To find the best way to introduce big data analytics technologies into its research and development processes, Honda R&D wanted to work with a technology partner that could offer a truly comprehensive service.

Kyoka Nakagawa comments: “IBM was the right choice of partner for two important reasons. First, IBM offers a very broad range of big data analytics capabilities, including data mining, text analytics and visualization—so we were able to get all the tools we needed from a single vendor. Second, IBM had the skills and experience to guide us all the way through our big data journey, from consultation through proof-of-concept to final realization.”

Honda R&D’s big data analytics environment is based on IBM® SPSS® Modeler, IBM Watson™ Content Analytics, and IBM Predictive Maintenance and Quality (PMQ). Kyoka Nakagawa’s role is to act as a networking hub for these technologies, helping to set up proof-of-concepts, organize training courses, and encourage engineers to share their knowledge, experience and data.

Kyoka Nakagawa comments: “The data mining training courses have been very successful—IBM SPSS Modeler has quickly become a popular tool throughout the business. More than 100 engineers have now completed the training, and many of them use SPSS regularly in their work.

“SPSS Modeler is very good for organizing raw data into usable data-sets, so that it can be analyzed easily. It is also very easy to use for complex analyses. Another valuable feature is the ability to monitor users and see how they are interacting with the tool. So if someone is struggling to manage their data effectively, colleagues can give them some extra help.”

Honda R&D uses IBM Watson Content Analytics for text mining—giving researchers near-instant insight into vast stores of documents and other textual data. For example, the JD Power Initial Quality Studies and Honda R&D’s in-house voice-of-customer studies are very valuable sources of information on automobile quality and reliability over time. In the US, the National Highway Traffic Safety Authority (NHTSA) also provides a rich source of insight into consumers’ problems and safety concerns.

Kyoka Nakagawa gives an example: “We recently had a meeting where an executive asked a question about a feature of one of our cars. We logged into Watson Content Analytics, analyzed over a million records in the NHTSA data-set, and within 10 minutes we had found three or four examples of relevant feedback from customers. This is the kind of analysis that would be almost impossible to perform manually.”

The IBM Watson Content Analytics solution runs on IBM’s flexible cloud platform, in a shared virtual server environment located in Tokyo. Honda welcomed the versatility of IBM Cloud for building and rolling out the Watson Content Analytics environment for users in the company’s Big Data Initiative team, and its ability to scale.

“From the perspective of our line-of-business users, it was not important whether we ran Watson Content Analytics on premise or in the cloud,” says Kyoka Nakagawa. “What really mattered to them was the speed of implementation—and IBM Cloud enabled us to get the solution up and running much more quickly than would have been possible with an on-premise solution.

“In addition, the computational requirements for text-mining with Watson Content Analytics depend on how much content we provide into the custom dictionary.

“Since we are constantly developing our dictionary while we refine our text-mining capabilities, it is vital to have a flexible cloud environment.”

The scalability of IBM Cloud infrastructure also means that it is easy to add new users, so if other departments decide to adopt IBM Watson Content Analytics in the future, Honda will be able to support them seamlessly.

IBM Predictive Maintenance and Quality is designed to help organizations monitor their assets and processes and predict asset failure or quality issues. Honda R&D has been piloting this technology in market quality warranty analysis, and the initial results are promising.

“We have been very impressed with the visualization capabilities of IBM Predictive Maintenance and Quality,” says Kyoka Nakagawa.

“PMQ serves as an analytics development environment to enable our researchers to explore where analytic insights can help identify quality or manufacturing asset issues in a sandbox environment. PMQ also serves as a complete analytics solution that operationalizes analytics with continuous process event data ingestion from our operations, where we can upload data and view it easily in intuitive dashboards.”

Honda R&D’s training programs for big data analytics have made a big contribution to creating a more open and collaborative culture among the company’s engineering teams.

“I call it the ‘Big Data Friends Club’,” says Kyoka Nakagawa. “Bringing our engineers together has been a benefit in itself, because they are more likely to share their data and learn from each other. For example, our chassis team was working on a project to study deterioration in exhaust pipes, and they needed some data about gasoline. Through the Big Data Friends Club, they discovered that our fuel economy team already had the data they needed – so it saved a lot of effort and expense.”

She adds: “Learning about big data analytics has also helped some of our engineers think outside the box. Instead of simply analyzing the parameters that they think are important, they can use data mining techniques to uncover patterns and clues that they might never have thought about.

“For example, we have millions of customers around the world, and driving habits in different countries differ in ways that are not always easy to predict.

“Recently, we were discussing where to locate an ‘engine start’ button in our vehicles. From analyzing the NHTSA records, we found that several drivers in the US had complained that they had accidentally pressed the engine start button with their knees.

“This was a big surprise for us, because it is only possible if you put the car into cruise control, take your feet off the pedals, and fold your legs up on the seat. We had never imagined that people would actually choose to drive like this—so it was a valuable insight for our product design teams.”

Another important advantage of Honda R&D’s approach is that it has shown the company’s engineers the value of big data analytics in understanding how cars and drivers behave in real-world conditions, and identifying new engineering projects.

For example, the company is using vehicle sensor data to monitor drivers’ steering manoeuvers and gain a better understanding of customers’ preferences. This real-world insight feeds into engineering projects that aim to design a steering experience that can be personalized for each driver.

Kyoka Nakagawa concludes: “IBM Analytics is helping Honda R&D’s engineers harness big data to explore valuable new areas of research—and ultimately design better, smarter, safer automobiles.”