• Written by Azhar Aliyar, Director

When evaluating data solutions for a large organisation, a question often arises: what is synthetic data? Synthetic data refers to artificially generated data that mimics the characteristics of real data, often used to supplement or replace existing data sets. This concept is frequently discussed in meetings, but its application and limitations are not always clear. Understanding synthetic data is crucial, as it can be a valuable tool in certain contexts, but also has the potential to launder gaps in existing data, rather than addressing the underlying issues.

Definition and Clarification

Synthetic data is often confused with anonymised data, but these are distinct concepts. Anonymised data is real data that has been modified to protect sensitive information, whereas synthetic data is entirely fabricated. This distinction is important, as anonymised data still retains its original characteristics and relationships, whereas synthetic data is generated based on patterns and models.

The generation of synthetic data involves using algorithms and statistical models to create data that resembles real data, but is not actually derived from it. This process can be useful for creating data sets that are similar to real data, but without the associated privacy or security concerns.

Differences from Anonymised Data

One of the primary differences between synthetic data and anonymised data is the level of control over the data generation process. With anonymised data, the original data is still present, and the anonymisation process is designed to protect sensitive information. In contrast, synthetic data is generated from scratch, allowing for greater control over the characteristics and patterns of the data.

This difference is significant, as it affects the potential applications and limitations of each type of data. Anonymised data is often used for analysis and research, where the goal is to understand patterns and relationships in the real data. Synthetic data, on the other hand, is often used for testing and training purposes, where the goal is to create a realistic simulation of real-world data.

Genuine Applications in Large Organisations

Synthetic data can be a valuable tool in large organisations, particularly in situations where real data is scarce or difficult to obtain. For example, a shift supervisor at a manufacturing plant may use synthetic data to test and train machine learning models, without having to rely on limited or sensitive real data. Similarly, a category manager may use synthetic data to simulate different scenarios and predict outcomes, without having to rely on historical data.

In these contexts, synthetic data can be used to generate realistic simulations of real-world data, allowing organisations to test and train models, and make predictions about future outcomes. This can be particularly useful in situations where real data is limited or difficult to obtain, such as in the early stages of a new project or initiative.

For more information on how AsscherAi can help your organisation leverage synthetic data, visit our website to learn more about our data solutions.

Limitations and Misconceptions

Despite its potential benefits, synthetic data is not a panacea for all data-related issues. One common misconception is that synthetic data can replace real data entirely, but this is not the case. Synthetic data is generated based on patterns and models, and may not capture the complexity and nuance of real data.

Furthermore, synthetic data can also launder gaps in existing data, rather than addressing the underlying issues. For example, if an organisation has poor data quality or incomplete data sets, generating synthetic data may not address these underlying problems, and may even exacerbate them.

It is also important to note that synthetic data is not a substitute for proper data management and governance. Organisations should still prioritise collecting and managing high-quality real data, and use synthetic data as a supplement or tool for specific purposes.

When Synthetic Data is the Wrong Choice

Synthetic data is not always the best solution, and there are situations where it may be the wrong choice. For example, if an organisation is trying to understand complex patterns or relationships in real data, synthetic data may not be sufficient. In these cases, it is often better to use real data, or to use anonymised data that retains its original characteristics and relationships.

If you are unsure about whether synthetic data is the right choice for your organisation, or have questions about how to leverage synthetic data effectively, contact us to speak with one of our data experts.