Driver Data: Advances in Innovative Exchange

With an innovation worthy of the digital age, the field of vehicle telematics is bringing auto manufacturers and insurance companies into sharper alignment. Now, data recorded in an individual vehicle can be “crunched” to yield insightstelematics about driving behavior—insights that can shed light on a driver’s risk category. In a further innovation, 2016 brought the establishment of a telematics data exchange, enabling risk managers to make use of this data with the consent of drivers.

Telematics data can potentially benefit consumers, fleet owners and insurers. Instead of insurers generally relying on a driver’s general information—age or gender, for example—policies can be written to address specific levels of risk supported by actual driving data (speed, acceleration, braking and time of operation). So the elements are falling into place to tap telematics-derived data, with potential for also attaining higher fuel efficiency and better fleet vehicle performance.

How do consumers and fleet owners benefit?

  • Rewards: Discounted insurance for drivers who have fewer risks or lower annual miles
  • Ease: Greater convenience, flexibility, and portability when shopping for auto insurance
  • Safety: Promotion of good driving habits
  • Savings: Insurers’ enhanced ability to segment risk types, potentially lowering premium costs for commercial fleet owners and managers

History of an idea

The seed for telematics was planted in the early 1960s, during a period when tensions between the United States and the former Soviet Union were escalating. That is also when the U.S. government, intent on national security and concerned about a potential nuclear threat, funded development of Global Positioning System (GPS) technology. Initially, GPS was intended for military and intelligence applications. By the early 2000s, telematics technologies were used in web-based fleet management systems that featured real-time information updates to remote networks. At that time, slow tracking rates limited data transmissions to one or two instances per hour. It wasn’t long, however, before GPS-based vehicle navigation systems flooded the consumer market.

Aligning value

In recent years, telematics has brought auto manufacturers and insurers into alignment, with both industries recognizing the potential of telematics. Automakers have found value in using telematics data to communicate information to car owners about their vehicle’s maintenance needs and performance and to convey information to consumers about their driving behavior, which could lead to safer driving. In turn, safer driving—such as fewer sharp turns and hard-braking incidents—could positively affect vehicle performance and fuel efficiency. And insurers have found a means to help better define risks.

Automakers also recognized that better fuel efficiency and less wear and tear (requiring less maintenance) could potentially save money for consumers, thereby reducing the total cost of car ownership.

Many insurers, too, quickly saw the inherent value of telematics data. Traditionally, insurers rate consumers on various factors that typically include proxy data to predict an individual’s risk level, which helps determine rates. Some consumers may complain that not enough insight goes into the rating process. Yet telematics data, applied through usage-based insurance (UBI) programs, allows insurers to consider details of individual driving behavior—which might lead to more accurate and customized pricing. Insurance rating could become more focused on individual behavior and performance. Insurers understand that a benefit of using telematics data as part of their underwriting practices can include the consumer’s perception that carriers are operating with greater transparency—and potentially give consumers greater understanding of their auto insurance expenses.

Consumers could now examine their own driving data—and likely this data overlapped with the data their insurance company reviewed when establishing their rate in the first place.

Great leap forward

For some time, we’ve said that a telematics data exchange might represent the future of usage-based insurance. That future isn’t far away. Consider this: It is estimated that by 2020, more than 90% of all new vehicles sold in the United States will be able to connect to the internet. Today, about 5% of vehicles are so equipped. That is a powerful leap forward in terms of the data that will be available from connected cars.

This gives auto-makers the potential to capitalize on vast amounts of data collected by the connected cars they sell. Insurers can benefit by potentially enhancing their efforts to acquire and retain safer drivers and monitor their policyholders’ driving behavior and vehicle mileage.

There can be corresponding challenges related to such connected vehicle data, however. The volume of data from connected cars is enormous and growing. The hardware, software, and carrying costs needed to store and manage that data can run into the millions of dollars—a cost many insurers may find onerous. Automakers face their own set of issues, chief among them being the “many-to-many” problem: how to connect with hundreds of insurers that might be interested in accessing their data. While those are just a few of the multiple hurdles to overcome when harvesting exponentially growing stores of data, these are challenges that a telematics data exchange can help address. That is why the launch of the first data exchange marks such a critical milestone in the history of telematics.

5 Analytics Tips for Your Chief Safety Officer

Safety data
Industries on average experience 3.2 non-fatal occupational injuries per 100 full-time workers, according to the U.S. Bureau of Labor Statistics. Some industries have nearly four-times this rate. Similar statistics exist for workplace illnesses and, unfortunately, fatalities. Could analytics be a solution for lowering these statistics?

Companies today gather huge volumes of operational and enterprise data, plus they have access to myriad sources of external data such as weather, traffic and social media. Unfortunately, this data is normally stored and analyzed in siloed data systems that are scattered across the enterprise. There are, however, steps a chief safety officer (CSO) can take to apply analytics to all available data to reduce incidents and, therefore, safety-related costs.

Here are five steps CSOs and other safety leaders can take to be smarter about data and safety.

1. Know your network

To reduce incidents and therefore safety-related costs for your organization, you need to know the what, where, when, why and how of accidents. After all, accidents happen at a specific time and place, and involve specific people and pieces of equipment. Knowing your network of time, place and equipment speeds up response time when accidents happen, and can even prevent them.

Analytics systems are now able to correlate, analyze and visualize operational, enterprise and external data from across your company. The resulting information can identify the situations, patterns and trends that indicate hazardous but preventable conditions. You can more clearly see the job roles, work sites and times of the day or week that pose the greatest risk. This information lets you invest your time, money and effort where it has the greatest impact.

2. Collaborate across departments

When you have analytics illuminating the times, places and activities of greatest risk, share that with everyone who can help reduce that risk. Workers and their supervisors need to know what the data indicate about risk, so that they can make appropriate changes. Your facilities department needs to know that some aspects of a work site—lighting, ventilation, access and drainage—contribute to unsafe conditions. Human Resources needs to know what training and certification is required, or should be offered, to increase staff potential.

But collaboration isn’t simply feeding analytics to various job roles. It is important that all those roles—operations, facilities, HR and more—share the same view of analytics in order to work together to address dangerous conditions before something happens.

3. Learn to trust your own data and analytics

There is now too much data arriving too quickly for us humans to manually gather and analyze. It’s still common for business and risk analysts to spend 80% of their time gathering data and only 20% applying it to solving problems. Analytics systems that correlate and analyze multiple data sources flip that equation, enabling analysts to spend 80% of their time acting on insights from data to solve problems.

While you might be willing to trust the math of analytics, you are probably like a lot of leaders who don’t trust their data. Many leaders believe their data is too incomplete, inaccurate, outdated or irrelevant to support an analytics program. When people say this, I usually ask them how they know their data is bad. Until you work with your data, you don’t really know its condition. When you start working with your data to solve a use case, you can address any data quality issues related just to that use case, without needing to somehow fix all of the data.

4. Look for analytics-leveraging skills when hiring

There is a witticism in the business world that “Culture eats strategy for breakfast.” While sayings like this can be cliché, in the case of analytics, this one is true. If your human and work culture doesn’t embrace data-driven decision making, any analytics strategy faces uncertain odds of success.

To establish an analytics culture within your organization, hire people who are comfortable exploring and applying data. You don’t necessarily need to hire data scientists, as that skillset is available from consultants and vendors if and when it is needed. You do, however, need people who are curious and capable of working with each other, and with data scientists, to formulate inquiries, pursue those inquiries, and apply the insights they discover.

5. Start small, but start now

Existing company safety programs that are not data-driven struggle to show their impact. That makes funding harder to justify, which can mean safety programs grow stale over time. If you’d like your organization to be better at safety and analytics, but struggle to measure the effectiveness of your investment in safety programs, it is possible to start small.

Any CSO can immediately identify their most dangerous job role or location. Start with one of those dangerous situations, use data to drive tangible changes in facilities, tools, process or training, and measure the results.

It is really that simple. You can start small, but at least start—now—and make safety a priority.