Create synthetic data based on pre-defined rules and constraints, aiming to mimic real-world data or simulate specific scenarios.
Rule-based generated synthetic data refers to the process of creating artificial or simulated synthetic data that follows predefined (business) rules and constraints. This approach involves defining specific guidelines, conditions, and relationships to generate synthetic data. Reasons why organizations use Rule Based Synthetic Data:
In cases where data is either limited or where you do not have data at all, the need for representative data becomes crucial when developing new functionalities. Rule-based synthetic data enables the generation of data from scratch, providing essential test data for testers and developers.
Rule based synthetic data could enrich data by generating extended rows and/or columns. It can be used to produce extra rows to create larger datasets easy and efficiently. Additionally, Rule based synthetic data can be used to extend data and generate additional new columns potentially dependent on existing columns.
The rule-based approach provides flexibility and customization to adapt to diverse data formats and structures, enabling the full tailoring of synthetic data according to specific needs. One can design rules to simulate various scenarios, making it a flexible method for generating data.
Rule-based synthetic data facilitates data cleansing by generating data adhering to predefined rules, correcting inconsistencies, filling missing values, and removing errors, ensuring the integrity and quality of the dataset is preserved. This allows users to have data with even higher quality.
Rule-based synthetic data generation is particularly useful in scenarios where real personal data cannot be used due to privacy concerns or legal restrictions. By creating synthetic data as alternative, organizations can test and develop without compromising sensitive information.
Our platform supports for Rule Based Synthetic Data generation via our Calculated Column function. Calculated Column functions can be used to perform a wide range of operations on data and other columns, from simple arithmetic to complex logical and statistical computations. Whether you are rounding numbers, extracting portions of dates, calculating averages, or transforming text, these functions provide the versatility to create exactly the data you need.
Here are some typical examples to generate Rule Based Synthetic Data with our Calculated Column functions: