI model training and data management), synthetic data provides quick access to the necessary information without the need for separate, pre-cleaned, or anonymized datasets.

Synthetic data allows companies to quickly define and generate data based on specific use cases, reducing data storage expenditure and providing flexibility. In addition, synthetic data platforms offer advantages like access to the desired data, rapid synthesis, and easy sharing across teams, eliminating time-consuming and costly preprocessing tasks.

However, synthetic data can not only reduce expenses but also open up new opportunities for data use.

Using synthetic data to create new revenue streams

Companies have numerous opportunities to capitalize on the growing demand for synthetic data:

• Data monetization services. Companies that sell data can use synthetic data generation to amplify their offering. Synthetic data can be generated with the same patterns and dependencies as real data and, most importantly, it doesn’t contain any PII. This allows synthetic data to be shared or sold without restrictions or strict regulations. It also solves the labor-intensive issue of collecting relevant, high-quality real data, which is often scarce.
• Industry-specific applications. Companies that generate synthetic data can sell it to startups operating in highly regulated niches like healthcare, finance, or automotive, where getting real data is costly and time-consuming.
• Research and development partnerships. Companies can partner with academic institutions, research organizations, and established companies to conduct research and development projects using synthetic data.
• Consulting and training services. Companies can provide consulting and training services to help other organizations understand the benefits and applications of synthetic data. This can include providing guidance on data strategies, best practices for generating synthetic data, and training workshops for data scientists and engineers.
• Marketplace for synthetic data. Companies can set up online marketplaces or platforms where users can preview, buy, sell, or exchange synthetic datasets. By facilitating transactions between data providers and consumers, startups can capture some revenue from selling synthetic data.
• Data catalog with sample synthetic data. Organizations can set up secure and controlled data preview environments by creating sandbox environments that enable comprehensive data searches and quick access to relevant data sets.

Using synthetic data for monetization opens up new opportunities for commercialization, gathering data insights, or offering data-driven products that would be harder to achieve with real data.

How to calculate the ROI of synthetic data

We’ve examined the expenses related to synthetic data and the revenue it can generate. Finally, we have enough information to discuss the return on investment.

The ROI of synthetic data can vary depending on the use case and industry. In general, however, a positive ROI indicates that the benefits outweigh the costs, meaning that the use of synthetic data is a profitable investment. A negative ROI, on the other hand, indicates that the costs outweigh the benefits, meaning that using synthetic data in its current form may not be cost-effective.

Here’s what you need to do to calculate the ROI of synthetic data for your business:

1. Identify the benefits

Start with the easy part: What benefits does synthetic data bring to your business in your particular case? Possibilities include reduced expenditure, saving time, improved data protection, scalability, reduced risk, and better performance.

2. Quantify these benefits

This can be tricky: Assign a monetary value or other quantitative measures to the benefits you have identified. For example, estimate the cost savings of using synthetic data compared to real data or calculate the value of improved model performance in terms of increased revenue or efficiency gains. 

Additionally, take into account the transformative impact on data scientists’ workflow. Presently, handling the time-consuming processes to anonymize or de-identify data can take four to six months. Even after this endeavor, data scientists usually access only a subset of the original dataset, constraining their insights. However, with AI generation, the entire dataset is transformed into synthetic ones, enabling more comprehensive analysis and insights for robust AI model development.

3. Evaluate the costs

Calculate the expenditure associated with creating and implementing synthetic data. This may include expenses for software tools, computing resources, expertise, and other relevant costs incurred during the synthetic data generation process.

4. Do the math

To calculate the ROI you should first calculate the Net Benefits:

Total Benefits – Total Costs = Net Benefits

Then, use the following equation:

(Net Benefits ÷ Costs) x 100 = ROI

Although you’ve calculated the ROI of the synthetic data for your company bear in mind this is a raw value and that you should consider the factors discussed in the next section.

What can influence the ROI of synthetic data?

Firstly, you should be realistic in your estimates of benefits and costs. Overestimating benefits or underestimating costs can lead to inaccurate ROI calculations.

It is also important to consider the time frame over which you are measuring ROI. Some benefits, such as improved model performance, may have long-term effects that should be considered.

Speaking of long-term impact, we recommend monitoring the performance and impact of synthetic data over time and adjusting your calculations as needed. ROI is not a one-time calculation but should be reviewed occasionally to account for changes in the environment and evolving circumstances.

Finally, when evaluating the ROI of synthetic data, you should also consider the potential limitations and challenges. For example, the quality of synthetic data is critical, as poorly generated synthetic data may not accurately reflect real-world scenarios and could result in sub-optimal model performance.

Summary

It’s crucial to understand the ins and outs of the ROI of synthetic data, so you can decide whether investing in it is the right choice for your company. Understanding the benefits of using synthetic data for your business and the opportunities for monetizing data is the key to calculating your potential ROI.

At Syntho, we firmly believe that synthetic data will improve data access for analytics, simplify data sharing, and accelerate innovation overall. For us, there’s no question about it: synthetic data is a sound investment and one we encourage you to make.

About Syntho

Syntho provides a smart synthetic data generation platform, leveraging multiple synthetic data forms and generation methods, empowering organizations to intelligently transform data into a competitive edge. Our AI-generated synthetic data mimics statistical patterns of original data, ensuring accuracy, privacy, and speed, as assessed by external experts like SAS. With smart de-identification features and consistent mapping, sensitive information is protected while preserving referential integrity. Our platform enables the creation, management, and control of test data for non-production environments, utilizing rule-based synthetic data generation methods for targeted scenarios. Additionally, users can generate synthetic data programmatically and obtain realistic test data to develop comprehensive testing and development scenarios with ease.  

Do you want to learn more practical applications of synthetic data? Feel free to schedule a demo!