Gil Gur Arie

Meet the Mind

Vol. 15 // 2020

Images courtesy of Ford Motor Company

Join us as we talk to Gil Gur Arie, Chief Data and Analytics Officer (GDI&A), Ford Motor Company

CT: Gil, congratulations on joining Ford as the company’s Chief Data & Analytics Officer. Please tell us a little bit about your background and the journey that got you to your present position?

GGA: Before I came to Ford, I was part of the Israeli defense force’s intelligence corps where I used big data and AI for the purposes of counter terrorism and homeland security. Though to the casual observer this might seem like a very different environment to Ford, I actually see a lot of resemblance from a data standpoint. In both cases the objective is the same: using data to drive value and as a building block of strategy creation. Making sense of the vast volumes and variety to accomplish known goals and reveal hidden opportunities.

CT: Given your experience, you are an expert at “sense making” from large quantities of data. How do you think this skill will be valuable when applied to the data Ford has access to? Will analytics be used to improve the customer experience? Improve manufacturing efficiency?

GGA: Every industry is now part of what we might call “the age of big data”. No matter what you do, you’re dealing with huge volumes of data, and a large variety of data signals and types. At Ford we’re looking to leverage it to achieve what we call “data superiority”. That’s the point at which we can use data to drive value not only in manufacturing and the customer experience, but also to improve dealership effectiveness, sales and marketing strategies, product development and Ford Credit. We aspire to use data to enhance the entire customer journey; from a customer interested in purchasing a vehicle, to entering the dealership, applying for credit, and improving the ongoing relationship with the dealer afterwards.

CT: Ford is one of the most iconic American brands of the past century. But in any organization with established traditions, change can be difficult to engineer. What challenges have you encountered as you seek to implement a data-first methodology?

GGA: Every other organization that I’m aware of that has been around for so long naturally finds itself with a tension between the existing ways of doing things and the new, the rapid, and the agile. Ford is now progressing with a new plan with our new CEO, Jim Farley, emphasizing the need for modernization. As a company, we’re making a non-linear leap to “modernize everywhere”. We’re pushing the boundaries of the enterprise when it comes to using data and AI, while at the same time decommissioning some of the heavy burdens we’ve carried forward until this point. For example, we can train a machine with artificial intelligence to spot the tiniest flaws on the assembly line. We also use an audio device and analytics to assure you get the right ‘click’ that means you’ve correctly inserted an important piece of cable into a vehicle connector. Setting these new capabilities on top of a legacy system foundation can be challenging, but we have a solid approach that allows us to “productionalize” the new while rebuilding or replacing the dated components.

“As a company, we’re making a non-linear leap to ‘modernize everywhere.’ We’re pushing the boundaries of the enterprise when it comes to using data and AI…”

CT: Even after they decide to implement AI, large companies are often split on the question of whether a single, centralized analytics organization should own data efforts, or whether this function should be distributed. In the one instance you might have conformity, while in the other you get independence and speed. What approach are you taking at Ford, and why?

GGA: There is a very fine balance here, which is why Ford has decided to adopt a hybrid approach. While we have my team driving a centralized strategy, we also practice and implement a democratized platform for employees where they can leverage data themselves within their skills teams. For example, we have built what we call the Mach-1ML platform that can be used by teams to build their own machine learning solutions. In many cases, this has already created efficiencies and brought times down from a few months to a few weeks, and in some cases a few days. We already have hundreds of different active users outside of my data and analytics centralized team.

CT: Please paint for us a picture of the data-enabled automobile of the future. How will data make the automotive experience richer and more enjoyable?

GGA: Firstly, it’s important to say that we’re no longer talking about the “automobile of the future.” The new F-150, the Mustang Mach-E, and the new Ford Bronco are really the most important vehicle launches in Ford’s recent history as they represent the next level of vehicle connectivity. Each of these vehicles will be equipped with our in-vehicle SYNC 4 technology that enables customers to make use of AI-driven technologies like smart navigation and voice recognition, while the vehicles themselves will keep customers out in front with regular bumper-to-bumper overthe-air updates. They are the cars of the future but they’re already here.

As a company, Ford has decided to place big bets on autonomous, electrification, and connected vehicle technology. For all of these, data is really the fuel to make the end goal and that means we need to have the right capabilities to understand how our vehicles are being used so that we can make the customer experience better. We also continue to expand the roster of tech companies and startups that we partner with to enhance our internal processes and connected vehicle technologies.

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