How Synthetic Users Differ from Synthetic Data and Avatars
From Behavioral Simulation to Data Protection: The Ultimate Guide to Understanding the Difference Between Profiles, Information, and Artificial Records
Although the terms are often used interchangeably, synthetic data, avatars, and synthetic users solve different problems. While synthetic data and avatars focus on statistically replicating real-world information to ensure privacy and train AI models, synthetic users go further — they are complex profiles with narratives, behaviors, and motivations created by algorithms to simulate users (not just their data) in research, design, and marketing tests.
Keep reading for a detailed comparison of the origins, generation processes, and practical applications of each approach. By the end of this guide, you’ll know exactly when to use the security of synthetic data to train models — and when to apply the intelligence of synthetic users to accelerate your innovation and user-centered design strategies.
What Are Synthetic Users, Synthetic Data, and Avatars?
Definition of Synthetic Users
Synthetic users are virtual profiles created by Artificial Intelligence to simulate the characteristics, behaviors, and stories of real users. They’re designed to consistently and diversely reflect human populations, improving research, simulations, and interaction design.
What Is Synthetic Data?
Synthetic data refers to artificially generated information produced by algorithms using statistical models that replicate real-world characteristics without exposing real individuals — ensuring strong security and privacy.
What Are Synthetic Avatars?
Avatars are locally generated records produced through stochastic simulations that create “fake,” but statistically similar, versions of original data. They’re a specific form of synthetic data focused on balancing privacy and local usability.
Origin and Generation
Real-World vs. Artificial Data
While real-world data comes directly from user observations and logs, synthetic data and synthetic users are artificially created to mirror real patterns without any direct link to individuals.
Generation Processes
Different techniques — such as generative models, stochastic simulators, and machine learning algorithms — produce these artifacts through tailored approaches depending on the purpose and application.
Privacy and Security
Risks Associated with Real Data
Even anonymized real data carries re-identification risks and must comply with strict regulations like GDPR and LGPD.
Advantages of Synthetic Data and Avatars
Because they’re not tied to real people, they offer superior security, enabling safe data sharing and AI training without compromising privacy.
The Privacy-Utility Trade-Off
Avatars aim for higher explainability in the generation process, which facilitates compliance verification, while synthetic data can vary in quality and transparency.
Quality, Diversity, and Applicability
Bias Treatment
While real data can contain biases and gaps, synthetic data generation techniques allow the creation of balanced and diversified datasets.
Narrative Consistency
Synthetic users — such as those developed under the Synthia approach — feature coherent stories and trajectories, essential for social simulations and behavioral studies.
Practical Applications
Synthetic users support design, marketing, and research, whereas synthetic data and avatars power analytics, model training, and complex system testing.
Traditional Anonymization Methods vs. Synthetic Approaches
Conventional Techniques
These include masking, suppression, and k-anonymity — methods that modify real data but often sacrifice utility and analytical precision.
Avatar Approach and Explainability
Avatars are an innovation that offer greater transparency and measurable metrics for evaluating privacy protection.
Impact on Data Quality
Synthetic data and avatars preserve important statistical properties for analysis, reducing the distortion risks typically found in traditional anonymization methods.
Examples and Use Cases
Design and Marketing
Synthetic users are employed to simulate user experiences, predict behaviors, and support strategic decision-making — all without exposing real data.
Artificial Intelligence Training
Synthetic data and avatars are widely used to feed machine learning models without disclosing sensitive or personally identifiable information.
Research and Development
Both approaches mitigate legal and ethical barriers, enabling experimentation and analysis in highly regulated domains.
Summary Comparison
| Aspect | Synthetic Users | Synthetic Data | Avatars |
| Origin | Detailed profiles with realistic narratives and metadata | Artificial data statistically generated | Local simulations that create versions of real datasets |
| Purpose | Simulate users for design, research, and marketing | Large-scale data generation for analysis and model training | Balance between privacy and local utility |
| Privacy | High — no link to real individuals | High — free of real personal data | High — includes measurable privacy metrics |
| Consistency | High — includes narrative and temporal evolution | Variable — depends on the generative model | Good — focused on explainability |
| Application | Interaction, social simulations, and marketing | Modeling, testing, and data analytics | Local analysis and compliance verification |
Related Questions
What makes a synthetic user different from an avatar?
Synthetic users are complex profiles that reflect multiple human dimensions — such as narrative and behavior — while avatars are specific stochastic versions designed to locally replace real data.
Why use synthetic data instead of real data?
To prevent privacy risks, reduce costs, and enable the generation of large, customized, and balanced datasets for use in artificial intelligence applications.
What are the main challenges in generating synthetic users?
Ensuring narrative consistency, demographic diversity, realistic alignment, and avoiding biases inherited from original data sources.
How do avatars help with data protection?
They replace original records with synthetic versions that maintain statistical properties, reducing re-identification risks and simplifying privacy audits.
When should I use synthetic users instead of traditional synthetic data?
When the goal is to simulate complex human behaviors for design, marketing, or social research — not just quantitative analysis.
Which regulations impact the use of real versus synthetic data?
Laws such as LGPD (Brazil) and GDPR (Europe) restrict the use of real personal data, encouraging safer practices like synthetic data and synthetic user generation.
How does Synthia enhance the quality of synthetic users?
Synthia builds realistic backstories by integrating real user data and temporal evolution, ensuring diversity and consistency for both scientific and business applications.
Does “synthetic” always mean privacy-safe?
Generally yes, but it’s crucial to ensure the generative model doesn’t reproduce identifiable data and follows robust anonymization standards.
What are possible applications of avatars across industries?
Sectors such as healthcare, finance, and telecommunications use avatars to develop predictive models and perform sensitive analyses without exposing real data.
How can companies start creating synthetic users?
By aggregating data, applying advanced generative models, and validating the consistency and realism of the resulting users for their specific use cases.
What common mistakes should be avoided when creating synthetic data?
Overlooking the quality of original data, failing to validate diversity, and neglecting safeguards against potential data leakage.
Can synthetic data completely replace real data?
It depends — synthetic data complements and scales real data, but validation with real-world datasets remains essential in many scenarios.
What benefits does MJV offer with synthetic user–based solutions?
MJV delivers innovative strategies for creating and applying synthetic users — combining security, realism, and business applicability to address complex challenges.
How does Artificial Intelligence enhance the generation of synthetic users?
Through advanced language models capable of crafting coherent narratives and diverse profiles that accurately mirror real behavioral patterns.
Why is explainability important in anonymization methods such as avatars?
Because it enables regulatory auditing, builds trust, and allows fine-tuning to optimize the balance between privacy and data utility.
MJV Can Help
Discover MJV AIRA and see how you can use synthetic users to accelerate your company’s processes. Click here to learn more.
Back