'Analytics has enormous untapped potential in automotive industry'

What is the value proposition for Analytics in automotive? What are the specific impact areas? 

Expertise in all the automotive fields like engine management, automotive safety systems like antilock braking, electronic stability, airbag, driver assistance systems, infotainment, telematics systems and automotive diagnostics is very crucial in analyzing data in the automotive domain. It is important to leverage these competencies when focusing on "Engineering Analytics".

Niche areas need to be identified, since engineering analytics brings in huge benefits. Analytics needs to be applied in house and with automotive OEMs for engineering and manufacturing. Initially the focus has been only on engineering analytics, but now the focus is soon expanding as we see that many use cases require end to end analysis of the value chain. In the context of the technology trend IoT (Internet of Things), where everything will be connected, we envisage more and more end to end solutions. Analytics will be part of the solution offering, rather than seen as a separate service. The building of such systems is already in progress.

What type of Analytics technologies are being used in the automotive space?
About data and algorithms, all problems could be grouped into few classical solutions. It is the domain knowledge that differentiates real insights that add business value. For example JD Power Initial Quality Study 2013 report says that majority of problems experienced by owners with their new vehicle in the first 90 days of ownership are design-related rather than manufacturing defects. With the use of social media analytics, one could validate this and connect with engineering design. Today, we are able to narrow down customer sentiments to feature and function level. We are also able to trace field quality issues to a specific slice in the product life cycle.

Analytics is now part of a system solution. There is in house expertise and it is now possible to develop complete products with electrical hardware, mechanical engineering teams and manufacturing. With the help of IT and ITES teams, it is possible to provide elegant solutions for engineering and manufacturing organizations. End to end integrated solutions from sensor network, M2M (machine to machine) systems to aggregate, data collection, validation and integrated analytics with existing IT systems are now offered.

What are the challenges involved in using analytics in the automotive space? What is the response you are getting in the domestic market?
The industry is rapidly evolving. Lot of interesting use cases, visualizations and business benefits are published. There is lot of expectation on Big Data along with IoT (Internet of Things). Open source adoption is gaining momentum. But strategy roadmap and systematic adoption in large organizations is still lacking. Overall it is an opportunity space.

That said, one of the major challenges is to convince middle and lower management that analytics helps them. Many of them are either skeptical due to deep conviction of their own established spreadsheet based methods or they see a threat of being seen as not effective, in case big improvements happen as a result of analytics. Coming out with good use cases is a team work. It involves collaboration between the analytics team, domain experts, actual users and management. Even if one of the stakeholders is not engaged, it could affect the speed or the extent of potential benefit itself

Do you think analytics be the game changer for the automotive industry?
Analytics and big data have enormous and mostly as-yet-untapped potential in almost all industries, including automotive. The amount of data generated/captured these days is enormous. So having big data itself is not sufficient; one needs data analytics to be able to make sense of it and put it to use.