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What are the benefits of choosing MOSTLY AI’s synthetic data platform

One smart data generation tool, two amazing use cases. Learn why you should upgrade your AI training and test data to smart synthetic data. 

Make your AI training dataSmarter

According to Gartner, 65% of companies can't explain how specific AI/ML model decisions are made. Only 20% monitor their models in production for bias and ethics. This blindness is costly. Synthetic data is a game-changer in AI and machine learning development. McKinsey’s research found that 49% of AI high performers generate synthetic data to train AI models. AI TRiSM tools, such as MOSTLY AI's synthetic data platform provide:
Trust
Risk 
mitigation
Security
management
Explainability
Improved
anomaly detection
Bias
mitigation
ModelOps

What makes smart training data smart?

Case study

Improve AI and machine learning performance

By 2022, Gartner estimates that over 25% of AI training data will be synthetic. Insurance providers, banks, telcos, and other fast-moving industries already prefer synthetic training data. Why? Synthetic data is better than real data when it comes to AI training. AI models need the right data to be able to learn patterns thoroughly. Synthetic data generation can upsample rare events, resulting in an up to 15% improvement in AI performance. Using upsampled synthetic data leads to better anomaly detection, such as fraud.
Read the fraud detection case study
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Remove bias with synthetic data

Data synthesization can introduce fairness constraints. MOSTLY AI's synthetic data generating algorithms perform exceptionally well in bias mitigation. Fair synthetic data is a mission-critical part of ethical AI.

More abour fairness
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Use synthetic data for explainable AI (XAI)

Synthetic data is an AI governance tool, providing explainability and model validation. Use representative synthetic data for model documentation and augmented synthetic data to stress test AI models.
More about XAI
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Use accurate and private behavioral data

MOSTLY AI’s synthetic data platform generates highly accurate synthetic behavioral data without privacy risk. Each generated synthetic data batch comes with a privacy and accuracy report, making it perfectly safe to use for AI training.
More about behavioral data
Read more about the AI/ML use case

Make your test data Smarter

The process of creating test data can be an arduous task.
It requires a considerable amount of manual work to obtain a dependable subset of your production data. Maintaining referential integrity, business rules, and representative business scenarios is a long journey full of privacy risks.

Test engineers no longer need to manually configure the business rules or logic in a test data generator. Our AI-powered synthetic test data generator learns all of the dataset's features and business rules.
Having test data that ticks these boxes helps test engineers to:
Identify which business scenarios will result in unexpected behavior from the product
Take appropriate action before the product is released to the public
Speed up test data generation by allowing AI to analyze production data and generate test data

Subsetting test data

Production datasets can be terabytes in size. But what you want for testing is the right data rather than all of it. MOSTLY AI can create a smaller-sized, representative subset of your data. The smaller subset covers all the business scenarios for testing. Instead of random sampling, MOSTLY AI's synthetic data platform synthesizes a flexibly sized subset. The synthetic data subset accurately represents all production data. All generated synthetic datasets come with a QA report.
This is analogous to the way that, for instance, sociologists conduct population studies. It wouldn't be feasible for them to interview the entire population of a country. Instead, they carefully select a sample with which they can say or conclude something about the complete population. Useful and realistic test data works in exactly the same way. Even though it's only a sample, its test cases are representative of the entirety of the production data.

Referential data integrity

Meaningful test data keeps referential integrity. Quality test data must contain the same relationships as production data. MOSTLY AI provides referential integrity through conditional data generation. Conditional generation works like auto-complete for your data. Once you have a training model of a subject table-linked table pair, you can ask this model to complete any subject table you throw at it. Upload your handcrafted, updated, or synthesized versions of the subject table to generate a fitting, realistic linked table.
Conditional test data generation works like a wonder. As long as the columns and data types are the same as in the original subject table, it will work automatically. Let AI do the heavy lifting for you and generate better, referentially intact test data faster and easier! 

Conditioned test data

Obtaining useful results from legacy test data generators is a laborious task. First, you'll need a deep understanding of the business logic and rules that underpin your production data. This knowledge is necessary to specify the conditions in which certain values occur. These can be simple rules — 80% of cars sold cost less than 20,000 euros. But they can also be nested — cars that cost less than 20,000 euros are often compact city cars. Or describe behavioral patterns — customers visit a car dealer six times on average before buying a car.

MOSTLY AI’s synthetic data platform saves you the effort of understanding and defining granular business rules. In fact, it automates the process of studying the data to formulate these rules. Our AI-powered synthetic data generator learns the patterns that are present in the production data, assigns probabilities to the conditions in which certain values occur, and synthesizes your test data accordingly.

The resulting test data breathes realism and makes your product come alive before it's launched to its users.

Want to learn more about how synthetic data can help you?

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