Data democratization: fix broken data access with synthetic data

• Data access is increasingly limited within organizations. Data access privileges are getting hard to come by, and rightly so. According to Gartner: 

"59% of privacy incidents originate with an organization's own employees. Worse still — 45% of employee-driven privacy failures come from intentional behavior (though it may not be malicious)."

• Limiting attack surfaces has become a high priority for companies that suffer major financial and reputational setbacks when data leaks happen. Protecting perimeters is no longer enough. Reducing the amount of unsafe data within the walls of organizations is more important than ever. 
• Most data strategies are not only unsafe but also seriously inefficient, with data scientists spending 80% of their time finding, cleaning, and organizing data. 

Everyone is talking about the importance of data-driven decisions, but only a select few actually have the data to make those decisions. At the same time, privileged data scientists have full access to raw data, which is not only dangerous but comes with hidden restrictions. They can only do what they have done before and already have the specific legal permission to do.

Once data scientists or machine learning engineers venture into yet-undiscovered territories and ideas, they need to obtain new legal authorizations for performing new ideas on old datasets, which can take weeks or more, depending on the complexity. Better, faster and compliant ways of data access are already possible with the right tools, yet most companies lack the awareness on good alternatives. 

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.

At MOSTLY AI, we see more and more companies pivot to the proactive data approach. These trend-setters create internal - or in some cases, external data exchange platforms - to facilitate innovation and data-forward thinking across organizations and beyond.

Synthetic data sandboxes are populated with curated and maintained synthetic versions of business-critical datasets. Access to synthetic data assets can be broadly and quickly provided. Citizen data scientists, third-party vendors, or even regulators can freely use synthetic data sandboxes, accelerating innovation and compliance. Synthetic data technology is a data anonymization approach that preserves all of the intelligence locked up in data assets. It's 100% GDPR compliant, ready to unlock customer data for a wide variety of use cases: 

• advanced analytics, AI and machine learning model training, 
• explainable AI, fairness and ethical AI,
• test data for software development.