Synthetic Data 2023
What is Synthetic Data?
Synthetic data is data that is artificially created rather than gathered from real-world sources. Algorithms can generate synthetic data that is used in model datasets for testing or training purposes. The synthetic data can be made to resemble operational or production data and can help train machine learning models or test mathematical models.
There are several advantages to using synthetic data: it can reduce the restrictions on using regulated or sensitive data, it can be tailored to match conditions that real data doesn’t allow, and it can be used to generate large training datasets without needing manual labeling of data.
Why is Synthetic Data So Important?
In order to create effective neural networks, developers need access to extensive datasets which have been properly labeled. Usually, models which are trained using data that is more diverse are more accurate. However, the process of collecting data-sets containing anywhere from a few thousand to tens of millions of elements is very time-consuming, and often too costly to be viable.
Synthetic data can help you save money while also ensuring that your data represents the real world. This can help reduce bias and privacy issues.
Since synthetic datasets are automatically labeled and can include infrequent but important edge cases on purpose, they might be more advantageous than real-world data.
Advantages of Synthetic Data
Data scientists should not care if the data they are using is real or fake, as long as it is an accurate representation of patterns, is balanced and unbiased, and is of high quality. Using synthetic data allows for more data to be used and for it to be of better quality, giving data scientists an advantage.
- Synthetic data is often of higher quality than real-world data, as it can be generated to automatically fill in missing values and apply labels, rather than being full of errors, inaccuracies, or bias.
- Machine learning often necessitates a deluge of data, which can be hard to procure at the required magnitude for both training and testing a predictive model.
Synthetic data does wonders in these cases, filling in the gaps by supplying additional real-world data to achieve a more sizable scale of inputs. - Synthetic data is often easier to generate and use than real-world data. Because real-world data often needs to have privacy restrictions, be filtered for errors, or converted to a uniform format, synthetic data provides a more accurate and reliable dataset.
1) Mostly AI Synthetic Data
Mostly AI is a big data analytics platform that uses AI to understand customer behavior. It lets users track and manage big data from different sources. It analyzes and provides insights into customer behavior and creates predictive models by using deep learning, machine learning, and artificial intelligence technologies. It lets users simulate data, develop models, and understand the variations in the data using deep neural networks.
While the startup has a strong foundation in technology, it is just as successful in terms of commercializing its technology and increasing the company’s value. AI is playing a major role in this unique and rapidly growing field, both in terms of deploying clients and in terms of knowledge.
MOSTLY AI sees the potential for using synthetic data in software testing. To maintain these use cases, it is necessary to create synthetic data that is not only known to data scientists, but also to software developers and performance testers.
2) Gretal AI Synthetic Data
Gretel’s platform enables developers to experiment with data and share it with other teams, divisions, and organizations. Customers can use a combination of tools and APIs to generate synthetic stand-ins for production data.
Gretel is a company that provides data access to developers without compromising accuracy or privacy. The company’s APIs make it easy to generate synthetic data that is anonymized and safe. This allows developers to preserve privacy and innovate faster.
Gretel’s data usually works well: AI models trained on it are usually only a few percentage points less accurate than models trained on real-world data and are sometimes more accurate.
3) Datomize
Datomize’s specialized models for more complex data types, models that establish conditions for maintaining relationships, and regularly scheduled training of its generative model result in data of excellent quality. Datomize provides extensive validation of numerous quality measures to guarantee accuracy.
Datomize is designed to handle large and complex data sets, and can generate high-quality data at any scale. Datomize can manage tables with hundreds of fields – including time-series and free text fields – and millions of records at the same time.
They provide automated synthetic data generation, optional data and dependency mapping, centralized data source definitions, single sign-on, and comprehensive APIs to integrate Datomize into your enterprise’s IT computer infrastructure.
3) Synthetaic
Synthetaic are a company that focus on detection AI from annotated images in under 5 minutes. Their platform RAIC is a data analytics platform that can analyze large, unstructured datasets, from photographs to satellite and aerial imagery. Other tools only work for specific types of objects, while RAIC can detect objects you didn’t even know were there.
Just some of the industries they focus on include Transportation Monitoring, Environmental Intelligence and Rare Object Detection.
4) Hazy
Hazy’s platform uses artificial intelligence to share data securely and automatically, while also anonymizing personal information so that it can’t be used to identify individuals. This allows data-centric businesses to share valuable data while protecting people’s privacy at the same time.
Hazy is a UCL AI spin out that is backed by Microsoft and Nationwide. Their main product is synthetic data, which is data that is artificially generated using machine learning techniques and offers a number of features. This synthetic data retains the statistical properties of real data, but can be used safely for analytics and innovation without compromising customer privacy or confidential information. Hazy achieves this by capturing patterns in raw data to generate completely synthetic hazy data that maintains the statistical value of the original data. This means businesses can unlock their data for innovation with safer and faster data provisioning on demand, and without any compliance, security, or privacy risks and procurement delays.
What applications use Synthetic Data?
Business functions that can benefit from synthetic data include: Automotive, Robotics, Financial services, Healthcare, Manufacturing, Security and Social Media.
Why is Synthetic Data important now?
Because it can be generated to meet specific needs or conditions that are not available in existing (real) data.
When data privacy requirements limit the availability or use of data, synthetic data can be used to test products before release. Synthetic data is data that is generated by computer programs rather than being collected from real-world sources. This type of data has been used since the 1990s, but its use has become more widespread in recent years due to advances in computing power and storage capacity.
What are some challenges associated with synthetic data?
There are a number of challenges associated with synthetic data. First, it can be difficult to generate data that is truly representative of the real-world data that it is meant to mimic. This can lead to issues when using the synthetic data for training or testing machine learning models, as the models may not be able to generalize well to real-world data if the synthetic data is not an accurate representation of it. Another challenge is that synthetic data can be difficult to label.