Thierry Martin, head of enterprise data and analytics at Toyota Motor Europe, is a man of varied talents. As he talks to Computer Weekly on a video call, it’s possible to make out the fine lines of some detailed illustrations on the shelves behind him.
“My way of relaxing is sketching,” says Martin, referring to the art. “That’s how I can go from a high-intensity brain, where I’m focused on data, and then move to a state where I can let go and let my hand draw. It’s enjoyable.”
More than that, Martin believes there are parallels you can draw between a high-quality illustration and an effective enterprise data platform: “They’re both about aesthetics, simplicity and extracting the essence of what you want to achieve.”
In his role at Toyota Europe, Martin has spent the past few years building the company’s data stack. An engineer by trade, he became interested in technology and developed his data skills with the firm. With almost 25 years in the business, Martin says Toyota Europe is a great place to work with a strong sense of belonging and identity.
“Company values are very strong,” he says. “If you look for the Toyota Way and our values, there is respect for people, teamwork and so on. It’s a company where you can grow and find your way. But what you also must do is to carry the Toyota jacket.”
While Martin says that phrase is a figure of speech, it also rings true. Employees wear personal Toyota jackets when they go to the company’s manufacturing facilities. He says that sometimes staff even wear these jackets in the office: “So, there is a strong identification with the company.”
Moving into data
Martin’s career journey at Toyota Europe has taken some intriguing twists and turns. He recognises that his early roles at the company had nothing to do with data – and certainly not artificial intelligence (AI). In these initial positions with the firm, Martin was firmly focused on engineering. He spent 15 years designing cars before turning towards technology.
“Data and AI came after that because I wanted to learn a new skill,” he says, reflecting on the transition. “When you reach a certain level where you have mastered a skill, you want to move to the next one and pass a new boundary. And that’s what I did.”
For five years, between 2017 and 2022, Martin worked in research and development (R&D), moving from body engineering to powertrain simulation. During this period in R&D, his interaction with IT increased. In 2022, he became a senior manager for data analytics. In this role, Martin was charged with building a data analytics team.
“When I arrived in that position, there was no data platform, so I had to build it from scratch,” he says. “That’s the kind of challenge that I like to have. The company gives possibilities to people who are ready to put in the effort. You get the chance to take a jump.”
Unlike some companies that might have a forced management rotation every few years, Martin says Toyota Europe encourages people to hone their skills in a single area if they’re confident. However, for those who want to try something new, the right candidates are given new opportunities to excel in other areas.
“For people who are ready to jump, learn and invest in themselves, there is a possibility,” he says. “But you must create the opportunity – that’s important. No one asked me to do data. No one asked me to move to AI. I proposed the shift. Then it’s a process of what we call Nemawashi”, a Japanese business practice of building consensus for a proposal among key stakeholders before a formal decision.
Building a platform
Martin’s priority during the past few years has been to create an enterprise-wide data mesh. He says Snowflake’s cloud-based technology is the cornerstone of Toyota Europe’s platform.
“Everything relies on strong data foundations and governance,” he says. “It’s important to have role-based access control, encryption and data available on the platform, but only for people who are authorised to access it. That’s all something we have built on Snowflake.”
“For people who are ready to jump, learn and invest in themselves, there is a possibility – but you must create the opportunity”
Thierry Martin, Toyota Motor Europe
Rather than a traditional data warehouse, Martin describes Snowflake as a scalable computation engine for analytics and AI initiatives. Other key technologies in the organisation’s data stack include Calibra for governance, Dataiku for collaboration, Qlik for ingestion, DBT for transformation, Monte Carlo for observability, and Sigma for analytics.
To ensure compliance was baked into their processes from the beginning, his team worked with internal enterprise architects and Snowflake professional services to define the right approach to data integration. Across all organisational areas, from design to logistics, his team has created a backlog of between 300 and 400 data projects.
In January, they passed the milestone of launching 100 data products in their internal data marketplace. Martin says the company continues to explore Snowflake features. Toyota Europe is already using Snowflake Intelligence, the tech company’s agent that allows users to exploit enterprise knowledge using natural language. A close working relationship with the business makes it much easier to create tightly focused data solutions, says Martin.
“My team can answer questions about data architecture, such as, ‘What’s the enterprise data model or the logical data model?’ If someone wants to understand data governance, protection or privacy, my team can answer. My team can also answer questions about optimising the model or building a data pipeline to get data into Snowflake.”
Leading from the front
Martin says his promotion to head of enterprise data and analytics at Toyota Europe in 2024 was a recognition of the growing importance of emerging technologies.
He says his role overseeing data and AI corresponds with Gartner’s description of a chief data officer (CDO). The responsibilities of his role include dealing with governance, building business relationships, upskilling and training people, and managing data science and platform teams.
“That is all part of my job description,” he says. “Now I’m an executive, that status gives me budget accountability, and the right to make decisions around my budget. It’s a new kind of job. It sounds cool, but it’s also a role that will become mandatory.”
Martin says CDO is generally viewed as a challenging role. Industry experts agree, with technology specialist DataIQ suggesting that the average tenure of a data chief in Europe is 1.9 years. The research suggests it often takes three to five years for the conditions associated with a data transformation to mature, including building trust, embedding governance, evolving operating models, and reshaping decision-making practices.
Given these complex demands and a 1.9-year average tenure for European CDOs, it’s unsurprising that many organisations never reach maturity before a change in data leadership. Thankfully, Martin, with his long history of driving data-enabled digitalisation at Toyota Europe, is rising to the task at hand – and he has advice for other would-be CDOs.
“It’s a very challenging role intellectually. You need to understand the significance of data, AI and governance. You need to understand the importance of architecture, to link that awareness with a vision and to link that vision with a budget – and then to execute your strategy,” he says, before outlining how successful CDOs seek out continuous development.
“The intellectual challenge means that you need to be eager to learn. You also need to find time to run. Personally, if I stop running, then in two years, with the rapid pace of change, there’s a risk I won’t be relevant anymore.”
Looking ahead
Martin says an important KPI for his organisation is utilisation of Snowflake and the rest of the data stack across the business. Several factories have already been onboarded to the Snowflake platform. Now, his team is exploring how the technology is used in individual European operations, such as Toyota France, Italy, Spain, Germany and the UK.
“We’re making sure the markets themselves use Snowflake, so that’s what we are actively working on,” says Martin, suggesting that data centralisation through the platform presents new opportunities. Before Snowflake, staff relied on API calls to pull data from Toyota Europe and would then sync the data into their systems.
Let’s use data to help run the business with the best efficiency first – and then we can begin to transform Thierry Martin, Toyota Motor Europe
“Now they can take data directly from Snowflake,” he says. “And they don’t just access the platform to download data, they can also build inside it.” This integrated approach makes it much easier for operations across Toyota Europe to exploit new ways of working.
“If one market, for instance, is building something useful, then it means it might be interesting for another market to use the same adaptation,” he says. “So, it’s about adding markets that historically had different systems and converging them onto one platform. And then we can create economies of scale and save time in development.”
Martin’s team continues to seek opportunities associated with AI and data. Snowflake Intelligence, the tech company’s agent for exploiting enterprise knowledge with natural language, allows business users in Toyota Europe to generate insights rapidly. However, Martin recognises that emerging technology in all its forms remains a work in progress.
“Data quality is important, and we will see what AI brings,” he says. “In the short-to-medium term, I see more data for various uses inside of our business, improving people’s work, vehicle quality and manufacturing processes.”
For example, Martin says the team is investigating how agents might use data to help boost maintenance processes. “When you look at the power of what you can achieve, it’s quite amazing,” he says, referring to the potential of AI and data to enable long-term change across multiple business areas.
“The first transformation is inside our business. Let’s improve first how we work. Every consultant will say, ‘Let’s transform the business.’ But, actually, let’s use data to help run the business with the best efficiency first – and then we can begin to transform.”
