VIN Basics Blog

Advanced Driver Assistance Systems, aka ADAS, is a hot topic in the auto industry, especially as these safety features become more widespread across manufacturers and their model lineups. Whether your business is in the retail, insurance, auto repair/service, or logistics space, you’ve likely been impacted by ADAS in some way, shape, or form.

Over the next few months, we will share a series of articles on ADAS and its relevance to businesses across the auto industry.  We’ll also be sharing some valuable data about the growth of ADAS from our proprietary vehicle database, and addressing how the use of a comprehensive vehicle database could help solve some of the challenges at hand with such a technical and sometimes confusing topic. Let’s start by learning about what ADAS is and how it came to be!

This post has been republished with updates since its original publish date of November 2011

As you might expect, we get a lot of questions about VINs. There is a lot of confusion about what a VIN is, how it is used, what information is captured within it, and what is not. In this series, we hope to provide a good resource that you can use to educate yourself on Vehicle Identification Numbers (VINs) and VIN Decoding. The articles will progressively build from very basic and general information about VINs to more detailed and specific aspects of using them and vehicle data in business and technical settings. We hope you find them useful.

Whether you’re a large salvage yard that builds out its proprietary tools for yard management, or a solution/software provider to small to mid-sized salvage yards, it’s essential that the vehicle data you leverage is accurate and complete enough to meet requirements for critical use cases, including some of the most common instances outlined below.  

VIN decoding and identification are terms that have a different meaning and context depending on the solution type and the use case. This could mean simply determining the year, make, model, and engine. Others could include much deeper level attributes including equipment, as well as the return of specific ACES VCdb fields for vehicle-to-part matching and compatibility.

In this article, we’ve described a few primary cases where vehicle data can be pivotal for efficient yard management processes.

Lithium-ion batteries, which power both fully electric and hybrid vehicles, are revolutionizing mobility and offering a cleaner alternative to gas-powered, internal combustion-based vehicles. However, a growing challenge lies beneath these batteries’ immediate eco-friendly benefits: recycling and reusing them. Due to the mining of scarce resources, EV battery recycling represents the most expensive component and largest logistical challenge for electric car adoption. A previous DataOne article discussed how EV batteries have a finite lifespan, and recycling can offer a more sustainable end-of-life solution. Understanding the impact of this technology requires examining the full picture – including the benefits of battery recycling, challenges, and drawbacks, and where the future of EV batteries is headed. Addressing these uncertainties today can lay the groundwork for a greener future.

Electric vehicles (EVs) continue to gain traction in the automotive markets, posting record high sales volume and market share in 2024. As more consumers inquire about EVs, questions about charging options and battery longevity are natural, and confusion around the jargon for the different types of EV chargers can be expected. Various types of charging levels are available–each with its own speed, convenience, and impact on battery health. Properly understanding these choices can significantly extend your EV’s lifespan and preserve its performance. 

A previous article by DataOne compared the steady, gradual charge rate of Level 1 to the rapid power of DC Fast Charging. Each charging option discussed in that article has unique benefits, potential drawbacks, and ideal use cases. However, by understanding how these charging speeds impact battery health, the everyday user can make more informed decisions that balance convenience and long-term performance for EVs.

A while back, we wrote an article highlighting some of the key differences between a free VIN decoder service and a paid VIN decoding solution. When actively comparing enterprise-scale, commercial vehicle data solutions for your business, the differences are not as obvious as with free solutions. Many commercial VIN decoding and vehicle data providers offer a robust level of detail for a large number of major OEMs, and may even offer different levels of specificity to match your business's data requirements. Rather than producing an elaborate text-based document to explain the differences, the DataOne team felt it would be more helpful to display how DataOne's VIN decoder API stands out from a basic or standard VIN decoder in an infographic format.

The auto insurance industry, after several years of experiencing unprecedented increases in loss ratio, managed to stem the tide in 2023 by posting a 104.9% mark, a 7% improvement from the prior year that still remains as a net loss. As these organizations continue to see business in the red, owner behavior is shifting in a manner that could easily see a return to loss increases. According to LexisNexis, high claim severity persists, and increased EV sales (up 54% year-over-year) could also inflate loss due to their increased susceptibility to total loss.

Vehicle technology is increasing in complexity besides EVs, and that complexity is a prime driver in added repair and claim costs. A recent AAA report that repairs focused on Advanced Driver Assistance Systems (ADAS) make up over 37.6% of repair costs after a crash. The industry-wide trend toward full vehicle automation is also fundamentally shifting risk from the driver to the car, and insurance risk modeling has not yet caught up to satisfactorily factor this in. McKinsey writes that this “connected revolution” in the automotive industry “means insurance coverage will likely need to shift from drivers to the automakers and software companies responsible for the development and maintenance of various autonomous-driving technologies.”

There’s nothing like the smell of a brand-new car, but with the average cost of new vehicles now sitting at  ~$50K, many consumers have opted for used vehicles as an alternative in recent years. New vehicle prices have spiked since the COVID-19 pandemic, with average transaction prices remaining nearly $11k higher than pre-pandemic back when average transaction prices for new vehicles were ~$37k. In this article, we will discuss some of the key drivers behind increased vehicle prices, how it has impacted the auto industry, and why a comprehensive vehicle data solution is even more important for the automotive businesses impacted.

The success of an e-commerce automotive parts platform depends on its ability to accurately convey the parts its customers need to complete their projects, but the automotive industry itself is making this easier said than done. Part returns are unfortunately a significant facet of their operations – the most recent National Retail Federation report showed that auto part returns are the most significant percentage of all returns in retail industries at 19.4%.

The frequency of parts returns is primed to only increase thanks to the worrying trend of parts proliferation; the significant expansions of vehicle trims in recent years, as well as new EVs, have led to enormous increases in the parts needed for effective service and repair. Lang Marketing, which released a study on this topic, has noted that “parts proliferation in the car and light truck aftermarket will soar for the foreseeable future, increasing inventories and the logistical burdens on manufacturers, distributors, retailers, and installers.” The necessity of a much larger parts inventory will naturally mean an increase in needed returns. The time needed to catalog this large volume of returns could disrupt businesses not prepared to handle it, possibly causing damage to your business’ brand and revenue flow. 

Fleet managers commonly struggle with unpredictable maintenance costs, vehicle downtime, and optimizing resale value. These challenges, compounded by a lack of real-time data, create inefficiencies that impact profitability. Effective fleet management, regardless of fleet size, requires taking advantage of many solutions and tools, where the optimization is only as good as the accuracy of the data that is being measured. This article explores a few common areas that can be challenging, specifically – fleet vehicle: acquisition, management, and resale.