Data analytics is impacting virtually all industries the world over and the automobile industry is no exception. Inexorably, all business activity is becoming data driven and this is where analytics comes in. According to NASSCOM, the analytics market will double to $2.3 billion by the end of 2017/18, up from approximately $1.5 billion in 2015 and the Indian analytics market will grow at a CAGR of nearly 23 per cent during 2014-2019.

All enterprises and businesses - healthcare, manufacturing, infrastructure, and education, automotive - are collecting more personal information about consumers, enabling them to gain not just richer but also actionable consumer insights. Data-driven industries such as banking and telecommunications may integrate data analytics in their operations faster while others like automobiles - one of the current sweet spots in India and the global automobile manufacturing scenario - can also effectively use data analytics.

As the Internet of Things (IoT) evolves, this network of physical objects - devices, vehicles, buildings and other items which are embedded with electronics, software, sensors, and network connectivity, enabling them objects to collect and exchange data - will truly create Big Data. And without analytics, Big Data isn't of much use.

The IoT phenomenon is now underway, though in India's case, real-time connectivity may be a challenge because of power supply issues and pervasive broadband reach. There is also the question of acquiring talent in data analytics; a key challenge for any industry and their success will hinge on getting such talent.

Take the scenario of the automobile industry - the bill of materials for assembly of a vehicle is critical to its business operations. An average vehicle contains 25,000 or more components and the base parts, sub-assemblies, subsystems and the final vehicle are fundamentally inter-related. From a data perspective, these relationships are hierarchical and can be complex to represent accurately and conveniently. Additional complexity comes from parts reusable across multiple vehicle models and option combinations that "look across" is also important to understand. Without data analytics, this task is nearly impossible to understand in a competitively useful manner.

Analytics helps convert consumer data into a corporate asset to know preferences and automobile usage data to increase customer acquisition and retention. It can help the industry gain design efficiencies, in procurement and smarter supply chain management, tweak production lines for continuous improvements, distribution and marketing and in all aspects of automobile servicing.

For instance, each part of an automobile can be designed for improved looks and performance. Model-specific performance over its lifetime can be gauged by actual data collected on driving and passenger comfort, safety, engine performance, servicing history and mileage - for every aspect of an automobile, its small and big components and their individual performance across all parameters. This, in turn, gives automobile manufacturers real-world data for designing and improving upon newer models.


For instance, performance analysis using analytics gives automobile manufacturers insights that they would otherwise not have easy access to. At the shop floor level, analytics can help improve equipment utilisation and availability across the enterprise and help optimise factory capacity at the least cost and each independent process can be fine-tuned to maximise output and avert failures. This can also help in smarter inventory management and supply planning.


Additionally, take the lifecycle performance of an automobile. Warranty Early Warning solutions provide predictive analytics of emerging warranty issues throughout the vehicle's lifecycle. This solution's engine leverages a set of predictive algorithms that detect reliability issues and enable root cause analysis for achieving massive reductions in the detection-to-correction cycle time. This is but just one example of the use of advanced data analytics in the automobile industry.


Wider adoption of telematics and an explosion of on-board instrumentation and sensors is now prevalent, more so in the consumer vehicles. This means that automobile manufacturers need more expertise in more kinds of activities that finally result in the production of an automobile; without analytics, the picture with regard to collating data and making sense of it would be a maddening one.

Real-life instances of analytics being used profitably can be gleaned through these two examples.

Ford integrated its vehicle plant data and purchasing data to allow it to build a vehicle-level view of profitability that incorporates the impact of both raw materials and non-standard incremental features for each stock keeping unit (SKU). Ford extracts real-time vehicle profitability details and delivers it to information stakeholders whenever needed.

In the case of Volvo, analytics has apart from its impact on vehicle design and quality, significantly transformed decision-making processes enterprise-wide. All queries are answered by starting with an exploration of relevant data, and each assumption tested against the data. Data is used to judge the scope, prioritise and scale the response in each problem response process. This has helped the company's employees to focus on things that directly affect and impact customer experience.

Globalisation may create islands of information constraining productivity but Big Data can put these together for obvious benefits. With standardisation and modular architecture on the rise and an ever-increasing use of sensors to gauge, correct and perform various functions, the ability of an automobile manufacturer to optimise efficiencies at each level - from implementing regulatory compliance with regard to emissions, environmental regulations, fuel economy, emissions, recycling and other guidelines to selling and servicing its vehicles is being impacted by analytics.