“The IOT generates large amounts of data and above all the expectation, for those who use them, to have processed and transformed them into useful information in real time. Therefore, we need solutions that are scalable in terms of economics and efficiency and, at the same time, guarantee the expected results. The choice of partners such as Agile Lab, always aware of innovation and with a solid expertise in new technologies, allows us to achieve ambitious results and to adopt innovative solutions for the challenges of tomorrow.”
Head of Innovation and Connected Mobility Platform, Vodafone Automotive
Real-Time Analytics in Mission Critical Applications
In order to to provide insurance companies with specific risk profiles for every driver, extracted from on-board black boxes, Vodafone Automotive needs a whole new systems for acquisition and processing of telemetry data, capable of Big Data collection, management and analysis in real time for over 227 million weekly mileage and driving related messages.
The new architecture, based on Cloudera, adding the components of Apache, Kafka, Spark and the combination of HDFS and HBase, has been specifically designed to be available on-premises. The Cloud Computing branch of Vodafone handles the management and maintenance of the environments, while the company relies on its ongoing collaboration with AgileLab for the application services development and management.
This new architecture – introducing a more flexible and innovative platform – has enabled the company to meet the level of service expected by Clients (i.e. a maximum of 15 minutes of latency between data generation and its reception on the Client’s Cloud, including cross-time on both mobile networks and the Internet) and it has become the solid foundation for developing services that the company wouldn’t otherwise have been able to offer.
Vodafone Automotive, part of Vodafone Group, operates in the specialized segment of Vodafone IoT, and it focuses on providing IT platforms to the mobility world. The company supplies telecommunication services and products targeted to the automotive sector. In particular, it offers stolen vehicle assistance and recovery services, theft prevention, crash and vehicle accident management services. Specifically for insurance-related services, it provides analytical functions for driving habits and styles, risk-management assessments, as well as a wide scope of vehicle management services (i.e. maintenance and management life cycle) for both fleet and OEM manufacturers (of automotive on board electronics). The main focus of our case study is on Usage Based Insurance (UBI).
UBI aims to address the traditional trade-off between the driver’s privacy and the cost of the insurance plan, a key aspect of the car insurance sector. Vodafone Automotive is able to provide insurance companies with different driving style profiles by collecting multiple information, as for instance the location and acceleration of a car, by installing on board the vehicle an electronic device (the black box).
Through this information, Vodafone Automotive helps insurance companies create a “score” that represents with the outmost precision the risk associated with the type of driver and therefore the single insurance plan, also providing data on the type of roads traveled, driving times, and many more.
The project was born in light of a necessity for extracting the maximum value from the data generated by on-board devices (often called black boxes, like the flight recorders on airplanes), to better cater to the needs of insurance companies. They can therefore utilize this data for policy pricing, (computationally, is organized in time intervals, with pre-established elaboration cycles and post-elaboration submission of data sets as per agreed with the company and used, for example, at the time of policy renewal, quarterly or even annually), but also to offer new services to their subscribers, strengthening and enhancing the customer experience. For example, by sending alerts related to the level of danger of a certain area (i.e. where the client may have parked), or localized weather-related alerts (such as hail alerts).
In compliance with Vodafone Automotive’s goal to increase safety on the street, the company launched this project to revise and revolutionize their systems for acquisition and processing of telemetry data (generated by systems previously installed by Vodafone Automotive on insured vehicles) by introducing the features and capabilities offered by the Cloudera platform to collect, manage and analyze in real-time the Big Data sent by the installed black boxes.
The Vodafone Automotive project, started in 2017, was aimed at deploying, managing and consolidating a platform able to collect and elaborate big quantities of data, to help insurance companies risk evaluation process both for issuing insurance plans and offering real-time services to their customers. The project led to the replacement of the previous architecture with a newer and innovative one, based on Cloudera, adding the components of Apache, Kafka, Spark and the combination of HDFS and HBase (architectural model “Lambda”), and later on also NIFI – which can elaborate data with a latency of a few seconds, regardless of their quantity or frequency. The primary feature of this platform is its ability to flexibly manage high volumes of data, and to be able to expand and grow according to the company’s evolving needs. Data processing occurs mainly through Apache Spark, which captures the data and processes them after their extraction from Code Kafka. Afterwards, the platform drops the base data on a distributed HDFS file system. Whereas processed data are saved on the NoSQL Database, achieving impressive performance results. The collected data are then sorted through the Lambda architecture, enabling both real-time data processing and effective storage for future re-processing needs. To accomplish the latter function, the architecture relies on NoSQL HBase. It should be noted that the primary data processing reconstructs the driver’s path from localization and speed data, and geographical information acquired through GPS system and the accelerometer in the vehicle’s black box.
Additional operations are required to guarantee the reliability of collected data: it is fundamental, for instance, to perform data cleansing and preparation, in order to spot any device malfunctions or differentiate between a pothole and a car’s impact (and consequently understand whether or not to provide assistance to the driver). The new architecture has been specifically designed to be available on-premises, and its related servers are placed in Vodafone Group’s Technology Hub in Milan, (for Vodafone Italy), which hosts the VFIT services. Also, a back-up server cluster has been created in the twin data center of Vodafone, as part of the disaster recovery plan. The Cloud Computing branch of Vodafone handles the management and maintenance of the environments (GDC – Group Data Center, where the data processing resources are being implemented. Vodafone caters to the Southern European market through this structure), while the company relies on its collaboration with AgileLab for the application services development and management. As a matter of fact, the architectural evolution that Vodafone Automotive implemented allowed the company to not only effectively handle high volumes of data, but also represented a qualifying element to guarantee the availability of real-time analyzed and processed data to insurance companies. Thanks to the new platform, today insurance companies are able to receive real-time information on the driving habits of their client, with a latency of mere minutes from the event registration in the vehicle’s onboard black box.
The following are some figures from our project:
- over 33 million messages per day
- 227 million weekly mileage and driving-related messages (for insured clients)
- 130 terabytes of data collected in 3 years.
From a management point of view, the project has required the involvement of a dedicated team, that focuses exclusively on the design and development of new architectural scenarios. This organizational choice and the collaboration with AgileLab – which took charge of every detail regarding the planning, engineering, and optimization of the application platform – played a key role in the success of the project. After the project launched, the team created by Vodafone Automotive to manage the development phases of the project, joined the IT of the company to work in the areas of Project Management, Development, Operations.
The greatest challenge faced by the company has been the need to integrate numerous recent technologies into its existing information system kit. The IT department was required to manage a series of new tools and platforms, and take all the necessary steps (also from a training perspective) to both maintain and employ those technologies to their fullest potential.
Achieved Results and Benefits
First of all, the new architecture – introducing a more flexible and innovative platform – has enabled the company to meet the level of service expected by Clients (i.e. a maximum of 15 minutes of latency between data generation and its reception on the Client’s Cloud, including cross-time on both mobile networks and the Internet). In addition, the new architecture has become the solid foundation for developing services that the company wouldn’t otherwise have been able to offer. It allowed Vodafone Automotive to acquire a definitive competitive advantage, positioning itself as one of the most innovative players on the market.
Among the potential evolutions of the platform, there is the possibility of adding a Machine Learning function to be applied to reliability and data quality check processes, even in streaming mode (as they occur). The introduction of automatic learning techniques would allow the company to identify any possible device malfunction a lot more quickly, becoming proactive in the process of maintenance and replacement, when needed, of the black box. This would also bring about the added benefit of avoiding corrective measures on corrupted data ingested because of device errors or malfunctions.