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Need test data? Synthetic data is better than production data
Say goodbye to low-value work and focus on high-performing testing and driving efficient software development
How does MOSTLY AI help CTOs, team leads, QAs, and developers improve software development and testing?
Realistic AI-generated test data at scale
Sensitive data anonymization
Safe data-sharing
Efficient application building
Customers who already use synthetic data
MOSTLY AI's synthetic data generator is trusted by major industry players across different sectors
Why is production and mock data not sufficient for testing anymore?
Beware of legacy data anonymization and generation techniques as they destroy the utility of data, kill your team’s efficiency, and endanger data privacy. Be smart and generate smarter synthetic data for speed, velocity, flexibility and cost-reduction.
Production data
Should not be used without proper anonymization
It‘s almost impossible to properly anonymize
Lacks outliers needed for negative testing
Too big and cumbersome to manage
Too small for stress tests
Rule-based mock data
70% of all test data is still created manually
Impossible to recreate all relevant business rules
Needs expert domain knowledge regarding the data
Not easy to maintain referential integrity
No correlations, no meaningful data
Why smarter synthetic data for testing?
0x
faster to generate
up to
0%
decrease in testers' time utilization
0%
compliant with the strictest legislation
The smarter way
to test software
Not only can you get test data faster than the competition, you get a whole load of other benefits.
Sign up for freeAutomate the process of generating high quality test data – the MOSTLY AI platform does the heavy lifting for you
No need to manually configure business rules anymore – you do not even need to know the details of the data you are working with
Do not worry about protecting privacy – in-built privacy guarantees will result in 100% anonymous test data
Cover more test cases with rich synthetic data – better test coverage means fewer bugs and higher reliability for your software
Create as much data as you need – perform load and performance tests at ease
How does MOSTLY AI’s synthetic data platform work?
1
Connect your source database
2
Define the tables where you want to protect individuals’ privacy
3
Start the synthetization - the platform automatically learns your data structures and business rules
4
Save the synthetic data to your target database
What databases and cloud buckets does MOSTLY AI support?
Direct database access
Direct cloud bucket access
Supporting various data types
- Numerical, categorical
- Datetime
- Geolocation
- Character Sequences
- Text
Secure deployment options
Either deploy in our cloud infrastructure or your environment (on-prem or in your private cloud)
User management
And much more
- Easy to use, self-service UI
- REST API
- Mock data generation
- Data Catalog for automation
Why testers and developers use MOSTLY AI’s synthetic data?
Sign up for freeMOSTLY AI’ platform helps you to:
Get AI-generated data at scale representative of the whole production data in just minutes
Deidentify data
Subset your entire data into smaller, more managable, yet representative batches
Speed up sprints and agility
Create bigger and more robust data from smaller sets for performance and load testing
Automate data generation using the Data Catalog function
Reduce costs of data generation
Comply with the stringest legislation. Our data is GDRP and CCPA-ready.
Learn more about synthetic data for testing
With smarter synthetic data your team can drive better software development and testing. Learn more in our free Ebooks.
Request a demo - no pitch slapping, no buzz words. All your questions answered.
Meet our team. Always happy to listen, consult and answer your questions. Get all your synthetic data questions answered.
Certifications
Compliant with
Questions & answers
Production data needs proper anonymization and proper anonymization of production data is near-impossible. Behavioral data especially.
Rule-based mock data is hard to create and mock data requires expert knowledge of the data. Mock data has no correlations and referential integrity is hard to maintain.
With MOSTLY AI’s platform you can automate data anonymzation without any expertise in the data that you want to synthesize. You get high-quality test data with preserved referential integrity in minutes.
Rule-based mock data is hard to create and mock data requires expert knowledge of the data. Mock data has no correlations and referential integrity is hard to maintain.
With MOSTLY AI’s platform you can automate data anonymzation without any expertise in the data that you want to synthesize. You get high-quality test data with preserved referential integrity in minutes.
Generative AI mimics data so well that you can end up with a 1:1-like connection to your original data. The important underlying concept of synthetic data is that there are no 1:1 relationships between the original and the synthetic data. The real data is only used as learning material during the synthesization process. Only generalizable patterns, distributions or correlations are learned. MOSTLY AI’s platform generates synthetic data from scratch based on these patterns. There is no 1-to-1 link between original and synthetic data. Because of this missing 1-to-1 link, there is no direct attack surface for re-identifying sensitive information.
However, it is essential to point out that not all synthetic data is created equal. There are open source solutions out there without additional privacy mechanisms in place that can leak privacy. The process of synthesization does not guarantee privacy in itself. One of the possible issues is outliers or extreme values that can easily be re-identified.
MOSTLY AI’s platform uses different mechanisms to safeguard against privacy and re-identification risks. The first mechanism makes sure that our deep learning algorithm will not overfit the original data. The second mechanism is built-in privacy protections on all levels. We automatically disable all categories used by a few sets of individuals and protect extreme values in other data types as there could be a privacy risk. The third mechanism is the quality assurance report after generation. We evaluate the model and each batch of generated synthetic data with strict privacy metrics to detect any and all privacy risks.
However, it is essential to point out that not all synthetic data is created equal. There are open source solutions out there without additional privacy mechanisms in place that can leak privacy. The process of synthesization does not guarantee privacy in itself. One of the possible issues is outliers or extreme values that can easily be re-identified.
MOSTLY AI’s platform uses different mechanisms to safeguard against privacy and re-identification risks. The first mechanism makes sure that our deep learning algorithm will not overfit the original data. The second mechanism is built-in privacy protections on all levels. We automatically disable all categories used by a few sets of individuals and protect extreme values in other data types as there could be a privacy risk. The third mechanism is the quality assurance report after generation. We evaluate the model and each batch of generated synthetic data with strict privacy metrics to detect any and all privacy risks.
The difference is in the basic approach. MOSTLY AI generates an entirely new data set that leaves no room for re-identification. The problem with data masking is that there is still a risk of re-identification. There is no good tradeoff between data masking and data utility. The approach we use is more secure, and it also opens the door to what we call programmable synthetic data. You can instruct the generative AI to generate data as you want.
There is no golden rule for that. It depends on the patterns, hidden business rules, and what you try to replicate. If you want to do text synthesization, generative AI will need more samples than categorical data. You can start with as little as 5000 rows, but you can leverage as much data as you want to upload. There is no limit. You can also get synthetic data from just a sample from your production data.
From a quality perspective, synthetic data looks real. MOSTLY AI’s platform provides a detailed QA report from a quality assurance perspective. We check the adherence of the synthetic data to the original data in terms of data distribution.
And the quality is not limited to the distribution adherence of single columns - what we call the univariate distribution. Our generative AI model can ensure a high quality of any column combination. Bi-variate, tri-variate, and so on.
And the quality is not limited to the distribution adherence of single columns - what we call the univariate distribution. Our generative AI model can ensure a high quality of any column combination. Bi-variate, tri-variate, and so on.
Have more questions?