Cloud Communications Analytics With Dynamic Event Mapping
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Solution Overview
Problem
Existing cloud communications platforms struggle to seamlessly integrate and correlate disparate telemetry data from various communication channels, requiring manual schema updates for new data sources and lacking flexibility in tracking additional metrics.
Innovation Solution
A system that uses session and event identifiers to correlate events across network elements, dynamically mapping event-specific fields to generic fields in a data warehouse, allowing easy integration of new channels and metrics without manual schema updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual schema updates are used to integrate new telemetry data sources, then data integration accuracy is improved, but system complexity and administrative effort increase
Solution Approach 1:
The system automatically detects new telemetry data sources and performs schema updates without requiring manual administrative intervention. The analytics platform self-configures to integrate new communication channels and data metrics by automatically generating appropriate data models and mapping relationships.
Solution Approach 2:
The data schema is made dynamic and adaptable rather than static. The system can automatically adjust its data model structure to accommodate new telemetry sources, communication channels, and metrics without requiring manual schema redesign, enabling flexible integration of evolving data requirements.
2Reliability
If fixed schema structures are used for data warehousing, then data storage reliability is improved, but adaptability to new metrics and channels decreases
Solution Approach 1:
The data schema is made dynamic and adaptable rather than static. The system can automatically adjust its data model structure to accommodate new telemetry sources, communication channels, and metrics without requiring manual schema redesign, enabling flexible integration of evolving data requirements.
Solution Approach 2:
The system employs a universal data model framework that can handle multiple types of telemetry data from various communication channels through a common schema structure. This universal approach allows the same system to reliably store and process diverse data types including voice, messaging, and emerging communication modalities.
3Loss of information
If comprehensive telemetry data collection is implemented across all communication channels, then analytics completeness is improved, but data processing complexity increases
Solution Approach 1:
The system segments the complex task of multi-channel telemetry data collection into modular components, with each communication channel processed through standardized data ingestion pipelines. This segmentation allows comprehensive data collection while managing complexity through organized, reusable processing modules for different channel types.
Solution Approach 2:
The system introduces intermediary data normalization layers that standardize telemetry data from various communication channels before storage and analysis. These intermediaries translate diverse channel-specific data formats into a unified schema, reducing processing complexity while maintaining complete analytics coverage across all channels.
Data Source
AI summary
The disclosed technology provides a system and method for correlating events from a single application run in a cloud communications network using session identifiers uniquely identifying a communication session, and event identifiers uniquely identifying events in network elements of the cloud communication network. A data manager of the cloud communications network maps common fields and event-specific fields of network element event records to common fields and generic fields of a data warehouse based on the event type of the event to be recorded.