Rainbird makes complex privacy risk assessment a seamless experience

Two years ago, HFS Research designated Rainbird as a OneOffice Hot Vendor for its comprehensive alignment with the HFS Triple-A Trifecta, using automation, analytics, and AI in combination to deliver more value. During the due diligence process, many customers reported that Rainbird’s capabilities and usability were a notable improvement from previous decision engines they had been using, making Rainbird an attractive choice for enterprise leaders looking to automate complex decision making at scale.

Rainbird recently helped EY develop a client-facing SaaS platform, EY Data Permissions Navigator (EY DPN). EY licenses the platform to clients to manage their privacy risks, specifically the privacy risk assessment and decision-making process. But with use cases, the proof is in the pudding. HFS met EY’s Global SaaS Law Leader to get honest and unfiltered feedback about Rainbird.

Rainbird has been a crucial add-on to the EY Data Permissions Navigator platform, injecting decision intelligence directly into the privacy risk assessment process

EY DPN helps EY’s clients automate gathering and analyzing multiple data points across risk control architecture and puts the contextualized data into risk assessments to get instant information about the risk profile of data use cases they are considering.

EY DPN delivers automated risk assessments to any first-line business user to tell them what they can or can’t do with data. It combines research from laws and regulations in more than 60 countries and maps them to “cognitive hubs,” which are cognitive capabilities mirroring how data privacy specialists look and think about a problem. Here is where Rainbird comes into the picture.

Mapping top-to-bottom flows with a single decision tree is so 1990s—automating complex privacy risk assessments requires complex decision engines

EY’s SaaS Center of Excellence started working with Rainbird to take advantage of its advanced non-linear decision engine, with data privacy being the first domain area. Rainbird provides the decision engine component of the EY Data Permissions Navigator platform. Selecting a linear decision engine was off the table because of the complexity of automating privacy risk assessments. EY selected Rainbird for its distinctive ability to simulate the application of human-like reasoning over the privacy-risk-assessment cycle to reach efficient, high-quality outcomes at speed.

Rainbird’s platform has been instrumental in capturing expertise from data privacy specialists and EY’s internal know-how and best practices; more importantly, it has helped EY replicate its expertise at scale. Rainbird codified and encapsulated why and how data-privacy subject matter experts make decisions, allowing EY to assess more nuanced data privacy use cases.

Usability makes Rainbird a step forward in decision-making automation, allowing fast adoption and remarkable results

Rainbird did most of the initial development work and handed it over to the EY Client Technology Operations team to maintain the decision model logic as part of its usual business activities. There were two recurring topics in our conversations with EY: Rainbird’s easy-to-use experiences and the responsiveness of its engineering and support teams to requests.

By leveraging the Rainbird engine and implementation team, EY cut roughly 12 months off the build time. The most notable outcome from the client side is that some decision-making processes that had typically required several months to execute can now be completed in around 15 minutes using EY DPN.

The Bottom Line: Complex processes need scalable decision engines that can easily cut through complexity, and Rainbird delivers on its promise.

Not all decision-automation engines are born equal. Scalability and usability make Rainbird a powerful platform for enterprises looking to automate decision-making processes. The demanding complexity of EY’s use case suggests Rainbird is making good on its promise to handle complexity at scale. Glowing client use cases like this one also outline Rainbird’s investment in its partnerships.