Synthetic data is algorithmically generated to mimic real data. This can be used for testing and building software applications without compromising real-world data.
YData is an example of a company that provides synthetic
data to enable businesses to build and test machine learning models without
sacrificing sensitive data. It is easy to use and provides a generous free tier
for businesses to experiment with it.
Real-time data
Real-time synthetic data
is a useful tool for testing and validating AI models. It also helps to reduce
the risk of exposing sensitive information or creating biases during predictive
modeling. It is especially helpful for companies that rely on financial data
and are required to adhere to strict privacy policies.
Several companies offer structured synthetic data. BizDatax,
for instance, offers a test data automation solution with a synergistic model
that identifies missing values and sensitive information. It is also compatible
with Google Colab, which makes it easy for data engineers to collaborate and
work on deep learning libraries.
Another provider of structured synthetic data is Hazy, a
tool for creating test data that is accurate and free from bias. The company
understands that sharing sensitive financial data across lines of business is
difficult, and its API enables banks to monetize customer insights without
revealing identities. Hazy’s platform also complies with GDPR and other global
privacy regulations.
Tabular data
In tabular data, rows and columns are arranged side-by-side
in a table format. This data often comes in the form of text files like txt and
csv. You can open these types of files in spreadsheet programs like Excel and
Google Sheets. This type of data is often used for scientific research and can
be downloaded from various websites.
Synthetic data generation technology makes it possible for
companies to generate new data sets that resemble real-world data without
compromising privacy laws. This allows businesses to test and develop software
applications and machine learning models in a shorter timeframe. It also
reduces the risk of data leakage and enables businesses to comply with strict
regulations such as those in health care and financial services.
Generating synthetic data is a challenge because it must
preserve referential integrity and be accurate in a controlled way. One example
of a solution is K2View, which uses business entity blueprints to maintain data
model integrity and ensure that the results produced by an application match
the original source. This ensures that the data is accurate and does not
contain any sensitive information.
Time series data
Time series data is generated by sensors that monitor
pressure, levels, temperature, and other variables. These data sets are
important for applications that require continuous data updates. They are also
essential for companies that collect text-based information, such as e-commerce
and customer support.
A synthetic data generation platform can generate time
series data without exposing personal information. This allows companies to
test their models without compromising real customer data. This technology has
several applications, including enhancing the performance of machine learning
algorithms and improving security.
One synthetic data generator, Facteus, produces fake credit
card and debit transaction data to help hedge funds analyze consumer spending
trends. The platform complies with GDPR standards and uses differential privacy
to protect data by adding mathematical noise to sensitive fields. It is easy to
use and costs less than traditional methods. It can also be integrated into
existing ML pipelines. Users can choose from four ways to use synthetic data:
generative AI, which mimics real-world patterns and distributions; rules-based
generation, which is ideal for generating negative testing scenarios; data
cloning, which creates copies of real-time data for software testing; and data
masking, which secures PII.
Image and video data
Unlike time series data, image and video data requires a lot
of processing power. Thankfully, there are several solutions to this problem.
One of them is to use GAN-based architectures. These can generate synthetic
data or adapt real data to fit a particular domain. The technology is getting
traction in areas like medical imaging and finance.
For example, American Express is using the technology to
detect fraud in credit card transactions. Another solution is to use 3D
simulations. Romanian startup Caper, for instance, uses them to create a
dataset of a thousand images of the same product. The dataset can help retail
outlets solve problems like misplaced products and out-of-stock situations.
Data offers a synthetic data
generation platform that allows users to generate data sets for
training AI models without the risk of losing customer privacy. Its patented
process is based on the theory of probability distributions. It also complies
with GDPR standards and prevents sensitive data from being exposed to third
parties. Its technology also enables organizations to automate the process of
accessing, profiling, and generating data sets while ensuring security and
compliance.
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