Once you enter the AI domain, you will often come across the term ‘synthetic data.’ In simple terms, the synthetic data is artificially generated data which is designed to duplicate the real-world data.
On the other hand, human-generated data is traditional data, which is collected by humans and can be anything from social media interactions, money transactions, how you interact with specific software, two-person conversations, invoice datasets, image collection, etc.
As the demand for high-quality data is increasing, we are witnessing two trends: people are pushing AI machines to generate synthetic data as close as possible to human-generated data and some people are insisting on human-generated data as they believe it has expression and realness to it.
So in this article, we will explore everything you need to know about human-generated data and synthetic data.
What is Human-generated Data or Real-world Data?
For starters, you are reading this article and Google is learning how much time you are spending on this website which will be used to improve SEO and overall user experience. In other words, human-generated data is nothing but data that is collected from people through various activities, including social media interactions, e-commerce transactions, surveys, sensor inputs, and more.
The most important part of the human-generated data is it represents real-world behaviors, opinions, and patterns, often captured in natural environments.
Here are some sources of human-generated data:
- Internet activity: How humans react to social media posts, clicks, searches, and reviews.
- Purchase history: Online shopping records, spending patterns, etc.
- Sensor data: Smart devices, IoT systems, and wearables.
- Feedback: Surveys, product reviews, interviews, call center conversations, and polls.
Pros and Cons of Human-generated
Pros:
- Real data: Human-generated data provides a true representation of how individuals think, act, and make decisions in real-world scenarios. This authenticity is invaluable, where understanding natural user interactions and preferences is essential to creating meaningful and engaging experiences.
- Context: The beauty of human-generated data is context which includes cultural, temporal, and situational nuances.
- Validation: The data is real and can easily be cross-checked with other data for accuracy (which you can not with synthetic data).
Cons:
- Cost and scalability: This is the biggest disadvantage of human-generated data as collecting the data from authentic sources is quite expensive and it can not scaled for data-specific tasks like machine learning.
- Privacy: The human-generated data might be sensitive and personal. If not handled properly, it might affect hundreds of people’s personal lives.
- Biases: Humans are biased and so does their generated data. Human-generated data can reflect societal biases and may lack diversity.
Applications of Real-world Data
Healthcare
Provides insights into patient journeys, treatment adherence, and health outcomes.
Financial Services
Drives risk assessments, credit scoring, and fraud detection using actual customer transaction data.
Autonomous Systems
Used in training self-driving vehicles to handle real-life scenarios, road conditions, and traffic patterns.
Retail & Consumer Behavior
Tracks real customer interactions, purchase trends, and preferences for personalized marketing.
What is Synthetic Data?
As the name suggests, the synthetic data is artificially generated based on specific scenarios. For example, you can create synthetic data for a random list of names for testing a form application that would look like this:
| Name | Age |
| Alice | 25 |
| Bob | 30 |
| Charlie | 22 |
| Diana | 28 |
| Ethan | 35 |
Here are some of the ways to generate synthetic data:
- Rule-Based Generation: You provide pre-defined rules and parameters to generate synthetic data.
- Statistical Models: Here, the synthetic datasets are created by replicating the statistical properties of the real data.
- AI-Driven Techniques: In this approach, you use modern AI techniques like GANs or variational autoencoders to generate complex synthetic data.
Applications of Synthetic Data
AI Model Training
By far, this is the most important use case of synthetic data as you need a large amount of data that can be scaled to train your AI model.
Autonomous Vehicles
Synthetic data can be used to create simulated environments to train autonomous vehicles for multiple scenarios.
Data Augmentation
Synthetic data is also used to enhance the existing datasets for better machine learning outcomes.
Pros and Cons of Synthetic Data
Pros:
- Privacy Protection: The synthetic data is generated without any real information about humans and does not contain any real-world identifiers which make it privacy-friendly.
- Customization: The synthetic data can be generated with specific parameters and rules which makes it extremely customizable according to specific needs.
- Scalability: This is yet another big advantage of synthetic data as compared to human-generated data, you can scale the synthetic data as per your needs.
- Cost Efficiency: As it can be generated via computers and allows you to generate data in large amounts, it is considered quite cost-effective compared to human-generated data.
Cons:
- Lack of Real-world Perspective: This has to be the biggest con of using synthetic data as poorly designed data can easily fail to represent the real world.
- Rigorous Testing: Generating accurate synthetic data requires you to do rigorous testing to align the generated data with the actual data patterns.
- Technical Expertise: Unlike human-generated data, generating accurate synthetic data requires advanced skills and tools.
Key Differences Between Human-Generated and Synthetic Data
Here are some of the key differences between human-generated data and synthetic data:
| Aspect | Human-Generated Data | Synthetic Data |
| Source | Human activities and interactions | Algorithmic and AI-driven models |
| Cost | Expensive to collect and label | Cost-effective at scale |
| Bias | Reflects real-world biases | Controlled during generation |
| Privacy | Risk of data breaches | Inherently anonymous |
| Scalability | Limited by human activity | Easily scalable |
| Use Case Diversity | Limited by availability | Customizable to niche needs |
How Shaip can Help?
Shaip is one of the leading platforms and has a global network of over 30,000 skilled data specialists spanning 100+ countries and 150+ languages. By adding such diversity of database, we ensure that you get the data that meets precision and efficiency.
For the scenarios where the privacy is utmost priority, Shaip can help you by generating synthetic data that is customized for your needs and aligns with all the privacy regulations. In healthcare, for instance, Shaip can create synthetic data that mimics patient reports without exposing sensitive information.
Shaip is more than just a data provider—it is a strategic partner committed to helping organizations unlock the true potential of AI.
