The Goal: To know how to market effectively, you need to gather lots of information about our prospective customers and how they might react to our efforts. Increased personalization is proven to boost engagement and ROI, and that includes when and how a message is delivered, as well as the message itself.
The Requirements: Gathering raw data for analytics and machine learning from multiple sources within your organization and from partners outside your organization. Rich diverse raw datasets are needed in combination to get a 360 view of how prospects may benefit from your services and what messages will be most effective to acquire them as customers.
The Challenge: Customer 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, personally identifiable information (PII) is required to join disparate data sets to accurately identify specific customers and their behavior. 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.