How companies in 2021 can effectively drive machine learning by harnessing their data ecosystems.

May 30, 2021
The proliferation of IoT devices and the shift of many activities to digital has created an over abundance of data, or data ecosystems. These data ecosystems provide a deep level of data that allows companies to enhance the sophistication of their data driven decision making and machine learning models - in theory.  However, the question then becomes – how do you balance data privacy with machine learning? It is estimated that almost $300B worth of data remains untapped every year due to the lack of a secure environment to process this information, which makes many data models and machine learning algorithms incomplete. “Despite years of investment, enterprise deployment of AI and machine learning often do not go as planned because of data as a limiting factor,” says Jeffrey Bohn, Chief Research and Innovation Officer at global re-insurance firm Swiss Re [https://www.swissre.com/]. “Many teams may celebrate the successful technology implementation of these models, but when we look at the business impact in ROI, we find few examples of success.” In the financial services industry particularly, there is a lot of data fragmentation in the legacy architectures that makes it hard to extract the very data itself. Global insurance firms including Swiss Re have been conducting research and development into privacy preserving analytics solutions to harness these data ecosystems because it is critical to their evolving data centric business strategy. “There are a lot of interesting applications in using sensitive data related to healthcare and cyber risk losses, and privacy enhancing technologies can change what we can deploy using this data,” says Bohn. Going into 2021, privacy enhancing computation technology will be very relevant across many industries. Gartner [https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/four-ways-to-accelerate-the-creation-of-data-ecosystems] recently released their Top 10 Technology Trends for 2021 [https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/four-ways-to-accelerate-the-creation-of-data-ecosystems] –  privacy enhancing computation is ranked #3. “This trend enables organizations to collaborate on research securely across regions and with competitors without sacrificing confidentiality. This approach is designed specifically for the increasing need to share data while maintaining privacy or security," writes Gartner. McKinsey [https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/four-ways-to-accelerate-the-creation-of-data-ecosystems] has also expressed the value of harnessing data ecosystems. “Data ecosystems also offer tremendous potential value for businesses, whether several come together to share research with one another or a single company creates a data ecosystem that enables customers and other stakeholders to share and access data.” McKinsey highlights that harnessing data ecosystems provides value to companies in 3 main ways: growth, productivity, and risk reduction. Tech giants cloud providers Amazon, Google, and Microsoft have all recently announced their increased investment into privacy enhancing computation in the form of an emerging technology called confidential computing. Microsoft Azure was the first major cloud provider to deliver confidential computing, while Google Cloud announced at Cloud Next’20 their confidential computing with confidential virtual machines program as their newest cloud security program. “ We believe the future of cloud computing will increasingly shift to private, encrypted services that give users confidence that they are always in control over the confidentiality of their data,” said Google Cloud. We at Decentriq are dedicated to building privacy enhancing computation technology with confidential computing, and partner with pioneers in this space including Microsoft Azure and Intel. Based in Zürich, decentriq provides a platform for organizations to securely share and collaborate on sensitive data with anyone – including internal and external stakeholders. “All you have to do is open up a secure instance in your cloud platform, invite stakeholders, and define the parameters of data access,” says Maximilian Groth, CEO & Co-Founder at decentriq. “You can get started in just minutes.” Decentriq is collaborating with clients in the financial services industry, including Swiss Re and Credit Suisse, and has just raised a $3.8 million funding round in Oct 2020 from tech security investors btov Partners and Paladin Capital Group. “Decentriq can prove mathematically that nobody else in the chain (including decentriq itself and the respective cloud provider) got access to these confidential assets,” says Andreas Goeldi, decentriq’s investor at btov Partners.