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.