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The Gradual Process of Building a Data Strategy

March 22, 2022

Vijay Yadav, Director of Quantitative Sciences & Head of Data Science at Center for Mathematical Sciences at Merck breaks down the different pillars of a scalable data strategy.

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Key Takeaways


Becoming data-driven is a holistic effort: Data strategies requires examining a variety of levers that an organization can scale. This includes ownership, data asset management, governance, enablement, culture, skills, and much more.


The most important component is people: When trying to become data-driven, the cultural and skills component is arguably the most important. Organizations cannot accelerate technological investments without building up a culture of analytics and data.


Data transformation is iterative: As opposed to traditional technology projects, becoming data-driven is a long arduous process that requires investment, time, and patience. Companies looking for ROI within 6 months of their data transformation will most likely be disapointed.

Key Quotes

We know that ROI cannot be that easy derived for data transformation. It takes some time to build out the organization's capabilities. If somebody's looking at ROI in six months to a year, it's probably not practical.

When building data literacy and evangelizing the value of data—it's important to keep people engaged with events and tools like hackathons, community of practice, and upskilling. It's a gradual upskilling.

About Vijay Yadav

Photo of Vijay Yadav
Vijay Yadav

Vijay Yadav is the Director of Quantitative Sciences and Head of Data Science at the Center for Mathematical Sciences at Merck. He is a seasoned data leader who drives the analytics strategy and roadmap for Merck’s Manufacturing teams and owns the development and deployment of advanced analytics capabilities throughout Merck. Vijay has over 20 years of experience working in pharmaceutical and chemical manufacturing and has deep insight into developing data strategies that scale. 

Photo of Adel Nehme
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

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