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Data democratization: fix broken data access with synthetic data

Data democratization is not just nice to have but a mission-critical part of organizational success. In short, limiting data access is bad for business. Not guarding data assets carefully can be a fatal mistake. Pro-actively served, curated synthetic data products hold the key to safe and meaningful data consumption across and even beyond the walls of organizations.

Data democratization and data access challenges

  • 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. 
  • However, most traditional data governance strategies are not only unsafe, but seriously inefficient, with data scientists spending 80% of their time finding, cleaning, and organizing data. 

The status quo in data sharing and data democratization

Everyone is talking about the importance of data-driven decisions, but in reality only a select few individuals in organizations actually have the data to make those decisions. Many times only privileged data scientists have full access to raw data. But even they have to play by the rules. They need to request specific permissions to work with certain datasets.

Once data scientists or machine learning engineers venture into yet-undiscovered territories and ideas, they need to obtain new permissions. Sometimes that is even the case for performing new analyses on datasets they already worked with in the past! Depending on the organization these processes to gain permission can take weeks or more.

Better, faster and compliant ways of data access are already possible today with the right approach, yet most companies lack the awareness of: synthetic data.

The data democratization solution 

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. And there is one privacy-enhancing technology, that stands out: synthetic data. It is 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.

See a concrete example of how synthetic data can be shared within an Databricks environment in the following video.

Data democratization best practices

More and more companies pivot to a proactive data approach. These innovators create internal - or in some cases, external data exchange platforms - to facilitate innovation and data-forward thinking across the organization 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 can freely use synthetic data sandboxes, accelerating innovation and compliance. Synthetic data technology is a data anonymization approach that preserves all of the intelligence of original data assets. However, it's fully anonymous data that helps to unlock customer data for a wide variety of further use cases, such as: 

data democratization with synthetic data
Case studies and guides

Ready to try synthetic data?

The best way to learn about synthetic data is to experiment with synthetic data generation. Try it for free or get in touch with our sales team for a demo.