Analytics and IoT platform and services

Our platform for developing and deploying end-to-end analytics applications

Nokia Warp 6 platform

Applications built with Warp 6 comprise three layers: Data integration and correlation, Advanced analytics, and Visualization. Warp 6 is optimized to move data and results efficiently through and within these layers to create scalable, efficient, effective applications.


Warp 6 components

Warp 6 platform components are tightly integrated to deliver solutions that solve your toughest operational challenges.


Insight and action delivered anytime, anywhere, to anyone. Learn More

Advanced analytics

Intelligence, machine learning algorithms. Learn More

Data integration and correlation

Seamless integration with virtually all internal and external systems. Learn More

Delivering faster time to value

Warp 6 has an efficient, modular architecture so we can build rich, tightly integrated web and mobile analytics applications in a fraction of the time custom or in-house software would require. Our platform and built-in expertise allows us to configure applications for specific customers and use cases in less time and less expensively than other approaches. That advantage builds as your needs grow and you expand your analytics application portfolio.

“Recipes” approach for rapid development

Warp 6 platform


Asset Inventory + Telemetry + Location +
Anomaly Detection + Failure Prediction +
Maintenance Optimization + Crew Optimization +
Visualization + Mobile App


Data Integration and Correlation

Business data

Business data

Data from business systems such as CRM, ERP, data lakes, analytics databases, and asset management systems contribute historical, financial, and transactional details to applications

Operational and IoT data

Operational and IoT data

Static data and real-time streaming data from a wide range of sources and IoT devices is accessed through built-in interfaces to support timely, high-confidence decision-making

External data

External data

Weather, fire, temperature, wind, traffic, and social media data are some of the many external data sources that improve effective management of operations and assets. The data is then correlated across space, time, and nodes in a network.



Spatial analysis provides an understanding of where assets, resources, and events are located, and their proximity to other assets, people, and structures, and how location factors into operational analysis.



Temporal analysis provides insight into when events occurred and views of historical or possible future events



Nodal analysis drives an understanding of how  assets and resources  connections are connected on physical and logical networks

Advanced Analytics


Historical data is analyzed to uncover unexpected variations in behavior or performance; streaming data is analyzed to detect and alert in real-time due to specific anomalies and events


Intelligent models learn how to find answers to problems too complex for humans with needing to be explicitly programmed to accomplish their task.


Data is analyzed across multiple dimensions using machine learning models to suggest possible future outcomes, facilitating planning and what-if analysis and driving optimization models.


Models learn from the outcomes of actions that it takes to find an optimal solution to a defined problem.

event processing

Seemingly unrelated events are correlated to create awareness of situations that require attention

Visualization and Action

The analyzed data results are presented in a wide range of intuitive visual formats, in the operations center, the executive suite, or in mobile applications.

We provide unique contextual understanding to users, helping them make more-informed decisions as a result, and automate optimized responses or allow them to take action within our applications, without data reentry, manual correlation of data across applications, or off-line analysis of data in spreadsheets.


Example application

Predictive maintenance and optimization

Connect and collect sensor data from equipment, business systems, other operational data, and external data (such as weather conditions). Predict the risk of asset failure. Optimize the asset repair, refurbish, or replace decision to extend its life as long as possible before replacement is actually necessary. Then do the work when it can be done at the lowest cost based on crew location, skills and part availability. Visualize that asset and repair planning in context of the whole asset base and crew locations. Act by sending jobs out to crews and moving inventory and equipment.

Predictive Maintenance and Optimization

Example application

Logistics and transportation

Connect trucks, equipment, and palettes of goods in transit. Predict arrival times, where the inventory needs to be, where people with specific skills need to be, where to locate which parts and how many. Optimize routes and loading dock schedules, crews, and inventory storage. Visualize the whole system and alert operators to problems. Act to redirect drivers and change job schedules.

Logistics and Transportation