The Benefits of Synthetic Data for Your Enterprise Data Strategy

Businesses collect information from countless sources, yet many struggle to extract value from it. In many companies, the datasets are siloed, unstandardized, and bound by data security and privacy laws. These challenges escalate if you lack an effective enterprise data strategy.

Data strategies benefit from high-quality data, which is hard to get due to scarcity and legal restrictions. Luckily, there’s a real game changer—synthetic data for enterprise. 

Synthetic data companies provide tools that can multiply, diversify, and adjust production data. All the while, the datasets you get adhere to strict data protection and security policies. Let’s break everything down.

What is an Enterprise Data Strategy?

A data strategy is a long-term plan that outlines how you collect, store, leverage, and share data assets to achieve your business objectives. In simple words, an enterprise data strategy helps companies deal with their data.

There are several components to an enterprise data strategy, as shown in these examples:

• Data governance means policies, procedures, and standards for data management that ensure integrity, standardization, and secure access.
• Quality management ensures accuracy, consistency, and timely access to data.
• Tools and infrastructure management refers to software that allows companies to integrate, store, visualize, and analyze the available datasets.
• The review process includes regular architecture audits, regulatory compliance, and quality standards.

In addition, a thorough strategy helps you make correct decisions based on verified insights, leverage advanced technologies, and follow privacy laws.

Why Do Businesses Need a Data Enterprise Strategy?

Companies only rely on access to high-quality data and testing datasets. Without a reliable framework, companies risk data loss, errors, and non-compliance. On the other hand, businesses stand to gain several benefits from a solid enterprise data strategy.

• Eliminate data silos: Silos happen when too much information is spread across separate systems within an organization. Employees may have access to only a portion of information, resulting in errors, duplication of effort, missed opportunities, or conflicting reports. What enterprise data strategy does is unify datasets across the company, ensuring quick access to real-world or anonymized data.
• Improve decision-making: Teams often have trouble finding relevant data or trusting its accuracy. A defined strategy keeps the data up-to-date, consistent, and accessible, enabling more accurate decisions that align with business goals.
• Prevent “shadow IT” practices: Poor data management can drive employees to use unauthorized tools or systems, making it harder to maintain compliance and introducing security risks. Companies with a robust strategy understand the needs of their departments and provide the necessary data management tools.
• Provide scalability: With proper planning, businesses handle the increasing volume and complexity of real data. The strategy ensures your systems can evolve with your technological advances and help implement innovative artificial intelligence (AI) and machine learning (ML) solutions.
• Guarantee regulatory compliance: Data privacy laws like GDPR, HIPAA, or CCPA impose strict requirements on handling personally identifiable information (PII) and protected health information (PHI). Strong governance policies and tools, such as those that help create synthetic data, can help avoid regulatory fines.
• Reduce security vulnerabilities: The strategy includes security mechanisms like encryption, access control, and backup. They protect real data from unauthorized access, misuse, or corruption, reducing the chance of breaches and subsequent financial issues.

Numerous tools can improve your strategy. One of them is the implementation of synthetic data for enterprises.

How Does Synthetic Data Enhance an Enterprise Data Strategy?

Synthetic data is artificially generated datasets that mimic the statistical properties of real data but without sensitive information. Unlike anonymized or pseudonymized data that alter the existing datasets, synthetic data is created from scratch. Complex algorithms produce it based on the existing data with references and patterns intact. Sensitive information is replaced with mocked data and random values. Gartner’s 2023 Hype Cycle Report for Generative AI (as presented by AI Authority) shares a few insights about AI-generated synthetic data in corporate environments. According to the report, over 80% of the data in enterprises will be artificially generated by 2026, up by over 75% since 2023. Synthetic data doesn’t completely revamp the enterprise data strategy, but it improves its performance at several stages — especially in data collection, usage, and sharing.

• Traditional data collection can be slow and expensive, especially in sectors like finance and healthcare. You can accelerate testing and analytics by generating synthetic datasets from existing data on demand.
• Real data is restricted by privacy regulations. However, synthetic data is crucial for enterprise privacy. Useful synthetic data is void of any PII or PHI, which virtually eliminates the risk of re-identification of individuals and exempts it from data privacy regulations.
• Real-world datasets are often biased or incomplete, limiting the effectiveness of testing and machine learning. As Gartner states, synthetic data can be used to address biases in AI models by generating synthetic test data that covers a wider range of scenarios. 
• Synthetic data reduces the costs associated with sourcing, preparing, and securely storing real-world data. You don’t have to expend as many resources on regular compliance checks and data handling practices (like eliminating data after a certain time).

Integrating synthetic data into your enterprise data strategy offers an immediate return on investment. The ability to produce realistic synthetic data is also incredibly useful in different business spheres.

Common Use Cases of Synthetic Data for Enterprises

Synthetic data generation offers a faster, scalable way to leverage data. It’s particularly useful for enterprises that develop software, conduct complex research, and train ML models. These are the most common use cases.

Privacy and compliance management

Businesses must anonymize real-world data before using it for any purpose. However, current anonymization techniques, such as data masking, can be time-consuming and costly. They may also reduce the quality of information and leave some risk of de-identification.

None of this is a problem with synthetic data platforms. Synthetic data retains all the nuances and statistical properties of the source data with no sensitive identifiers. It allows you to generate compliant and standardized datasets that don’t require additional processing, so you can ensure data quality and meet strict privacy guidelines.

Machine learning training

Machine learning models require diverse data for training. Without sufficient data, the algorithms can introduce biases (imbalances, incomplete data, or overrepresentations) that negatively impact the fairness and accuracy of models.

Structured synthetic data can transform available training data into compliant datasets. It allows you to upsample, subset, and rebalance groups, helping create more representative samples for AI training. For example, companies can create diverse data for job application screening models that don’t include gender or racial biases. 

With such capabilities, you can improve the accuracy of predictive algorithms and make the models fairer.

Software development and testing

Enterprises should establish a robust test data management framework to identify as many issues as possible during software development. 

Synthetic data allows companies to produce realistic testing environments where they can simulate various user interactions and malicious attack patterns. It can help quickly scale up testing to stress-test systems. This accelerates the development and testing cycles, resulting in more user-focused and resilient software.

For example, a financial software company can use synthetic datasets to simulate thousands of transactions to test the system’s fraud detection capabilities. 

Business intelligence and analytics

Organizations use artificial datasets for analytics and business intelligence when their real-world data is incomplete or imbalanced. Because it closely resembles real data, you can use it for prototyping and hypothesis validation, enabling you to fine-tune the AI model before deployment.

In particular, structured synthetic data can help predictive modeling that accurately forecasts trends, identifies vulnerabilities, and optimizes operations. A retail company could use synthetic customer data to develop product recommendation algorithms. In other words, you improve personalization strategies while protecting customer privacy.

Data monetization

Enterprises with large volumes of unique data can transform into synthetic data providers. Rather than sharing actual data, which involves privacy concerns, you can upsample and sell synthetic datasets. 

Many companies would rather buy synthetic datasets than deal with collection, processing, and anonymization. For example, a telecom company could produce and sell artificial data based on customers’ calling habits or internet usage. Similarly, healthcare companies sell synthetic patient data to research facilities.

Healthcare (clinical) research

Healthcare and pharmaceutical companies often run into data scarcity problems. Their existing datasets may be limited in scope for rare conditions and edge cases.

You can produce synthetic datasets from actual patient data to upsample specific cases or demographic profiles. This would help the researchers have enough data to test hypotheses, develop treatments, or design drugs—all with fewer risks of bias. 

Additionally, incorporating artificially generated data allows healthcare companies to share their research while following HIPAA. This leads to faster research in the industry as a whole. Considering all these use cases, enterprises should be aware of the technical limitations of synthetic data generation.

Potential Limitations of Synthetic Data for Your Enterprise

Synthetic data platforms can lack some subtle nuances found in actual datasets or produce outright incorrect results. The most common problems right now include the following: 

• Accuracy and representation challenges: Not all synthetic data companies have tools advanced enough to preserve the referential integrity and statistical properties of real data. This can lead to faulty predictions, flawed analyses, and poor business outcomes. Enterprises need rigorous validation, such as comparing the model output and running stress tests. 
• Generative AI hallucinations: AI algorithms can sometimes “hallucinate,” meaning they generate incorrect or misleading data points that seem statistically accurate. An enterprise data strategy should include regular human reviews to prevent such problems.
• Amplified anomalies in datasets: If the original data contains anomalies or outliers, there is a risk that synthetic data could either amplify these anomalies or obscure them. This can make the model too sensitive to rare patterns, fail to generalize to broader datasets or overlook critical events. 

Reliable synthetic data generation platforms like Syntho have measures that help mitigate these limitations. Their algorithms are trained on vetted datasets and regularly fine-tuned to maintain statistical accuracy and compliance.

We offer several additional features that help produce high-quality data. For example, organizations can adjust synthetic data generation rules, scan for PII and PHI in datasets, and validate the output.

Strengthen Your Data Strategy with Syntho

Synthetic data generation fits into enterprise data strategies, providing businesses with privacy-compliant ways to handle sensitive data. It empowers businesses to overcome burdensome data privacy that complicates data sharing.

Artificial datasets have several applications, from test data management to clinical research. Advanced platforms can even help you turn data into a marketable asset.

Reliable synthetic data generation platforms can secure access to accurate and compliant data for your needs. Want to learn more? Contact us to learn how Syntho’s expertise can strengthen your strategy.