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Meet Patel, Global Senior Director, Advanced Analytics – Strategy, Capabilities & Culture, Kellanova

Describe your career to date

I have a strong passion to solve challenging business problems and drive impactful results based on advanced analytics and data-driven decisions. Throughout my career, I have been bridging the divide between advianced analytics and business at a startup to Fortune 10 with domains expertise in data science, artificial intelligence (AI) and machine learning (ML), strategy, marketing, and financial modeling while working in CPG, automotive, telecommunications, media and advertising, and financial services.  

I am currently the Global Senior Director of Advanced Analytics – Strategy, Capabilities, and Culture at Kellanova (formerly Kellogg Company). I am responsible for leading and supporting initiatives across full-funnel marketing, revenue growth management, retail, and e-commerce by leveraging AI and ML to build and evolve data-driven solutions and capabilities globally.  

Additionally, I am also responsible for developing next-generation solutions through rapid proof of concepts and then scaling them across regions and markets by building industry-leading innovative solutions and utilizing data science best practices. I take a role in supporting with building strategic roadmap and planning, analytics quotient, value engineering, stakeholder alignment, and change management to deliver top-tier performance.  

Before Kellanova, I was a part of the Coca-Cola Company as Director of Advanced Analytics and Decision Science responsible for building data-driven capabilities and strategic initiatives to drive revenue growth through the lens of revenue science and retail execution. Prior to the Coca-Cola Company, I was part of the internal consulting group at Cox Automotive responsible for leading data-driven strategic growth solutions, operations research, and change management initiatives. I have also spent time at AT&T’s Big Data group responsible for creating algorithms and generating insights to monetize AT&T’s data assets by providing consultative and data-driven solutions backed by AI and ML to Fortune 100 clients. 

How are you developing the data literacy of your organization, including the skills of your data teams and of your business stakeholders?  

Over the last few years, we have built formal training modules by building a domain-specific learning platform and certification programs. The goal of the domain-specific learning platform is to drive a baseline understanding of topics with the hope of elevating the analytical skillset and subject matter expertise in revenue growth management, marketing, and other domains.  

Furthermore, we realized even greater success in elevating and equipping skills by building an analytical tool, capability, or solution-specific training modules to drive adoption and efficiency. The goal is to raise the overall analytics quotient across the entire company and with change management initiatives tied to the deployment of new products and solutions; we expect to continue to make progress towards data-driven decisions.  

Our approach to building a domain-specific community of practice has allowed us to create a forum to drive knowledge sharing, and learning in the form of new technology developments, exploring current challenges, and opportunities for improvements. 

What role do you play in building and delivering conventional AI solutions, including machine learning models? Are you involved in your organization’s adoptions of generative AI? 

I am largely involved in building AI and ML-based data-driven capabilities and solutions in revenue growth management, full-funnel marketing, retail, and e-commerce domains. Part of my role includes building a short and long-range roadmap and performing value engineering to prioritize use cases and solutions.  

Once the use case is defined, I work with the business stakeholders to understand their needs and translate their business problem into a data problem. I work with data scientists to define the problem, objective, logic, and approach for the solution including identifying different modeling techniques, ML models, parameter tuning, and data quality assessment, among other steps.  

Once we have derived the results based on different model runs and iterations, I work with the business stakeholders to get their perspective and make necessary adjustments before deploying the solution in the environment. I also develop criteria for MLOps to capture the model drifts and data quality flags to ensure the solution stays functional and relevant as expected.  

In terms of generative AI (genAI), I have been involved in identifying the use cases that would drive greater operational efficiency, and we are exploring a proof of concept to further build this capability beyond the limitations of genAI’s ability to perform descriptive analytics to rule-based predictive analytics. 

How are you preparing your organization for AI adoption and change management? 

As part of the Global Advanced Analytics team, we are largely focused on building commercial capabilities in full-funnel marketing, revenue growth management, retail, and e-commerce. To drive AI adoption, we work with the business stakeholders and create a vision for the art of possible, and how we can leverage artificial intelligence and machine learning to drive efficiency, optimization, and transformation.  

We prioritize use cases based on feasibility, scalability, and value creation. Once we have developed a proof of concept or prototype for a given capability, we work with the stakeholders to finetune it, and then we would scale it across multiple markets and regions.  

The biggest challenge with any scaled deployment is the adoption of the new tools and capabilities. The way to ensure less friction is by seeing that you are seamlessly providing additional value so that the end user is not stuck doing analysis paralysis but is feeling comfortable with the new capability to drive the business decisions forward. Based on the type of capability, it must be decided if there should be separate roles to help data and model output translations so stakeholders can focus on decision science problems.  

Ultimately, there is no one size fit all approach to driving AI adoption and change management, it largely depends on the culture, organizational structure, and the actual business problem at hand. 

Meet Patel
has been included in:
  • 100 Brands 2024 (USA)
  • No. 8 100 Brands 2023 (USA)