business intelligence automotive industry

In this blog, we talk about macro trends shaping the overall automobile sector across commercial and passenger vehicle segments. In the process, we highlight the role of BI (business intelligence) and analytics to make more data-driven decisions across the product lifecycle from suppliers and manufacturing all the way to the end-user.  

The pace of change in the automotive industry is tremendous today and is being influenced by various factors. Electric Vehicles (EVs) are now a reality and we’re seeing rapid adoption of EVs across passenger and commercial segments.  

Vehicles of today are truly “connected” and it is now possible to pull data about most parts in a car – be it tire pressure, the status of the fuel system, or battery health – with just a few clicks. While self-driving cars are definitely several years away, cars are now loaded with semi-autonomous features, going much beyond cruise control. Cars can park by themselves, listen to voice commands, and follow instructions.   

We could go on and on, but this blog is not about the future of cars or electric vehicles. It is, rather, about the use of business intelligence (BI) and analytics in the automotive industry, and how the connected vehicle is taking the usage of BI to another level. This is primarily because there is now access to massive amounts of data from in-car parts.  

Broadly speaking, the adoption of BI in the automotive industry can be split into two broad categories:  

  • In-car Analytics: Using data from within the car (or any vehicle) to make various decisions. For instance, car manufacturers are now analysing data about the life span of wipers by calculating the typical number of cycles in over four to five years. Car OEMs are choosing the material in wipers, based on this data. If the car is being used in a geography that has more rain, the wiper is modified accordingly.  
  • Strategic and Operational Analytics: Needless to add, data is being used to make decisions around consumers, which tires to use, which colour combinations to offer, marketing campaign planning, production planning, inventory and supply chain management, etc.  

There is no lack of input data in the automotive industry  

The use of technology to meet the Future of Mobility goals has transformed the face of automobiles, especially in the passenger car segment. There are around 100 million lines of software code in a  modern car today, and this is expected to go up to 300 million lines of code by 2030. Compare this to a passenger plane that has only around 15 million lines! These codes enhance customer experience and empower OEMs with data that provide insights. This helps OEMs make data-driven decisions around product and service offerings.  

Additionally, it is also important to note that there are the Euro norms that require companies to reduce emissions in petrol and diesel vehicles. Key decisions around some of these requirements are being made with the use of BI.  

The introduction of electric vehicles, the cost of fuel, and other factors such as economic ups and downs, the Covid-19 pandemic, and now the Russia-Ukraine war – all have a bearing on the auto industry.  

Automobile manufacturers find it difficult to anticipate demand, manage inventories, deal with supply chain challenges, optimise production and protect investments. Managing suppliers on the one hand and the distribution channels on the other have to be finely balanced to maximise profits and resource utilisation. 

Although there has been a resurgence of demand for vehicles since the pandemic, auto businesses need greater insights into customer behavior, visibility into their supply chain, and improvements in their operations to increase their profitability.  

One of our BI experts at Merit, who recently worked on a business intelligence solution within the automotive industry says, “Decision-makers are looking for insights across several layers. One end of the spectrum is to use BI to manage uncertainty, identify opportunities, spot trends, and overall keep pace with change. The second BI layer revolves around using in-car data to drive decisions around product specifications and consumer preferences. The third is to use data to drive operational decisions – improve processes, get better at quality management, cut costs, supply chain planning, etc. It is truly an exciting time for this sector and the only way out is to use data to drive decisions.”  

Business Intelligence tools enable businesses to gain these insights by processing high-volume internal and external data sets. This has increased the demand for big data, as indicated by a market research report projecting the big data market in the automotive industry to grow from USD 3,607.47 million in 2020 to USD 8,929.37 million by 2026 at a CAGR of 16.81%.  

Use Cases for Business Intelligence in the Automotive Industry 

Data from enterprise systems and IoT devices, enriched by external data, can help automotive companies and auto industry intelligence firms generate reports and run analytics. Some of the areas where data-driven intelligence can help the automotive industry include: 

  • Spotting industry shifts: One area where business intelligence and analytics are coming in really handy is in spotting macros trends shaping the automotive industry. For example, in the commercial vehicle segment, traditionally, companies lease or own fleets of vehicles to manage logistics. This has now shifted to subscription-like services, where companies are partnering with fleet operators on a pay-per-month or pay-per-drive business model. It is important for OEMs (Original Equipment Manufacturers) and fleet operators to understand how these shifts will affect their own product planning and strategy.  
  • In-Car Features: Data is now enabling several decisions around in-car features. This includes everything from design aesthetics to sophisticated features like what distance your car will run on a flat tire. OEMs are also partnering with leading analytics companies to offer real-time data to consumers on fuel mileage, car statistics, next maintenance due date, etc.  
  • Marketing Automation Efforts: Automotive brands, both in the commercial and passenger segment, are using data to make various marketing decisions. Marketers are able to segment their entire customer base into several buckets — planning customised campaigns for each segment. Different features are being marketed to different segments, while various digital marketing channels are being utilised as well. Most importantly, by using BI, marketing teams are able to automate workflows by persona, with little to no manual intervention.  
  • Improved Operations from Shop Floor to Top Floor: BI can help with planned downtime for machine maintenance (on the shop floor) to minimise disruptions to production schedules. The procurement process can be improved to meet forecasted demand based on past and projected data. This can also help reduce scrap and wastage, thereby improving cost management. Demand prediction can help to align production goals for greater cost-efficiency. There is also end-to-end data visibility for executive leaders, who have a finger on the pulse of all operational data across the value chain.  

How Merit Data and Technology can help with your Automotive BI Journey  

At Merit, we are specialists in collecting high-volume, accurate industry-specific data, at scale and speed. Driven by our proprietary data engine Data Xtractor, Merit enables rapid data collection, transformation, and ingestion from a diverse range of disparate online sources. 

Additionally, our data science and BI teams are specialists in several BI tools and technologies, and in partnership with our automotive domain experts, we’re able to provide context-specific advice to a business situation your organisation is in. 

Our data engineers make it easy for industry intelligence firms to automate the following processes: 

  • Harvest, aggregate, and manage data, images, and narrative content from thousands of web sources 
  • Handle data in various formats – HTML, APIs, JSON, Excel, PDFs, PowerPoint 
  • Refine the raw data into a consistent and searchable format 
  • Enhance data with additional research to deliver value-added insights 
  • Setup the underlying data and information flow using the right data lake and data warehousing architecture

To know more about our data practice, visit:

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