A familiar trend has come to the automotive space: An increase in the number of digital, automated systems has led to richer, more detailed data output. The Internet of Things (IoT), in which advanced sensors collect data from a wide variety of systems and objects, now includes cars and trucks.

To turn the IoT's potential into real value, automakers, fleet owners, and all other automotive stakeholders need to consider how they'll process and analyze new data sources. They'll also need to build larger programs around these signals to unlock competitive advantage.

What does data analysis mean for automotive manufacturers?

Data tells a story. When given access to real-time streams of detailed data, users can apply analytics and interpret signals, and this kind of informed decision-making is at the heart of the IoT.

Now, items that once required outside observation are sending out signals. In industries like the auto space, where physical wear and tear is a fact of life, these signals have a direct and obvious use case: as fuel for predictive maintenance in automotive spaces.

Predictive maintenance isn't just an automotive concept — it applies to anything that can possibly break down. In simple terms, it's a process of prioritization: Rather than simply inspecting or repairing mechanical parts on a preplanned schedule or waiting for failure, it's now possible to know which items are in greatest need of attention at any given time.

Impactful use cases for predictive analysis in automotive systems

There are multiple uses for streams of vehicle condition information. Manufacturers, owners, and repair technicians are achieving a variety of advantages through data analysis, some immediate and others more long-term:

• Consumers benefit because when automakers receive signals indicating current or impending problems, they can use alerts to warn drivers and ensure they get the repair services they need in a timely manner. The car ownership experience improves, positively impacting the connection between the manufacturer and its customers.
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• Repair technicians can complete their work more quickly and efficiently, accurately fixing every detected issue. This helps them fit more repairs into less time while also helping them achieve good results for their customers, improving the quality of their service. As with the consumer market, access to data can help build the bond between technicians and auto manufacturers.
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• Automakers themselves can interpret long-term trend information to roll out fixes for widespread issues and make changes to their designs that will deliver future reliability. This focus on ongoing quality can help a manufacturer burnish its reputation and save costs that would have to be allocated to expensive recalls. Brands that offer strong tech platforms can stay ahead of the curve within the space.

Knowledge is power across industries and equipment types. When the vehicle ecosystem has the right technologies in place to collect and interpret data, the result is an infusion of actionable knowledge. 

Specialized use case: fleet management predictive analytics

One specific use case where predictive maintenance and automotive data analysis can have an immediate impact is in the management of commercial vehicle fleets. The need to efficiently oversee large numbers of vehicles for maximum efficiency provides a valuable proof of concept for increased data usage.

Shell Fleet Solutions notes that predictive maintenance adds a new wrinkle to fleet management. By scheduling repairs to favor vehicles at the greatest risk for breakdowns, fleet managers are reducing vehicle downtime with data and optimizing maintenance rotations.

In addition to setting up maintenance rotations based on real-time data, fleet owners can take a long view and base their overall repair strategies on patterns of data from previous failures. Furthermore, fleet managers can save money on their repair bills because proactively maintaining vehicles comes with lower costs than recovering from breakdowns.

Specialized use case: data analysis and electrification

The Shell Fleet Solutions report highlights another application of data analytics and predictive analysis in commercial vehicle operations: these technologies can help ease the transition to electric vehicles. Fleets in the midst of electrifying their trucks may want to see data from the new vehicles, both to better care for them and prove their return on investment.

Data analysis platforms can deliver in both ways. With predictive maintenance in place, operators can deal with the new repair tasks associated with electric components such as batteries and regenerative brakes. They can also keep a close eye on the costs and schedules of those repair activities, helping managers justify the decision to expand the electric vehicles fleet.

Key technologies enabling data analysis for vehicle maintenance

Technology for vehicle data collection and analysis has been evolving for decades, from purely fleet-based options to more mainstream systems in cars and trucks of all kinds.

Reliable technology tools are needed at every stage of data analytics programs to ensure they live up to their potential. By building a strong data ecosystem out of these complementary systems, automakers can move toward a more digitally enabled era. Solutions worth prioritizing include:

• IoT sensors: Automotive IoT sensors have become standard equipment in today's vehicles. They continuously collect and transmit data on key systems like the engine, electrical components, and temperature levels. Instead of relying solely on dashboard alerts, this data is now sent to cloud-based networks where AI and ML analyze it in real time. These insights allow automakers and fleet managers to proactively monitor vehicle health, enabling timely maintenance and preventing potential issues before they escalate.
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• Data infrastructure: One element of automotive predictive solutions that may be lost in the shuffle is cloud-based infrastructure to securely hold all the data collected from sensor networks. Handling fast-moving global data from a vast number of vehicles is a technical challenge on its own merits.
  Scalability is critical for data infrastructure. For this reason, the cloud is the natural choice for automakers as they build their data storage and processing systems. The ability to scale up without becoming cost-prohibitive has been a primary cloud computing selling point since the dawn of the big data analytics era.

• Predictive maintenance solutions: Predictive maintenance technology platforms act as the hubs at the center of automotive data analysis efforts. The key function of these solutions is to aggregate input from IoT sensors and apply advanced AI and ML algorithms to find useful patterns.
  The predictive models that sit at the heart of these platforms must accurately predict which sensor readings indicate an impending mechanical problem - collaboration between automotive experts and data scientists is the key to crafting these models.

• Artificial intelligence (AI) and machine learning (ML) algorithms: AI is the hottest area in technology development today. Applying AI and the related concept of ML to analyzing automotive data is a natural next step from this popularity. But what can these systems really accomplish?
  AI and ML tools are capable of generating novel insights from huge amounts of esoteric data. These solutions can also assist with decision-making: rather than raw output, AI systems can produce intelligible results for vehicle owners, fleet operators, and repair technicians to act on.

Automakers that assemble these elements are well-positioned to create useful predictive analytics networks for drivers, maintenance professionals, fleet owners, and others to use in their pursuit of reliable, cost-effective operations.

Transforming automotive technology for next-generation driving experiences

If you're developing a data analytics solution for use in the automotive field, you'll need to focus on creating reliable solutions for every piece of the equation and integrating them into a seamless whole. Weaknesses at any part of the process, from data intake to storage, analysis, or interpretation, can lead to unreliable results.

Working with expert design and development professionals is one way to tap into industry expertise and elevate the quality of your efforts while minimizing strain on your internal teams. The Transcenda team has worked on data-intensive projects of all sizes and types and can provide any level of support you need, from consulting to hands-on, side-by-side development.

When it's time to move to the head of the automotive data analytics and predictive maintenance space, Transcenda can get you there. Contact us to find out what we can do for your project.