In the era of data-driven decision-making, organisations face a constant challenge of balancing the need for valuable insights with the responsibility of protecting privacy and complying with regulations. This is where synthetic data emerges as a game-changer. In this blog post, we will explore what synthetic data is and some of its application use cases for customer data professionals.
What is Synthetic Data?
Synthetic data refers to artificially generated data that mimics the statistical properties and patterns of real-world data, without containing any personally identifiable information (PII) or sensitive details. Synthetic data, in the context of analytics, is an artificially created dataset that closely resembles real-world data while maintaining privacy. It is generated using algorithms, models, and statistical methods to replicate the attributes, distributions, and relationships found in actual data. By leveraging synthetic data, organisations can perform a wide range of analytics tasks without the risks associated with using real personal information.
Application use cases of synthetic data for customer data professionals
A non-exhaustive list of how synthetic data can be used by customer data and analytics professionals. These reflect some use cases applied at Human37.
Testing – Synthetic data proves invaluable in testing scenarios where using real data may be impractical, restricted, or risky. Software developers and testers can use synthetic data to simulate large datasets with diverse characteristics and edge cases. This allows for thorough testing of applications or algorithms, ensuring their performance, scalability, and functionality without accessing or manipulating sensitive or confidential information. Think of building out a customer data infrastructure in which downstream data pipelines need to be validated. It’s not always straightforward to anticipate how a destination will react to a payload it receives. For example – sending data from Segment to an advertising platform. In order to test safely synthetic data can be used. This ensures we can verify if rules are applied correctly, data transformed and delivered with zero risk.
Training – How do you train customer data professionals in environments where PII is key without using actual production data that contains PII? Synthetic data is the answer. Synthetic data can for instance be used for training professionals in the usage of Customer Data Platforms (CDPs), product analytics, Email Service Providers (ESPs), Data Warehouses, etc.
Demos – Similar to our previous points. Mimicking proof-of-concepts or demos without PII, in cases where PII is key, is hard. Synthetic data allows us to inject these data points into an environment in order to provide an idea of what things would look like.
Algorithm & data model development (including AI) – Synthetic data plays a crucial role in analytics, enabling organisations to perform computations, data modelling, or algorithm development without directly accessing or storing real data. Researchers and data scientists can leverage synthetic data to explore and experiment with data-driven techniques, such as machine learning algorithms or statistical analysis. Synthetic data can also augment existing datasets, increase sample sizes, or balance imbalanced datasets, improving the quality and robustness of analytical models.
Synthetic data as part of our organisation’s training ground
Synthetic data is revolutionising the way organisations approach testing, training and data modelling (among others). It provides a means to achieve goals that require PII loaded data sets by generating datasets that mimick this without compromising privacy. By leveraging synthetic data, organisations can strike a balance between data utility and privacy concerns, promoting innovation, knowledge development, and compliance in the data-driven landscape. As technology advances, the potential for synthetic data to shape the future of analytics and privacy is vast, unlocking new possibilities for organisations across various industries. At Human37, being customer data professionals, we embrace synthetic data in order to ensure we can develop our people, expertise as well as our solutions while ensuring maximum security standards.