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Why choose MOSTLY AI’s synthetic data platform for AI training and test data generation?

One tool, two amazing use cases. Learn why you should upgrade your AI training and test data to smart data!
Learn more from the Synthetic Data Guide for Enterprises!

Smart training data

According to Gartner, 65% of companies can't explain how specific AI model decisions or predictions are made. Only 20% monitor their models in production for bias and ethics. This blindness is costly. AI TRiSM tools, such as MOSTLY AI's synthetic data platform provide:
Trust
Risk
Security
Management
Explainability
Improved
Anomaly Detection
Bias
Mitigation
ModelOps
Synthetic data is a game-changer in AI development. It’s a versatile tool that is the missing piece of the puzzle for all companies looking to leverage and scale AI. McKinsey’s research found that 49% of AI high performers generate synthetic data to train AI models.

What makes smart training data smart?

Improve AI performance

By 2022, Gartner estimates 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 well enough. 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.

Remove bias with synthetic data

The data synthesization process allows for the introduction of fairness constraints. MOSTLY AI's synthetic data generating algorithms performs exceptionally well in bias mitigation. The algorithm generated a fair version of the US Census dataset by narrowing the gender pay gap to 2%. By introducing the same parity correction, the likelihood of Black recidivism was lowered to 1% from 24% in the Compas recidivism dataset. Fair synthetic data is a mission-critical part of ethical AI.

Use synthetic data for explainable AI (XAI) and model validation

Synthetic data is an important AI governance tool. It provides explainability and model validation through shareable copies of input data. Use representative synthetic data for model documentation and augmented synthetic data to stress test AI models.

Forget the accuracy and privacy trade-off once and for all

MOSTLY AI’s synthetic data platform generates highly accurate synthetic data without any privacy risk. Each generated synthetic data batch comes with a privacy and accuracy report, making it perfectly safe to use for AI training.

Smart test data

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.

Having test data that ticks off 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

Test engineers no longer need to manually configure the business rules or logic in a test data generator. Our AI-powered synthetic data engine learns all of the dataset's features and takes care of the business rules.

What makes smart test data smart?

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 data sets 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 integrity
A useful test dataset must contain the same relationships as the production dataset. It's accurate values should be based on business rules. MOSTLY AI provides referential integrity by conditionally generating the linked tables of your production dataset.

Conditional generation works like an 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. As long as the columns and data types are the same as in the original subject table, it will work automatically. Upload your handcrafted, updated, or synthesized versions of the subject table, and MOSTLY AI will generate a fitting, realistic linked table.
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.

Are you ready to learn more about MOSTLY AI’s synthetic data platform?

Check out the platform’s features!
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