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Analytics in the driver’s seat at Ford
Behind the scenes with Ford Chief Data and Analytics Officer Paul Ballew
By Anne-Lindsay Beall, SAS Insights Editor
Ford Motor Co. was the first to create a moving automobile assembly line and the first to make cars affordable to the masses. No wonder it’s an American icon. Founder Henry Ford was a pioneer and an innovator, a tradition still carried on today: In 2014, Ford was the first major automaker to appoint a chief data and analytics officer.
Paul Ballew leads Ford’s global data and analytics teams, which includes the development of new capabilities for connectivity and smart mobility, and projects involving vehicle-to-vehicle communications and the development of autonomous vehicles. Those initiatives are just one part of Ballew’s busy schedule, but he made time recently for a conversation about the company’s approach to analytics.
We look at cognition or adaptive learning environments as the next wave of analytics, driving greater speed and more precise insights.
Chief Data and Analytics Officer
Ford Motor Company
How is Ford using analytics?
Ballew: We’re leveraging data and analytics on both sides of our business model. We’re addressing the fundamentals, which include infrastructure, data management, and the integration of those assets with the analytics community. Business adoption of analytics – and integration and embedding analytics with business partners – is important.
Another thing we’re focusing a lot of time and attention on is what’s behind the next corner, as we’re all trying to provide greater precision and timeliness in our insights. There’s a lot of buzz in the industry – everyone wanting to talk deep learning, alternative analytic methods, unstructured data. But for us, it’s more about where does the journey take us in terms of deriving business value.
Internet of things is a hot topic in your industry. What can you tell us about what you’re doing there?
Ballew: When you talk about IoT, that’s a subset of a lot of things we are doing in manufacturing and connected vehicles. For example, we’re working with Amazon and the IoT platform Wink to grant Ford owners access to their connected-home devices from their cars through Ford SYNC.
Voice commands issued at home through Amazon Echo would allow Ford owners to remotely lock their vehicles, program a time to start the engine, or check fuel levels. And from your car, you could adjust lights, change your thermostat setting and access any other home system connected to the IoT – all from behind the wheel.
Today almost every business seeks to develop an analytics culture, but getting there is not necessarily easy. How has Ford used analytics for competitive advantage?
Ballew: This to me is the biggest question of all – how do you achieve business value, and how are you integrating yourself with the business partners?
Our approach since day one has been not only to embed resources, but to take it to the next level by working together with our business partners to continuously understand their issues, and bringing them through our problem formulation activities.
We view ourselves as internal consultants, and any good consultant has to understand the customer and the opportunity and then work with the customer through adoption, measurement and tracking, and assessing on the back end, from a continuous learning standpoint. So we’ve put training as one of the four pillars in our strategy, and we approach it holistically, through all of the dimensions I just mentioned. It’s at the top of our list, along with talent development, every day.
We still have technical opportunities, but increasingly we’re going into the realm of the human side. And that’s the ability to have talent that’s smart and nice and inquisitive, and on the flip side, having business partners who are tightly engaged with you to catch whatever you throw at them, and the ability to throw things back.
How many people on your analytics team?
Ballew: We have 600 direct resources and another 400 external, so 1,000 team members total. We’ve more than doubled in size in the last 18 months. That commitment speaks to the company’s focus on both the core and emerging business as Ford expands its business to be both an auto and mobility company. We have a very large and complex core business around designing, engineering, manufacturing and distributing vehicles, and we have emerging businesses like mobility, connectivity and autonomous vehicles. And those businesses require data and analytics even more than our core businesses, so we cover both sides of the ledger.
You mentioned training. What kind of training do you provide to ensure an analytics culture?
Ballew: It involves some technical training but also involves training around principles of critical thinking. Our problem formulation activities are centered around understanding the issue, the business processes, to structure the ability to assess and measure.
Is the popularity of open source code a help or a hindrance to you?
Ballew: Our approach has always been to be very flexible and agile. We like open source environments and open source tools. That’s the way the world has gone, so we’re less wedded to our infrastructure environment and our analytics platforms. We believe that’s where the world is going, and it’s clearly accelerating.
How important is cognitive computing to your strategy?
Ballew: A few months back, we formed a Center of Excellence group on machine learning, and it’s looking at cognition. For us, the applications are broad-based. We’re already using deep learning and machine learning methods throughout the organization – not just for autonomous vehicles but also to support our credit decision making, our sales and marketing interactions with customers, and for other parts of our product organization. So we don’t see it as something that’s apart from the whole, but as an ongoing evolution of analytics competencies.
We look at cognition or adaptive learning environments as the next wave of analytics, driving greater speed and more precise insights. The journey I’ve been on for three decades comes down to having an actual insight, providing greater precision and timeliness. And that’s what machine learning lets you do – it lets you deal with increasingly complex decisions at greater speed.
How are you using analytics to better understand your customers?
Ballew: We use it every day. One of the largest groups we support is Marketing Sales and Service. Our focus there is not just on improving traditional methods, but how analytics can help deliver a better experience for our customers by knowing them in a meaningful way.