Goodbye, Controlled Data Access.
Hello, Collaborative Data Access.

Collaborative Data Access represents the new way to unblock and securely share data across—and even beyond—your organization for optimal data productivity. 
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Collaborative Data Access represents the new way to unblock and securely

Has data become the biggest productivity killer?

Your approach to data is quickly becoming the biggest make or break factor to productivity in Engineering. Meanwhile, the business is now more dependent on your productivity and speed than ever before.
While nobody is letting go of control when it comes to data access, winning organizations see the bigger objective of driving impact through an elevated approach that takes advantage of a new set of capabilities and practices.
In response to the challenge of tightened data access combined with an ever-increasing data dependence, Collaborative Data Access is the approach that ensures data is useful, realistic, safe, and accessible by way of API.
With markets, customers, and products changing so rapidly and demanding more of Engineering, Collaborative Data Access empowers development and testing teams by reducing bottlenecks while protecting privacy.
Big players like eBay have already been able to generate realistic, safe data for development and testing across distributed teams and environments in the petabytes, all thanks to an approach like Collaborative Data Access.
Controlled Data Access as a primary focus no longer serves us.

Why winning companies have started switching

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

#1 Speed

Organizations are developing 100X faster than they were 10 years ago and with far greater output than ever. Amazon, Airbnb, Walmart and others are now deploying tens or hundreds of thousands of times each day.

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

#2 Transformation

Even since before the massive wave of generative AI, transformation has been a key business imperative. Buyer expectations shaped by the consumer world continue to be a significant driver, and the advent of LLMs has thrown transformation and unpredictability into overdrive.

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

#3 Privacy

Data privacy regulations are here to stay and will only expand, impacting companies’ ability to efficiently execute on the need for speed and transformation. But winning organizations are finding ways to strike the balance.

Product teams need Controlled Collaborative Data Access

Controlled Data Access

The traditional option for a company that handles sensitive data, heavily restricting access to it and minimizing its value.
Rudimentary masking and anonymization techniques make it less accurate for testing and analysis.
Data is an obstacle for progress. Sensitive data cannot be used in AI/ML models.
Data is controlled and not shared. A few super users hold the data hostage.

Collaborative Data Access

Sensitive data is protected while its utility is maximized for collaborative software and AI development.
Synthetic data mimics your real data so it’s an accurate de-identified representation for effective testing.
Data is a plentiful resource, enabling teams to generate powerful insights.
Data can be shared and used across departments and teams with no links to the original information.

Why Collaborative Data Access matters so much

50%
of development time is spent on testing
59%
Of privacy incidents originate with employees
$59b
Annual cost of software bugs to the US economy
1.5x
better revenue growth than control-focused counterparts

Collaborative Data Access introduces a new reality

Collaborative Data Access captures every detail of your production data in realistic, safe-to-use datasets kept in sync across environments and free from compliance constraints.

Development times accelerate and product quality improves, advancing outcomes throughout the software development lifecycle.

‍The result?

Companies that embrace Collaborative Data Access are winning the upfront sprint for optimal data productivity and the ultimate race for transformative business impact.

5 Operating Principles of Collaborative Data Access

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Data must be useful, 
maintaining the database’s underlying business logic, structure, and referential integrity

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Data must be rapid, provisioned and refreshed on demand, for both shared and isolated use cases

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Data must be fresh, kept up-to-date with production and in sync across lower environments

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Data must be secure, with privacy achieved by way of masking or synthesis to ensure compliance

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Data must be realistic, capturing the complexity of real-world data without requiring complex workflows to do so

Developer solutions built for Collaborative Data Access

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Tonic Structural

For de-identifying structured and NoSQL data

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Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Tonic Textual

For de-identifying free-text data

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Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Tonic Ephemeral

For ephemeral data environments

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