How to Use Data Lakes for Automotive Industry Data

For the automotive industry, Big Data has become as valuable as oil. An increasing amount of data is being generated from end-user sensors fitted inside automobiles, as well as data collected from supply chain logistics, warehousing, advertising and marketing. Extracting meaning from all this data offers new insights and greater direction to key stakeholders in all regions of the industry, including OEMs, suppliers, manufacturers, insurers and dealerships.

In 2018, this business sector was expected to spend over $3.3 billion in Big Data, with this figure expected to increase by 16% over the next few years. This rapid growth exemplifies that data is one of the biggest assets for the automotive industry. Data Lakes democratize data by allowing both structured and unstructured data to be stored in their original raw condition and in real-time. Real-time data storage is the driver that will enable this industry to remain adaptive and innovative.

Data Lake Advantages for Sensor Data

Most of the recent changes in the automotive industry have arisen from new technology. Think back to sitting behind the wheel about a decade ago—it’s a very different experience, as today’s vehicles are far more technologically advanced. In fact, today’s high-tech vehicles are likely akin to what your grandparents might have imagined for future travel. Modern vehicles are essentially computers on wheels, with sophisticated proximity sensors, hands-free dashboard computer systems and automatic braking designed to make driving safer. Satellite radio, WiFi, and hands-free calling allow you to remain connected while you’re on the road. Meanwhile, built-in GPS systems have replaced paper maps by serving as a personalized and automated means of navigation.

Connected cars—while making driving a whole different experience—also generate large volumes of data every second. The challenge lies in collecting this data and analyzing it in a manner that will give vehicle manufacturers immediate insights, such as how a vehicle performs in different road, weather, and traffic conditions. The information that’s gathered can be utilized to provide value add-ons, tailored to the individual consumer’s wants and needs. These insights have improved after-sales service, fueled new product promotions, and drivers can even be connected to local roadside assistance operators. A ripple effect has moved through the automotive industry and beyond, impacting third parties such as insurance providers. In fact, consumers now have the ability to share their sensor data with insurance providers who can now offer customized insurance plans that reward safe driving.

Sensor data can also give rise to product design innovations thanks to data on customer usage patterns. Many cars and commercial vehicles already have Advanced Driver Assistance Systems (ADAS) but manufacturers such as Ford are now on a new trajectory to introduce even more advanced IoT services embedded with AI technology. The fast-evolving car industry is prompting automakers to rely heavily upon software providers as they strive to design vehicles that blend improved safety features with comfort and ease of use.

According to some sources, a vast majority of all automotive-related data in existence has been generated in the last few years alone. These large volumes of information will continue to increase exponentially, making Data Lakes an essential tool. Cloud-based Data Lakes are inherently scalable by nature, so they’re especially suited to handle ever-increasing amounts of data.

Using Data Lakes for Enterprise Data

Automobile manufacturers, dealerships, rideshare companies, and even rental car agencies such as Uber or Hertz have global operations. Advanced Analytics is driving these companies’ global supply chain management and effectively reducing risk. To do this, they have had to break down individual data silos to understand data flow across supplier chains, procurement, operations, sales, and customers. This will create faster reactions based upon incoming data, while simultaneously creating quick alternative responses.

Enterprise Data Lakes are centralized repositories of structured and unstructured data that provide the right storage platform for Big Data tools to drive actionable insights. These tools are allowing the auto industry to identify patterns and correlations in their enterprise data like never before. The cost-efficiency of these cloud-based systems have made them a viable option even for small and mid-sized players.

A proactive management model is visible in a number of use cases within the automotive industry. Managing inventory no longer involves just replacing cars that have been sold. There are certain models and colors that will sell more than others. Data-driven stocking decisions will be more beneficial for the success of a dealership. This technology can help businesses plan ahead in order to make proactive—rather than reactive—business decisions. Manufacturers can identify emerging trends to make product changes quickly and forecast demand with greater accuracy. In addition to optimized inventory, analytics models can also help detect defects early, improving quality management and even recall management. This is important in view of the increasing number of recalls in recent years, such as the Fiat Chrysler recall involving millions of vehicles with cruise control issues. There was also an incident whereby Honda recalled a number of cars due to defective Takata airbags.

External Data Storage in a Data Lake Environment

There are more customer touchpoints on social media than there are footfalls at the actual dealership. So if you want to know how your brand is perceived, you must be monitoring social media due to the much larger data set. There are numerous tools available for social listening, such as NLP and Machine Learning. These programs can monitor online conversations and incorporate this data into your Data Lake. In turn, you’ll have the data you need to build an extremely detailed and comprehensive picture of customer feedback, satisfaction levels, and the overall perception of your company.

With current technology, automakers and allied services can now quickly handle complaints, while also monitoring the effectiveness of their campaigns. Companies can also see how competitor models are faring vis a vie their own data set. Collecting data from multiple touchpoints provides greater insight into customer behavior in more natural settings than one might find with self-reporting. These insights can provide direction for future changes and improvements. Futuristic trends indicate that autonomous and self-driving vehicles could be the next big change in the automotive industry.

Social content is unstructured and up until relatively recently, it has been a largely untapped source of external data. However, Data Lakes serve as a storage system that stows information in a way that allows for valuable data analysis. Data Lakes utilize Artificial Intelligence and Machine Learning technology to tag this content with metadata, while Big Data tools make sense of it all.

Leveraging Data Lakes for Customer-Specific Data

The “Amazon effect” has changed how customers expect to be treated. This trend has percolated down into every industry, and automakers now realize that it’s a whole new world when it comes to marketing. A car may still be a status symbol, but customers also have unique preferences. Innovative technology, efficiency, and service are now some of the top factors affecting consumers’ decisions.

A customer’s journey rarely involves a single contact with an immediate purchase. That customer experience typically involves multiple touchpoints. Raw customer data can be structured in the form of purchase data, but it could also be unstructured, such as the data gathered from social media, email, reviews, and call center logs. There is so much customer data available, including the large streams of data originating from real-time operation modules. The centralized data storage capabilities of data lakes pull all this information together, regardless of structure, offering a single point of truth.

This data can be used to market to customers by targeting them with compelling and personalized promotions. It can also help in better customer segmentation and understanding the needs of these segments. The traditional auto dealer must be given the capability to bring in more innovative sales processes before they fall behind.

The relationship with the customer continues beyond the completion of the sale. Companies can continue to deliver a personalized experience by maintaining robust customer records for warranty information, repair and maintenance history, and even vehicle history reports. Data Lakes and Predictive Analytics could help auto shops to determine when it may be a good time to notify customers who are due for services such as an oil change, tire rotation, or other routine maintenance. Dealerships will also have the ability to examine customer data more broadly to predict exactly when customers will start searching for a new car or which customers may be interested in a vehicle trade-in.

Car brands need to think beyond the actual car, or you risk becoming a mere hardware supplier while tech-minded carmakers perfect the customer experience with vehicles that perfectly blend technology, comfort, and safety. Think of your iPhone and the capabilities it has and the rate at which you see new features. To remain innovative and competitive, key players in the automotive industry will need to move beyond their normative processes to incorporate emerging technology. In the coming decade, it’s likely that data will drive a massive tech revolution in the auto industry.

While data lakes have key benefits for the automotive industry, their powerful storage capabilities and connection to business intelligence tools make them relevant to many industries. Sertics’ SaaS platform helps democratize data-driven insights by providing access to key business users without the need for data scientists. To experience the power of Data Lakes, data visualization and Predictive Analytics, contact the Sertics team today.

Shane Long is the President of Sertics and SevenTablets, two companies that drive impactful digital innovation for their clients. Shane is an enthusiastic leader with more than 20 years of experience in hardware and software development, SaaS products and mobile technology. He holds an engineering degree from Texas A&M University, as well as a master's in finance from Southern Methodist University.