For years, Nestle has remained at the top of CGT’s 100 ranking for net revenue by publicly traded consumer goods companies. With over 2,000 brands, 1.2 billion products sold every day, products sold in 187 countries and 300,000 employees worldwide — it also means that data and technology challenges can oftentimes come at massive scale.
But despite its size, Nestle CIO Filippo Catalano and chief data and analytics officer Fancesco Marzoni reveal that the company prides itself for still being able to move at start-up speed.
“In the past few years, we have accelerated considerably our journey in digital transformation on the consumer side, on the customer side and on the manufacturing side,” Catalano says, and during the opening keynote at Analytics Unite 2020, the duo went over how they are doing this through data, analytics and AI.
First, Catalano notes that IT is at the center of this transformation, which is obvious through its motto: “We make IT possible.” During the session, Catalano and Marzoni covered the scale of what Nestle’s data and analytics journey has consisted of, including:
- The largest, fully integrated transactional landscape in the FMCG industry
- Massive adoption of “Citizens Analysts” solutions with over 40k users
- Raised importance of external data feeds for day to day operations and value extraction
- Freedom in a box model
By providing the right framework, Catalano says they aim to empower their people and markets to do what they need to deliver value every day, which is where FAIR comes in. Marzoni talked about how a data set is a data asset if it’s Findable, Accessible, Interoperable and Reusable.
“FAIR doesn’t stand only for a technical framework, but it stands for an ethical framework,” he said. Marzoni then went over Nestle’s six AI ethics principles, which is a values-based approach. The principles are interconnected to be mutually beneficial; they are rooted in Nestle’s culture of integrity — particularly its approach to ethical business — and they are now part of the Business Principles at a corporate level.
Some of the six principles include transparency, diversity and accountability, which Marzoni detailed along with various examples. “From an acceptance point of view, a lot of the fears you hear in the industry around AI/ML are indeed coming from the difficulty of explaining how the data is obtained, how the data is stored, and how the data processed, and how the data is then used to derive value for consumers, customers and employees,” added Catalano.
Nestle is also using technology and data "for good" not only within its sustainability commitments, but also for accountability and technical robustness (talent). However, Marzoni asserted, “We do need to have a clear mission as a force for good.”
The duo closed by listing some key learnings from this process including to tackle the ethics principle early in your data/AI journey and strongly link it to company purpose. Incorporate a strong foundation like FAIR for your data strategy and think about up-skilling or re-skilling the workforce as domain expertise is key. Lastly, break down complexity.