Data tooling for the next trillion-dollar companies

May 28, 2021
Reports [https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/catch-them-if-you-can-how-leaders-in-data-and-analytics-have-pulled-ahead] as well as market caps [https://www.axios.com/big-techs-power-in-4-numbers-de8a5bc3-65b6-4064-a7cb-3466c68b2ea0.html] show that being a data driven company is not only paying off, but also that it is slowly and steadily becoming the only option to navigate the ever-increasing data producing  economy [https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning#part3%E2%80%8B] we live in. Of course, companies understand this, so they rush to update their business models to become “data-driven” or “data-first”. However, they quickly see that “unlocking their data” is a very big task with many difficult, and sometimes cross-company, challenges. In this article we focus on a specific subset of these difficulties, namely: Data access and data sharing. Arguably, the most important benefit of being a data-first organization is that data is freely flowing in and out of the company. All departments nowadays have the data, the tools, and the skillset to combine and analyze seemingly disparate datasets to gain insights they could not get before. And the truth is that in the past few years the market has been flooded with analysis tools and a (re)trained workforce, ready to use these tools. But there is an issue: these tools and the accompanying skillsets are modeled most of the time on the needs of sophisticated “data champions” - companies that are already completely data-driven and push the boundaries on what can be done. The reality, of course, is that the vast majority of companies do not approach the analytic sophistication of Amazon nor Google. Their data sharing and utilization needs are vastly different but just as important. At decentriq, we routinely encounter companies that get discouraged by this divergence and declare data a “lost cause”. This could not be further from the truth. They need approachable tools that solve their problems and not Google’s problems. We have seen many companies “silo” and air-gap their most sensitive and valuable datasets because the risk of leakage would be just too high. Other companies have so many complex data access policies in place that they end up having a system that is worse than before. > We have the rule of thumb that “The more valuable the data, the harder it is to unlock”. The fault is not with the companies. The cost of “doing data wrong” can be immense. Looking at the cost of data breach [http://auditanalytics-trends-in-cybersecurity-breach-disclosures.pagedemo.co/], it becomes clear why companies are reluctant to open up their data silos. Instead, they resort to cumbersome or impractical solutions when it comes to data utilization. Here at decentriq, we solve this dilemma of data value creation vs. data security by introducing Confidential Query (CoQuery). CoQuery enables every organization to securely and confidentially share, combine, and analyze data without worrying about complex access policies, trust, or data leakage. > CoQuery: An SQL framework built from the ground-up with security and confidentiality in mind By basing CoQuery on Confidential Computing, our clients are taking advantage of cloud scaling without ever disclosing unencrypted data to the cloud provider ( to learn more about that, see our previous blogs [/]), making deployment as simple as using an app. In addition, we strive for zero disruption for our users’ workflows by utilizing the latest advancements in privacy-enhancing technologies to ensure that queries can work out-of-the-box or with minimal changes. What are the implications? Quite simply, it means that our clients now have the tools they need to become truly data driven. They are able to collaborate with external parties that they simply could not collaborate with before, and combine datasets that were completely air-gapped without fearing leakage. They even use CoQuery to simplify their internal sharing policies by limiting the access to the raw data to the bare minimum or to minimize the data exposure that they might have. From what we have seen, they are only just getting started. If you want to learn more about CoQuery get in touch with us at hello@decentriq.ch and follow our social media [https://www.linkedin.com/company/decentriq/] and GitHub [https://github.com/decentriq] pages where we will soon drop more demos.