The Goal: To improve their business continuity and resource availability, managers must proactively detect and respond to potential sources of threats, disruptions and delays. Predictive, real-time and prescriptive analytics are vital to any organization with these goals in mind. However, the statistical and Machine Learning technique used in these efforts require access to rich, diverse raw data sets from within and outside the organization.
The Requirements: Most organizations do not have a sufficient amount of internal activity to detect every failure mode, identify every cybersecurity threat, or track the movement of every goods and services across multiple delivery channels in real time. Gathering raw data for analytics and machine learning from multiple sources within your organization and from partners outside your organization is required.
The Challenge: Business activity data is 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 cannot be exposed directly to analysts but must be available for the analytics. For example, real time location information is required to join disparate data sets to accurately identify specific threats. Analytics require this detail but cannot expose 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 data 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.