The Goal: Data and analytics have been changing the basis of competition and companies are analytic capabilities to launch entirely new business models. While the volume of available data has grown exponentially in recent years, most companies are capturing only a fraction of the potential value in terms of revenue and profit gains. Access to diverse, granular data sets is driving competition – and disruption.
The Requirements: In industries where incumbents rely on a certain kind of standardized data from 3rd party services to make decisions, bringing in fresh types of data sets (“orthogonal data”) to supplement those already in use can change the basis of competition. Much of this data will come from outside your organization.
The Challenge: Forming data partnerships to share raw data has been overly complex., expensive and cumbersome. The most valuable data has sensitive data elements which are vital for marketing and business teams to combine and commingle with yours. Sensitive data cannot be exposed directly to analysts but must be available for the analytics. Analytics require this detail but cannot expose sensitive data in violation of the owner’s corporate policies and regulations.
The Problems: Conventional systems can be configured to allow users to expose or change data owned by another party. If data Owner A shares raw data with Owner B, using a conventional system, then Owner B can use the combined data sets in violation of Owner A’s rules. Legal agreements cannot be relied upon to prevent data misuse. Most data sharing projects get stuck as a result. Reliance on 3rd party services do not provide the capability for self-service analytics, and do not provide the data quality and transparency required to ensure the analytics are generating accurate results.
The Myngl Concordance Solution: Secure Analytic Containers work differently than conventional systems: providing decentralized controls to each data owner that unify data security, governance and compliance to each data owners separately and simultaneously. Users, admins, even Myngl staff, are blocked from direct source data access to change or override owners’ rules. This removes vulnerabilities and prevents data risks, misuse, and breaches.
Analysts can make use of sensitive data without exposing it. Secure Analytic Containers provide analysts with a secure analytic sandbox utilizing open-source Zeppelin Notebooks, SQL, Python, TensorFlow and other powerful tools. Analytic jobs are separately authorized by data owners to make use of restricted data. Automated scheduling is orchestrated via microservices and results are segregated from source data. This allows analytic apps to use data elements which are hidden from direct Subscriber view.