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The global telemetry market is expected to be 243 billion dollars by 2020 with a very high growth rate and covers a wide spectrum of the industry: Telecom, medical, consumer electronics, networking etc. Hence, there is a tremendous opportunity for data monetization, as more and more organizations are betting on data analytics to provide improved customer satisfaction, better performance and enhanced product support. Coupled with data monetization goals, industry trends like multi-vendor networks, software defined networks, cloud services, elastic needs of customers, multitude of IoT devices etc. have put immense pressure on OEMs, cloud service vendors and packaged software vendors to remain agile and adapt to changing scenarios.. These trends are creating extensive amount of data – called telemetry data or product intelligence data, waiting to be mined and exploited.. All of these requires a robust lifecycle management framework so that the data can be used in a meaningful way to improve product usage and customer experience.
HCL's Product Intelligence Framework is a comprehensive solution to cater to insight-based telemetry. It has well defined control interfaces to discover and configure the devices. Discovery is possible via SNMP, and configuration is possible via YANG model driven telemetry. The telemetry data interface supports various push based industry protocols like Google Protocol Buffers (GPB), JSON formats, or custom xml or other formats. If the devices do not support push based service, then pull based mechanism can also be employed. Telemetry data is stored in a time series database, which is then ingested to Pangea in a batch mode for analysis and model generation. Machine learning algorithms are applied using Pangea and visualizations/dashboarding is made available on the same , along with real time dashboarding for telemetry data and insight generation.
PROTEL supports auto Discovery via SNMP, SSH protocols. It also supports manual provisioning of devices.
Insight based model selection is supported. PROTEL can configure device models based on published list: YANG model, sFlow MIB, OpenConfig etc.
Telemetry protocol formats supported: GPB, JSON, XML, sFlow. Telemetry Data Configuration: Rate Control, Cadence, Telemetry Model Settings
Streaming Data Connectors
PROTEL supports custom connectors and third party connectors for various telemetry protocols
Data Management & Security: ETL
PROTEL supports device and schema association, queue management, device authentication and data type validation during ETL phase
Data Management Framework: Reporting
PROTEL supports data security, data lifecycle management and data governance features during the reporting / visualization phase.
Actionable insight generation
Role based insight generation for various stakeholders like marketing, product management, engineering and support functions
Machine Learning analysis of telemetry data
PROTEL has a rich repository of pre-built machine learning data models with state of the art algorithms, associated business logic and key metrics generation capability
Reporting and Visualization
Supports building blocks for visualizations, reporting and insight generation that can be tuned according to customer requirements.
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