Data is increasingly treated as a product, even and especially within the walls of organizations. Data should be proactively served in a cross-departmental fashion, flowing freely between different lines of business and even subsidiaries located in different countries or continents.
The much-coveted concept of the data mesh remains hard to attain for highly regulated industries without the necessary privacy-enhancing technologies. Privacy-enhancing technologies, like synthetic data, are revolutionizing data anonymization and data-sharing processes and making true data democratization an everyday reality.
In practical terms, the use of synthetic data significantly simplifies the implementation of data democratization within an organization, especially in sectors subject to stringent regulatory guidelines, such as healthcare, banking, and government.
While traditional data-sharing methods often require lengthy approval processes and complex legal frameworks to ensure privacy and compliance, synthetic data can bypass these hurdles. This is because synthetic data retains the useful characteristics of the original dataset for analysis, learning, or decision-making, but doesn't carry the personal or sensitive information that would trigger privacy concerns.
Therefore, synthetic data can be shared more freely across various departments, business units, or even between different companies in a conglomerate, without necessitating exhaustive privacy impact assessments or risking regulatory fines.
This not only speeds up decision-making but also fosters a more collaborative and innovative work environment. With synthetic data, the aspirational concept of a data mesh—a decentralized, domain-oriented ownership model for data architecture—becomes not just achievable but operationally efficient, even in the most regulated industries.