Synthetic Data Vs Real Data 

There has been an increase in interest in synthetic data over the past few years for various applications such as machine learning and data analytics. Research predicts that synthetic data will surpass real data by 2030. However, some executives and business leaders may not be familiar with the concept due to its differences from real data. 

GENINVO uses Datalution™, the all-in-one solution for generating synthetic data for testing electronic data capture screens, edit checks, Data management activities (as part of UAT Process), programming, and statistical setup activities.

What is Synthetic data? How is it created? 

Synthetic data is artificially generated data that is created using computer algorithms and models rather than being collected from real-world observations. It can be used to replace or augment real-world data in various applications, such as testing and training machine learning models, conducting simulations, or generating new data for research purposes. 

The process of creating synthetic data typically involves the following steps: 

Defining the data requirements: This involves specifying the characteristics and properties of the data to be generated, such as the size, format, structure, and distribution of the data. 

Designing the model: Based on the data requirements, a computer model is designed to simulate the generation of synthetic data. The model may incorporate statistical, probabilistic, or machine learning algorithms, depending on the type of data being generated. 

Generating the data: Once the model is designed, it can be used to generate synthetic data by simulating various scenarios and events. The data can be generated in batches or in real-time, depending on the application. 

Validating the data: Before using synthetic data for any application, it is important to validate its quality and accuracy. This involves comparing the statistical properties of synthetic data with those of real-world data to ensure that they match. 

There are several tools and platforms available for creating synthetic data, ranging from open-source software to commercial products. Some popular methods for generating synthetic data include generative adversarial networks (GANs), variational autoencoders (VAEs), and Monte Carlo simulations. 

What are the key benefits of synthetic data over real data? 

Synthetic data is generated by computer programs or algorithms that simulate the statistical properties of real data without containing any personal or confidential information. Here are some potential benefits of using synthetic data over real data: 

Privacy Protection: Synthetic patient data can be generated to replace sensitive or personal data, protecting individual privacy and reducing the risk of data breaches. 

Cost-Effectiveness: Synthetic data can be generated at a lower cost than collecting and processing real data, especially for large datasets or data with high variability. 

Availability: In some cases, real data may not be available or difficult to obtain due to legal, ethical, or practical reasons. Synthetic data can be generated quickly and easily, providing a viable alternative. 

Control over Data Properties: Synthetic data can be tailored to meet specific research needs or scenarios by controlling data properties such as distribution, noise, and correlation. 

Reproducibility: Synthetic data can be generated repeatedly with the same statistical properties, ensuring reproducibility and consistency of results. 

However, synthetic data may not always be suitable or replaceable for real data in certain applications or domains, especially when the data needs to capture the complexity and richness of real-world phenomena or behaviors. Additionally, the accuracy and validity of synthetic data depend on the quality and representativeness of the underlying algorithms used for generation. 

What are the challenges with using synthetic data against real data? 

Using synthetic data instead of real data has its advantages in certain situations, such as when the real data is sensitive or unavailable. However, there are also several challenges with using synthetic data that need to be considered. Some of the key challenges include: 

Lack of variability: Synthetic data may not be able to capture the full range of variability present in the real data. This can result in models trained on synthetic data being less accurate or less robust than models trained on real data. 

Bias: Synthetic data can be biased in ways that are not representative of the real data. This can occur when the synthetic data is generated based on assumptions that do not hold in the real world or when the synthetic data is not diverse enough to capture the full range of variation present in the real data. 

Data quality: Synthetic data may not be of the same quality as real data. This can lead to models trained on synthetic data performing poorly when applied to real data. 

Limited applicability: Synthetic data may only be applicable to certain contexts or scenarios and may not generalize well to other situations. 

Ethical considerations: Using synthetic data can raise ethical concerns, particularly if it is used to simulate sensitive or personal data. This can include issues around privacy, consent, and data ownership. 

Overall, while synthetic data has its advantages, it is important to carefully consider the potential challenges and limitations before deciding to use it instead of real data. 

Which type of data should be used for specific applications? Synthetic or Real? 

The choice between synthetic and real data depends on the specific application and the goals of the project.  

Here are some general guidelines: 

If the goal is to train a machine learning model to perform a specific task, such as image recognition, natural language processing, or speech recognition, then both synthetic and real data can be useful. Synthetic data can be generated in large quantities and can be labeled more easily than real data. Real data, on the other hand, can provide a more realistic representation of the problem domain. 

If the application involves safety-critical systems, such as autonomous vehicles or medical devices, then real data is generally preferred. This is because real data can capture the complexity and unpredictability of the real world, which is crucial for ensuring the safety and reliability of such systems. 

If the goal is to perform simulations or virtual testing, then synthetic data may be more appropriate. Synthetic data can be generated to represent a wide range of scenarios and can be used to test the system under different conditions. 

In summary, the choice between synthetic and real data depends on the specific application and the goals of the project. In general, a combination of both synthetic and real data can be beneficial for training machine learning models and testing systems. 

How can GENINVO help you generate ‘meaningful’ synthetic data? 

Introducing Datalution ™, A Meaningful Synthetic Data Generation Solution    

  • Leverages clinical study documents 
  • Ensures data are representative of the “Patient Journey” 
  • Generates “Meaningful” test data:   
  1.  Chronological order of the dates/visits. 
  1. Generates the test/assessments based on the protocol. 

Feel free to reach out at: Contact us – GENINVO to know more about Datalution™ technology and services. 

More Blogs

Transforming Document Creation in Life Sciences with DocWrightAI™ – GenInvo’s Advanced AI Assistant!

Transforming Clinical & Regulatory Medical Writing through the Power of AI!  GenInvo is leading the way by accelerating the availability of…
Read More

Embracing the Digital Era: The Transformative Power of Digitalization in Medical Writing

In recent years, the widespread adoption of digitalization has revolutionized various aspects of society, and the field of medical writing…
Read More

Data Masking and Data Anonymization: The need for healthcare companies

In the healthcare industry, the protection of sensitive patient data is of utmost importance. As healthcare companies handle vast amounts…
Read More

Artificial Intelligence in the Healthcare Domain: How AI Reviews Clinical Documents

Let’s know what Clinical Documents are.  Clinical Documents are written records or reports documenting various aspects of patient care and…
Read More

Importance and examples of usage of Data Anonymization in Healthcare & Other sectors

Data anonymization plays a critical role in healthcare to protect patient privacy while allowing for the analysis and sharing of…
Read More

Data Anonymization and HIPAA Compliance: Protecting Health Information Privacy

Data anonymization plays a crucial role in protecting the privacy of sensitive health information and ensuring compliance with regulations such…
Read More

Automation of Unstructured Clinical Data: A collaboration of automation and Medical Writers

In the field of healthcare, clinical data plays a crucial role in patient care, research, and decision-making. However, a significant…
Read More

Quality Control of the Methods and Procedures of Clinical Study

Methodology section of the Clinical Study Report (CSR) provides a detailed description of the methods and procedures used to conduct…
Read More

Automated Quality Control: Get the best out of your Clinical Study Report Review 

What are Clinical Study Reports?  Clinical study reports (CSRs) are critical documents that summarize the results and findings of clinical…
Read More

Clinical Study Results: Quality Control on study findings and outcomes

Clinical Study Reports, or the CSRs, are comprehensive documents providing detailed information about the design, methodology, results, and analysis of…
Read More

Big Save on Time > 60%, A case Study: DocQC™ Tested on 25 Studies.

Medical Writers have provenly spent a lot of time historically, in reviewing the Clinical Study Reports. Clinical Study Reports, or…
Read More

Data Anonymization in the Era of Artificial Intelligence: Balancing Privacy and Innovation

Data anonymization plays a crucial role in balancing privacy and innovation in the era of artificial intelligence (AI). As AI…
Read More

Automated Quality Control: Get the best out of your Clinical Study Report Review

What are Clinical Study Reports?  Clinical study reports (CSRs) are critical documents that summarize the results and findings of clinical…
Read More

Data Redaction: Safeguarding Sensitive Information in an Era of Data Sharing

Data redaction is a technique used to safeguard sensitive information in an era of data sharing. It involves selectively removing…
Read More

Building a Strong Foundation: Robust Metadata Repository (MDR) Framework for Automated Standard Compliant Data Mapping

Pharmaceutical and biotechnology companies operate within a constantly evolving regulatory landscape, where adherence to standards set by organizations like the…
Read More

Digitalization of Medical Writing: Balancing AI and Rule-based algorithms with Human Supervision in Medical Writing QC

What is Digitalization of Medical Writing?  The digitalization of medical writing refers to using digital technologies and tools to create,…
Read More

The Rise of Differential Privacy: Ensuring Privacy in the Age of Big Data

The rise of differential privacy is a significant development in the field of data privacy, especially in the age of…
Read More

Role of Intelligent Automation: How Intelligent Automation transforms the Clinical Study Document Review in Real Time

Clinical Study Reports play a critical role in assessing the safety and efficacy of new medical treatments. Review of these…
Read More

Automation on Clinical Study Report: Improve the Speed and Efficiency of document review. 

Clinical Study Report (CSRs) are critical documents that summarize the findings and results of clinical trials. These reports require a…
Read More

Digitalization of Quality Control in Medical Writing: Advantages Digitalization brings for the critical aspects of Quality Control

Quality control in medical writing is a critical aspect of ensuring the accuracy, clarity, and reliability of medical documents. It…
Read More

Contact Us​

Skip to content