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[Radar Recap] Unleashing the Power of Data Teams in 2023

Vijay Yadav and Vanessa Gonzalez will outline the keys to building high-impact data teams in 2023.
Mar 30, 2023
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Guest
Vanessa Gonzalez
LinkedIn

Vanessa Gonzalez is the Sr. Director of Data Science and Innovation at Businessolver where she leads the Computational Linguistics, Machine Learning Engineering, Data Science, BI Analytics, and BI Engineering teams. She is experienced in leading data transformations, performing analytical and management functions that contribute to the goals and growth objectives of organizations and divisions. 

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Guest
Vijay Yadav
LinkedIn

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. 

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Host
Richie Cotton

Richie helps individuals and organizations get better at using data and AI. He's been a data scientist since before it was called data science, and has written two books and created many DataCamp courses on the subject. He is a host of the DataFramed podcast, and runs DataCamp's webinar program.

Key Quotes

Any work that is being done by a data team is all about the business outcome. The output of data teams must provide value in terms of growth, efficiency or cost-savings. So in high-impact teams, it's about how quickly you can give value back using data.

Data teams can be a huge environment to work across—ideally team-members will specialize in one area while being cognizant of everything that happens surrounding data.

Key Takeaways

1

Data teams should be more aligned to working closely with many parts of the business rather than tech-specific functions such as IT. Getting value from data teams is heavily linked to ensuring teams solve the right business problems with data. To do this, it makes more sense for data teams to work across the business, dependant on what goals the organization is looking to achieve. 

2

To ensure real-world value comes from your data projects, keep referring to the problem you are trying to solve. It can be easy to fall into a trap of 'how' to solve a problem while ignoring 'why' you are solving it. The real value of a data project comes from the 'why' more than how it is solved. 

3

You are much more likely to see wider variety of value and thought when you have a data team that is diverse in both background and culture. It is an essential factor to asses when building or growing a data team - a wide range of people and opinions will create a wide range of opportunities for business value. 

Topics
Data Science
Data Leader
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