The Goal: Corporate executives are acutely aware that risks and threats arise from within and outside your organization. The goal is to detect and validate patterns suggesting and measuring risks or threats so they can be proactively addressed before they can cripple your business.
The Requirements: In a perfect world, risk managers would combine and analyze data from myriad internal and external sources operational data, behavioral data, data from social media, connected devices, 2nd party apps, and other data partners. The accuracy of predictive analytics for risks and threats is directly related to the quality and variety of data your risk management team can access.
The Challenge: Data prized by demand risk managers is highly sensitive because its derived from important value-producing activity. Predictive analytics require vast amounts of diverse raw datasets, rather than aggregates or summaries. Gathering data from multiple owners for predictive analytics and machine learning requires that each owner can control how and by whom the data is used.
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 which allow users to view or change data from another owner. Concordance decentralizes security, governance and compliance controls to each data owner 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.
Concordance 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. Metering data usage for monetization is automated.