The Goal: C-Suite executives are inserting machine learning and AI driven innovations into their strategic plans to improve the autonomization and coordination of services – from customer engagement to product transportation to factory production.
The Requirements: The analytics underling automation need rich, accurate, diverse raw data sets. Simply put, most organizations do not have a sufficient amount of internal data to achieve these objectives and your teams must augment internal data with data sets generated outside your organization.
The Challenge: The data underlying automated activity is highly sensitive, and each data 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. Sensitive data, such as price, cost and source, cannot be exposed directly to analysts but must be available for the analytics. Analytics require this detail but cannot exposing such 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 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.
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.