Over the past 150 years or so, Nestlé  has evolved from a regional producer of powdered baby formula to a massive, global multi-conglomerate. From Nesquik chocolate milk to Purina Cat Chow, the Nestlé universe encompasses 291,000 worldwide employees, 2,000 brands and 400 factories

Like many of their legacy Global Fortune 500 peers, around 2017 Nestle found themselves at a crossroads. They realized that in order to maintain their competitive advantage in the digital age, they would not only need to operate with great agility, but also start developing the next wave of digital technologies such as Artificial Intelligence (AI), advanced analytics and machine learning. 

Up until that point, when it came to innovation and data management, each business unit, country and brand pretty much did its own thing. As a result, data was siloed limiting evidence-driven decision making and the successful operationalization of machine intelligence. 

“When you have 40 brands doing their own tests-and-learns with different pilots and different proofs of concept suggested by different agency partners, there’s no common learning agenda. It gets a little bit unwieldy,” Orchid Bertelsen, the head of digital innovation and transformation at Nestlé USA, explained at a 2020 Brand Innovators event. 

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If Nestle wanted to operate more like a startup, they quickly realized that establishing a powerful enterprise data and business analytics strategy was paramount to achieving this. Thus began their journey towards FAIR data and analytics. 

Nestle understood that in order for data to become an asset, it first had to be democratized - or accessible to everyone. Thus they developed their FAIR data framework which stands for:

• Findable - addresses the need to understand what existing data assets are available and what they mean
• Accessible - balances the need to ensure data assets are readily available with authorization and security considerations
• Interoperable - standardizing how data is linked and exchanged 
• Reusable - clarifies the usability of data in a new context according to its origin, quality and compliance aspects

The goal of Nestle’s FAIR framework is to ensure that the right people have access to the right data and can use these insights to make better, more strategic decisions. 

A Value-Based Approach to AI Ethics

Ethics and AI performance are intrinsically linked. As Reid Blackman wrote for the Harvard Business Review, “failing to operationalize data and AI ethics is a threat to the bottom line.”  Not only do poor ethical guidelines open companies up to immense reputational, regulatory, and legal risks, the consequences of poor ethics (i.e. AI bias and poor data quality) lead to ineffective bots. Considering AI applications can cost millions of dollars to develop, the price of failure is just too high to ignore.

In an effort to avoid this fate, Nestle baked AI ethics into their enterprise data strategy. As Nestle CIO Filippo Catalano and Chief Data and Analytics Officer Fancesco Marzoni explained at the 2020 Analytics Unite event, the 6 principles that defined their AI ethics approach were: 

• Transparency/explainability
• Technical Robustness
• Diversity, Non-Discrimination & Fairness
• Accountability
• Environmental & Social Wellbeing
• Privacy & Security

In addition to supporting Nestle’s data strategy, these principles also aligned with Nestle’s overall corporate culture of business ethics and integrity.

Centralized BI Tools

Once Nestle had its overarching data strategy in place, they needed to operationalize it. In partnership with IBM, Nestle developed a centralized BI system that provided users of all kinds from IT analysts to frontline managers - self-services analytics via Microsoft Power BI. 

Sitting atop their existing SAP data warehousing system, this tool empowers users to create, action and share their own bespoke data-driven insights. To maximize agility, data quality and usability, they built this new BI capability around 4 key components:

• BI tools
• Machine learning and statistical platforms
• Data management solutions
• Data governance and integration

*Image sourced from "Nestlé invests in advanced technologies, including Power BI, to foster a data-driven culture and empower employees to make smart business decisions," https://customers.microsoft.com/en-au/story/857107-nestle-consumer-goods-azure-power-bi

In early 2018, Nestlé rolled out its Power BI tool to all its users globally. Since becoming generally available, the company’s user base has grown to over 45,000 monthly active users.

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Data Ethics in Action

It’s no secret that, worldwide, women only make 77 cents for every dollar earned by men. In an effort to stay true to their corporate culture of ethics and integrity, Nestle decided to apply business analytics to the problem. 

Since 2018 Nestle has monitored the salary pay gap continuously and built a D&I specific dashboards hiring manager could use to hold themselves accountable and monitor their progress. These dashboards measure 5 key areas:

• Headcount
• Attrition
• Hiring
• Talent
• Succession

One area where this tool has been especially helpful is with succession planning. When it comes to entry-level and mid-level positions, many companies are able to achieve pay equity. However, as you climb the leadership ladder, that’s where the biggest discrepancies start to emerge. 

To address potential areas of gender discrimination as proactively as possible, Nestle’s D&I dashboard measures gender balance holders vs. successors, “meaning the ratio of women in every level in the company vs. the ratio of women who are equipped with knowledge and skills to succeed to senior roles.”

These metrics help ensure that women are not overlooked when it comes to training & development opportunities that could help prepare them for future roles. 

Nestle’s Digital Warehouse

Another interesting and innovative project supported by Nestle’s immense enterprise data network is its digital warehouse of the future. 

Developed in partnership with XPO logistics, this state-of-the-art 638,000-square-foot distribution center will feature advanced sorting systems and robotics co-developed with Swisslog Logistics Automation. Powered by predictive analytics and intelligent machines, this digital warehouse will be used to accelerate the distribution and delivery of Nestle products as well as function as a testbed environment for XPO technology prototypes prior to global release. 

Personalized Customer Engagement

Communicating effectively with customers is always a challenge. However, using machine learning and NLP, Nestle is able to build one-to-one relationships with its customers. Using these tools, Nestle is able to craft personalized messages to its customers.

In 2020/2021, Nestle has expanded this personalized approach to social media and digital ad campaigns. In addition, much like Nike did, they’re experimenting with DTC approaches to not only strengthen their direct relationships with customers, but collect more data on them too. With KitKat’s Chocolatory experience, for example, customers could customer and purchase uniquely flavored candy bars. This provided Nestle with a wealth of knowledge pertaining to their customer base and the changings tastes of the next, digitally native cohort of consumers.