Mar 23, 2021 | 5 minutes Read

Top Four Challenges and Fixes in Telematics Management

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“The Global Vehicle Telematics market was valued at USD 43.61 Billion in 2019 and is expected to reach USD 129.2 Billion by the year 2027.”[1]

Telematics, the process of analyzing and collecting data from vehicles, is used primarily for fleet management, workforce efficiency and more. The advantages of telematics include accurate measurement of data, driving styles, battery & energy consumption, driver behavior and risk assessment for automotive insurance, real-time vehicle diagnostics, and so on and so forth.[2] While the advantages of telematics are well known and statistics prove that it is an integral part of the automotive industry’s future, employing telematics efficiently is not a challenge-free journey. Through the course of the article, we address the top four challenges in telematics management and their fixes.

Challenge 1: Enabling Efficient Data Management Practices in Telematics

Data management in telematics comes with a fair set of challenges. Enabling telematics via smartphones would be relatively easier to transition to from traditional setups given that most vehicles are already enabled with smartphone syncing capabilities for GPS tracking, etc. When connected with an all-encompassing mobile application, drivers can leverage telematics to not only get a wholesome view of the vehicle’s health but also help create a complete overview of any unforeseen circumstances that take place, which can be leveraged especially for insurance contracts.

Furthermore, data collation is easier said than done. There are many instances where telematics is already built into vehicles, a feature that the customer is aware of when purchasing the vehicle. While such pre-existing features are great automated sources of needed data, the methods are in need of standardization. Additionally, the manufacturers need to ensure that data collation aligns with the needed privacy and security norms, given that it depends on the customers’ willingness to share data. Creating awareness among customers about how data collation might help when insurers underwrite the vehicle and provide a holistic 360-degree view of the vehicle’s functioning might help in easing collation efforts.

One major advantage is that models powered by telematics can keep track of not just driver performance but also factors like road conditions, locations, accident history, etc. This is a step that conventionally takes longer in the absence of telematics and automated data collation.

Telematics can also cross reference vehicle performance data with historical data which in turn can power predictive models for vehicle assessment. However, while bigger firms might be able to collate the mass of data required, smaller insurers might not have the needed volume of data and this, coupled with cost, might be a major deterrent for them to employ telematics in their vehicle insurance services.

One way to mitigate this challenge is to diversify data collation. Collaborating with third-party organizations can help them gain the needed information and ensure that it is not restricted to their own resources. Not only would this help small insurers, but also larger companies as they too must look at diversifying data for accurate modelling. While at present, data gathering might be restricted to the extent of one company’s policy holders, in the long run, industry-wide data pooling would ensure that better policies are created.

Yet another issue that stakeholders are posed with in data management is filtering through the copious amounts of data collated with telematics. Fleet managers and analysts need to ensure that data bases are equipped to handle the heavy influx of data generated by telematics devices. Traditional data warehouses do not have the capacity to handle copious volumes of varied data flowing through frequently and often, at near real-time intervals. In this regard, firms must consider investing in Big Data-powered data warehouses where large volumes of data can be analyzed to reveal pertinent data patterns and trends.

Of the data collated, unwanted information or noise has to be sifted through. To mitigate the same, ML models can be set up to highlight data insights that warrant immediate attention, such as highest idling time, rapid fuel utilization, etc. Alternatively, via automated classification, the data can be presented in parameter-specific dashboards, such that the clients get to scrutinize particular aspects such as identifying parts that need replacement, attending to asset depreciation, reducing idling time, optimizing fuel usage, etc.

Challenge 2: Adoption Barriers

Budgeting for telematics is one of the major points of concern for fleet managers. In many cases, transport or fleet management is a part of routine operations and not the revenue generating function. Therefore, most managers are hesitant to budget money for telematics.

However, when leveraged right, telematics becomes a significant cost-saving opportunity for consumers. Depreciation and fuel are the costliest fixed expenses for fleets and one steep uptick in fuel prices can adversely affect operational costs.

Furthermore, telematics can be used to motivate better driver behavior - erratic driver behavior sometimes results in as much as 20% more fuel consumption. [3] Telematics can help observe driver characteristics such as acceleration, waiting time, and cruising, and gauge its impact on fuel expenditure as well as vehicle depreciation.

Telematics also helps assess inconsistencies in fuel logs - helping understand whether there have been cases of fuel theft or fuel misappropriation. With continuous analyses, other aspects such DEF levels and electrification suitability, etc., can be assessed to identify future cost saving opportunities.

Yet another challenge faced is lack of driver readiness to adopt telematics. In 2014, about 47% of drivers were not open to having their driving behaviors monitored via a mobile device.[4]  However, a survey in 2019[5]  indicated that while these numbers were improving, there were about 35% of drivers believed that law should not mandate monitoring, indicating that awareness and acceptance is still moving at a very slow pace. In terms of age, adoption was and continues to be more prevalent among younger drivers, who believe that telematics makes the vehicle safer. On the flipside, the costly nature of the technology has made many reluctant to shell out money for the same. In the long run, perhaps, this might lead to a market bifurcation: cars with telematics for driver monitoring and a niche market for cars without monitoring.

Therefore, before launching telematics systems, companies must look at creating awareness about the advantages of the technology and highlight that it goes much beyond just analyzing driver behavior. As the roadways become increasingly autonomous, telematics will empower vital functions such as predictive maintenance, route planning and optimized power usage, all of which can result in greater savings for the drivers.

Apart from educating drivers about the product advantages and savings capabilities, companies must also look at collecting extensive data to form detailed consumer personas based on the market and sales data already available to them. Furthermore, firms must determine who they can market the product to, simulate the success rates of enabling alternative payment methods to increasing affordability and perform further marketing, social media listening, etc., to gauge receptivity and product acceptance.

One of the major challenges that firms might have to grapple with is instances of unforeseeable behavior. Incase drivers are aware that their behavior is bound to bring up some red flags, they might also be prompted to counter-productively try dangerous techniques to avoid being marked unsafe upon scrutiny. Some might try to tinker with the GPS signals, or even turn off their telematics, disrupting the data being automatically relayed. Therefore, to steer clear of such malicious intent, telematics manufacturers need to constantly tweak their models.

Challenge 3: Privacy & Security

In Europe, General Data Protection Regulations (GDPR) is a statutory requirement that fleet managers need to comply with. And in this regard, there needs to be a thorough understanding of what can be classified as ‘personal data’, and how the classification can impact driver privacy and security. In such cases, fleet managers must analyze how their data is being used and also inform drivers about the data being mined to ensure consent, guard individual privacy and security, and reduce potential conflicts that might arise over consent.

Another major cause of concern in this technology is to guard against data theft. Miscreants might collect data about another driver, for instance, and use it for their own insurance purposes. Also registered driver identities might get stolen, details might be tampered with, and so on and so forth. Therefore, telematics manufacturers must actively invest in fraud detection and in identifying patterns of fraud detection – against external miscreants and those attempting device tampering, to ensure that the technology is used responsibly.

Challenge 4: Hardware Management

There are multiple types of hardware used to track various aspects of vehicle health and performance. Devices like OBD II ports are placed to analyze vehicle emissions. Any trouble code sent by the vehicles to alert managers on the parts that need remediation can also be collated through this device. Such plug and play devices can be flexibly adopted because they can be implemented and moved to other vehicles with minimal effort.

Alternatively, tracking devices can also be hardwired into the fleet. This tech is useful when specific vehicles need to be tracked, and given that the device runs on the vehicle's battery, there is no need for any additional battery recharge, etc. This is specifically useful to guard against vehicle theft as hardwired devices cannot be easily noticed by miscreants and even if they do identify it, it is difficult to tamper with the device.

Management of this hardware is an essential part of telematics. Mapping the hardware to the most beneficial end use case is important to ensure optimal telematics ROI. Depending on how often data is collated and the functions for which the fleet is leveraged, the right device can be utilized to ensure that minimum time, effort and resources are spent towards installation and upkeep of the hardware. Therefore, before choosing to leverage the benefits of telematics, firms must have a thorough understanding of the kind of functions that are imperative to their fleets and the conditions they operate in. This will help to accurately leverage the right kind of hardware for telematics requirements.

What Does the Future Hold for Telematics Adoption

“The telematics market is expected to register a CAGR of 20.7% over the forecast period from 2021 to 2026.”[6]

Despite the challenges that arise, companies need to actively work towards setting up telematics because in the future, data will become as important for cars as fuel/electricity. While it might be a long way before manufacturers of both the luxury-end of services and the more affordable alternatives are able to leverage the technology easily, connected vehicles are increasingly becoming a “common feature”.[7]   Therefore, while there are challenges to deploying this technology, its advantages are far too many to be left ignored. What the industry needs is standardization of practices to ensure optimal telematics efficiency.

Telematics is no longer the niche market. Connected vehicles are the major determinants of the future of the automotive industry.



AUTHOR BIO

Amit Shah | Delivery Manager

Amit Shah is a Delivery Manager at TheMathCompany and is highly skilled at data science, business strategy and project management. With extensive experience in the transportation and automotive sectors, he focuses on enabling analytical transformation and self-sufficiency for diverse organizations. A coffee and F1 enthusiast, he also enjoys the outdoors and can be found hiking, exploring new places and cuisines, and appreciating a good movie.

AUTHOR BIO

Srikar Manepalli | Senior Associate

Srikar Manepalli is passionate about problem solving. He has close to five years experience in the analytics and data science world. He is especially experienced in working in the automobile industry on price optimization solutions, demand prediction models, recommendation systems and much more. When not at work, Srikar takes time off to indulge in a biryani like there's no tomorrow.

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