We continue to observe the growing importance of AI and machine learning systems and with it, the growing demand for large, diverse and well-labeled datasets.
It's worth noting that real-that is, "real-life"-data is often:
- difficult to collect, especially at scale;
- expensive to acquire and annotate;
- fraught with privacy, data protection and regulatory issues;
- limited in terms of representing rare scenarios or edge cases-those that are rare but critical.
In this context, an alternative is increasingly being considered: synthetic data-computer-generated data, often using 3D simulations and modern rendering techniques. One of the leading platforms in this area is SKY ENGINE AI, whose solutions are worth considering if we're thinking about scaling AI projects.
Below, we discuss why we need such data - what problems they solve, what opportunities they offer and when they're actually worth using.
What is synthetic data-and what does SKY ENGINE AI offer?
Synthetic data is information generated artificially-through simulations, algorithms, or generators - rather than by recording real-world activity. In the context of computer vision, this often means rendering 3D scenes, simulating lighting, materials and sensors and then automatically creating annotations: semantic masks, depth maps, bounding boxes, 3D key points, normal maps, etc.
The SKY ENGINE AI platform goes further-offering the so-called Synthetic Data Cloud: an environment that can generate massive, well-labeled datasets in various "modalities" (e.g., standard photography, other sensor modes, simulated conditions, different perspectives), with full control over the scenarios.
This transforms synthetic data from a "set of examples" into a resource for design, experimentation and scaling - with the benefits of speed, flexibility and security.
Why do we need synthetic data? Key advantages
Scalability and rapid availability
When you need large amounts of data-e.g., hundreds of thousands or millions of images-collecting them physically can be expensive, time - consuming and often downright impossible. Synthetic data can be generated relatively quickly and on a large scale-without the need for a photo campaign, logistics, people, or processes.
Rich, precise annotations ("ground truth")
With synthetic data, objects, scenes, sensors and labels (segmentation, bounding boxes, depth maps, normals, 3D keypoints, etc.) are generated automatically and with high precision. This provides a huge advantage over manual labeling: no annotator errors, complete consistency and the ability to generate annotations that would be costly or even impossible in real life (e.g., precise depth maps, normals, multimodality).
Coverage of rare or challenging edge cases
Real data often lacks rare - but crucial-scenarios: extreme lighting conditions, manufacturing errors, unusual poses, defects and exceptional scenes. Synthetics allows for the deliberate generation of such cases, allowing for the construction of a robust, more universal and predictable model.
Privacy, security and ease of data sharing
Some real data can be sensitive-medical, industrial, or private. Synthetic data does not contain PII or sensitive data-it can be freely used, shared among teams and tested without compliance concerns.
Flexibility and control - data as a tool, not a constraint
Synthetic data is not a set of predetermined information - it is a tool. You can design scenarios, test different conditions, randomize parameters and experiment. This makes work on an AI model more experimental, iterative and adaptive.
Why is there less and less real data today, more and more processed?
As AI develops, much of the data that was once "fresh" is becoming processed: compressed, anonymized and transformed, potentially losing quality or original features. Accessing the original data is also becoming increasingly difficult-due to privacy, regulatory and cost reasons-for reasons of privacy.
Synthetic data is becoming a response to several trends:
- Increasing demands for data diversity - for a model to cope with multiple conditions, scenarios and perspectives;
- The development of sensors and multimodality - to train models using not only RGB but also other sensors;
- The need for rapid iteration, testing, validation and retraining - development cycles are becoming more frequent;
- Legal and ethical regulations - privacy, data protection and compliance-which limit or complicate the collection of real data;
- The desire to democratize access to data - smaller companies, startups and R&D teams may have limited access to large datasets-synthetic data equalizes this access.
This makes synthetic data not just an "option," but often a necessity for ambitious AI projects.
When synthetic data makes sense - and when it's worth combining it with real data
Synthetic data is a powerful tool, but it won't always completely replace real data. It's best suited when:
- we need large and diverse datasets and collecting real data is expensive or impossible;
- precise annotations are required, difficult to create manually;
- we need coverage of rare scenarios, edge cases and extreme conditions;
- we need rapid iteration, testing and experimentation;
- we need to protect data privacy, or the data is sensitive.
At the same time, in many applications combining synthetic data with real data makes sense: synthetic data provides scale, diversity and coverage of gaps - real data adds authenticity, natural fluctuations and the unpredictability of the world. This combination often yields the best results.
Synthetic data is not a fad, it's the next stage of AI development
Synthetic data today is not an "add-on" or "replacement," but often a key element of AI strategies. Thanks to it, it enables:
- scaling AI projects without the limitations of data availability,
- rapid experiments, iterations, tests and adaptations,
- building more resilient, universal and well-structured AI systems,
- adhering to privacy and compliance standards,
- access to data (and training data) even for smaller teams, companies and startups.
Platforms like SKY ENGINE AI demonstrate that generating synthetic data can be a professional, optimized process - not an experiment. If we plan our scenarios well, ensure quality and realism and-where necessary - combine synthetic data with real data - we will have a tool that truly accelerates AI development.
Synthetic data is the future, but above all-it is a sensible, practical and modern way of working here and now.