The Goal: Your business teams need rich diverse datasets for analytics and machine learning, so do your partners, vendors, customers and clients. You teams can improve your operations by analyzing external data in combination with yours. The reverse is also true. Telecom giants, retailers, automotive companies, app makers, financial services institutions, healthcare providers, and utility companies all produce and consume data from outside their organizational firewalls.
The Requirements: Monetizing data requires sharing it with external parties. Your data is most valuable to other parties when combined and meshed with theirs for analytics and machine learning. For example, banks seeking to target customers need data gathered from mobile operators, grocery stores, and utilities to determine if a borrower will repay a loan on time.
The Challenge: Data prized by other organizations is also the most sensitive because its derived from important value-producing activity. Very limited value can be derived from selling aggregates or summaries of data sets, when compared to analyzing raw data meshed together from multiple owners. Each owner will allow it to be combined and commingled with yours so long as they 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.