Many sectors have been riding the digitisation wave and the automotive industry is no exception. Increasing data analytics in the automotive industry, coupled with changing customer expectations, has led to the change in how cars are built, sold and used. Predictive analytics has played a major role in this shift as our vehicles become more connected and advanced. According to Mordor Intelligence,  The Big Data Market In The Automotive Industry is expected to grow from USD 5.92 billion in 2024 to USD 12.86 billion by 2029, at a CAGR of 16.78% during the forecast period (2024-2029). Europe is the largest market, underlining the value of data-led operations for UK OEMs in the years ahead.

Data science, analytics, and artificial intelligence can help OEMs remain competitive and differentiate by delivering value across all functions, including marketing, sales, and operations. However, argues Alan Glazier when some of the biggest passenger car automakers have more than 10 million vehicles’ worth of data sitting in their data repositories, failure to tap into these vast data stores risks not only lost safety opportunities, revenue and brand value but, critically, undermines OEM opportunities to embrace vital new innovation in car ownership models…

Data Led Innovation

At a time of extraordinary technology innovation, the automotive industry is facing an unprecedented array of operational and strategic challenges. According to McKinsey, “the levels of disruption in the automotive industry coming over the next dozen years are likely to exceed those of the previous 50 or more.” Certainly, the on-going debates regarding the next generation vehicle technology – and the growing customer concerns regarding the adoption of Electric Vehicles (EV) – underlines the enormous changes currently underway.  But that is just one part of a very complex story.

In many ways, the industry has barely started to scratch the surface of the inevitable transformations – transformations that can and must be driven by the evolutions in data science. Data has become one of the most important business assets in the last few years and its value has been further enhanced by the explosive growth of generative AI. Organisations in every industry have been collecting and storing every possible data point with the view to improving business performance and unlocking additional areas of revenue.

The automotive industry has been somewhat slow to the party. Despite the phenomenal, data-led innovation within design and production, the industry continues to overlook significant opportunities to leverage data to enhance brand value, transform customer engagement and improve operational performance.

Overcoming Data Inertia

Despite investing billions of Euros in creating connected vehicles that have the power to collect an extraordinary array of data – from traditional telematics to speed, location, even weather conditions – to date this information remains virtually untapped, used primarily to track component performance and improve safety performance. OEMs have yet to determine how best to commercialise this insight, not least due to confusion surrounding data ownership and regulatory restrictions.

While data ownership concerns are valid, they are unfortunately creating an industry mired in data inertia. These issues will not be resolved unless the industry proactively tackles the situation and, in the meantime, there are swathes of internal data resources that remain massively underutilised. Brand value is a prime example – while this is a massive OEM priority, the approach has been through marketing rather than leveraging data to deliver a tangible change to the experience of existing customers.

One OEM has taken the lead by promising a same brand courtesy car to any customer whose car requires repair. This is a huge commitment given not only the enormous cost but also the fact that the OEM has no control over the repair process delivered by the dealer network. From initial vehicle triage through technical fixes, spare parts ordering and undertaking the work, OEMs have limited visibility and no control over the duration of the process. As a result, a repair that could be completed in 5 days might take 10 – doubling the amount of time a customer is provided with a courtesy car and, therefore, adding unnecessary cost to the OEM.

Leveraging Existing Data Resources

For this particular OEM, however, the brand value of this commitment is strong enough not only to justify the investment but also to overcome the traditional cross-business data silos to create a far more efficient and controlled courtesy service. By pulling together information from across the business, including spare parts ordering and delivery as well as technical information, the OEM is hoping to halve the amount of time customers require courtesy cars.

Improving the visibility of information throughout the process provides the OEM with an end to end view of requirements, from technical fixes to spare parts. The dealers can be more confident about when a replacement part is due, enabling timely scheduling of the work. Meanwhile proactive customer communication regarding problem resolution will further reinforce the brand value.

Conclusion

Effective data use is increasingly defining corporate success and the automotive industry has an extraordinary array of untapped data at its fingertips. There are undoubted hurdles to address with regards to data ownership with connected vehicles but there are also so many areas to explore with existing resources that will improve profitability and, critically, reinforce brand value.

With sales declining and customer confidence eroding, it has never been more important for OEMs to overcome their data inertia and actively embrace data science to continue the industry’s transformation beyond vehicle innovation towards new levels of brand engagement and models of car usage and ownership.