Big Data in the Automotive Industry

Technological advancements and capabilities have brought many improvements for businesses around the globe, improving the way they interact with clients and operate in streamlined, cost-efficient ways that make business processes and decisions more effective.

When we look at businesses now compared to five years ago, we notice significant advancements and initiatives to bridge the gap between data intelligence and business operations for many organizations. For example, big data provides valuable product and supplier information that businesses can use to ensure their suppliers are in economically feasible locations, have the necessary car components and materials available, can produce on-time deliveries, and charge industry-standard prices.

With the help of big data, key players in the auto industry gain significant amounts of intelligence, transparency, and flexibility to manage the risks involved in the competitive auto industry. As a result, car dealerships, manufacturers, and businesses involved in the automotive supply chain can create actionable strategies and preventative measures to remain scalable and agile, even when there are global pressures.

A great example of the effectiveness of big data in the automotive industry is the way successful key players have reacted and responded to the supply chain challenges brought on by the ongoing pandemic. Global business shutdowns have significantly affected supply chains and the availability of car components and materials. Customers have also shifted their consumer mindsets and purchase behaviours.

Using big data, automakers can better expect changes in supply chain management and consumer expectations to adapt to these global challenges. For example, supply chain delays caused component shortages for many car manufacturers worldwide. As a result, key players in the industry had to analyze customer data to anticipate production delays and consumer demand. They also had to focus on changing certain processes to ensure operations were optimized as much as possible during these delays.

Another example is the increasing importance of sustainability and zero-emission vehicles. As consumers become more conscious of environmental impacts from their purchases, the government poses regulations towards a net zero-emissions auto industry. Automakers leverage big data to analyze what consumers expect from the industry from a sustainability perspective and develop initiatives aligned with customer values. They also leverage big data to improve manufacturing and design processes to ensure vehicles are more reliable and more environmentally friendly.

Big data has many use cases in the automotive industry and is increasingly relevant in the daily operations of automakers. As the industry becomes more data-driven and technology-reliant, large and complex data sets serve as the backbone of many strategic business decisions that help automakers grow their business successfully.

What is big data?

Big data is a term that refers to large, fast, and complex sets of data and information that are very difficult to process using traditional methods of analysis. While data has been known and relied upon by engineers, analysts, and many other professions for a long time, the concept of big data has become more relevant in the last 20 years.

Big data has five dimensions that define it: volume, velocity, variety, variability, and veracity.

Automakers and businesses in any industry collect large amounts of information constantly. They have data about their customers, operational processes, vendor relationships, shipping and production schedules, finances, and more. These extensive sets of data can come from various channels and are stored in secure areas, such as the cloud, to ensure the safe access and authorization of the information.

Velocity refers to the speed at which organizations collect and handle big data. Since data can come from multiple channels, such as social media, connected cars, customer management tools, vendors, supply chain management processes, and more, at a fast pace, automakers must ensure they have the resources and tools to handle the data quickly. As a result, automakers implement things like sensors to collect, process, and relay information in real-time to automakers.

Car dealerships, manufacturers, and other key players in the industry receive data in all types of formats. For example, they collect customer contact information, email lists, material prices, vendor lists, shipping information, manufacturing costs, and production statuses. Big data can be structured and presented in traditional databases, such as stock information, or unstructured formats, such as documents, videos, and financial transactions.

Variability refers to how volatile and unpredictable big data can be. Since data comes from all channels and quickly, it can be hard to determine the exact changes in the information. Businesses need to know how to interpret big data, draw trends in the database, and manage peaks and valleys triggered by seasonal or event changes.

Veracity refers to the quality of big data. It can be challenging to track the relevance of data across systems and match useful information. Data analysts specialize in connecting data and finding relationships and trends that businesses can use to make strategic decisions and forecasts. They also regularly clean up and transform data into readable formats that other people can use.

How is big data being collected?

Big data is very complex and collected through a variety of channels. They store transactional data, supply chains, social media, geolocation, loyalty cards, and online marketing are all ways organizations in the auto industry can collect big data from their customers, vendors, and partners.

Automakers can collect big data by directly or directly tracking it from customers or acquiring it from other companies. For example, customer data on loyalty cards, consumer behaviour, and car location can be tracked by monitoring social media information, asking for contact information, monitoring website browsing activities, and using car sensors and GPS systems. Most companies will directly ask the customers for some of their basic information at the beginning of their car purchasing journey.

Cookies, apps, and third-party trackers are also ways organizations pull big data on their customers. For example, cookies track how their target customers research cars and interact with various dealerships and the websites they access.

Satellite imagery is one of the ways organizations can track shipping status, customer location, and warehouse location. This is especially helpful in ensuring on-time delivery of products throughout the supply chain and better targeting customer sales and marketing.

What are different types of big data?

There are three types of big data: structured data, unstructured data, and semi-structured data.

Structured data is highly organized and has clear parameters, including information that can be easily entered and grouped into rows and columns on a spreadsheet. The data is already in tangible numbers, making it easy for analysts to compile, sort, and clean. Examples of structured data include customer age, billing information, address, expenditures, card numbers, production hours, and payroll.

Unstructured data refers to datasets that lack structure. Analysts can find it challenging to process and analyze unstructured data since it’s not as easily understood and organized as structured data. Most of the data that an organization collects are unstructured data. Emails, phone calls, messages, invoices, and social media posts are all considered unstructured data types. Analysts must process unstructured data to make it readable and understandable. Otherwise, other teams and departments can’t interpret the data to make strategic business decisions or improve operations, supply chain management, and customer service.

Semi-structured data combines structured and unstructured data and has no relational database structure. In most cases, semi-structured data refers to unstructured data that includes metadata or semantic tags that enable analysis. Metadata is a portion of the file that contains information about the contents of a file, such as author, file size, address, and purpose. It helps categorize semi-structured data so analysts can easily search and interpret the information. Examples of semi-structured data include email, HTML, zipped files, and web pages. Analysts can use semi-structured data to integrate information from multiple sources or exchange information between systems.

How will big data improve the auto industry?

Automakers use big data to provide better quality cars and customer service. Data is important for the auto industry because it relies on the collection, transformation, and analysis of information to ensure that the supply chain is running smoothly. Big data provides more opportunities for automakers to make informed decisions about current and future operational needs.

Many car manufacturers are now investing in big data collection and analysis to find new opportunities, overcome challenges, and become more competitive in the market. For example, the collection and interpretation of big data help automakers define customer segments. This helps them better understand who their customers are and how they can align operations with their customers. As a result, they can analyze the effectiveness of marketing investments and create a more effective approach to targeting the audience.