DataFramed DataCamp

As organizations of all sizes continuously look to drive value out of data, the modern data stack has emerged as a clear solution for getting insights into the hands of the organization. With the rapid pace of innovation not slowing down, the tools within the modern data stack have enabled data teams to drive faster insights, collaborate at scale, and democratize data knowledge. However, are tools just enough to drive business value with data? 
In the first of our four RADAR 2023 sessions, we look at the key drivers of value within the modern data stack through the minds of Yali Sassoon and Barr Moses. 
Yali Sassoon is the Co-Founder and Chief Strategy Officer at Snowplow Analytics, a behavioral data platform that empowers data teams to solve complex data challenges. At Snowplow, Yali gets to combine his love of building things with his fascination of the ways in which people use data to reason.
Barr Moses is CEO & Co-Founder of Monte Carlo. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and, among other functions, built the data/analytics team. 
Listen in as Yali and Barr outline how data leaders can drive value creation with data in 2023.

Data leaders play a critical role in driving innovation and growth in various industries, and this is particularly true in highly regulated industries such as aviation. In such industries, data leaders face unique challenges and opportunities, working to balance the need for innovation with strict regulatory requirements. This week’s guest is Derek Cedillo, who has 27 years of experience working in Data and Analytics at GE Aerospace. Derek currently works as a Senior Manager for GE Aerospace’s Remote Monitoring and Diagnostics division, having previously worked as the Senior Director for Data Science and Analytics.
In the episode, Derek shares the key components to successfully managing a Data Science program within a large and highly regulated organization. He also shares his insights on how to standardize data science planning across various projects and how to get a Data Scientists to think and work in an agile manner. We hear about ideal data team structures, how to approach hiring, and what skills to look for in new hires. 
The conversation also touches on what responsibility Data Leaders have within organizations, championing data-driven decisions and strategy, as well as the complexity Data Leaders face in highly regulated industries. When it comes to solving problems that provide value for the business, engagement and transparency are key aspects. Derek shares how to ensure that expectations are met through clear and frank conversations with executives that try to align expectations between management and Data Science teams. 

Finally, you'll learn about validation frameworks, best practices for teams in less regulated industries, what trends to look out for in 2023 and how ChatGPT is changing how executives define their expectations from Data Science teams. 

Links to mentioned in the show:
The Checklist Manifesto by Atul Gawande
Team of Teams by General Stanley McChrystal
The Harvard Data Science Review Podcast

Relevant Links from DataCamp:
Article: Storytelling for More Impactful Data Science
Course: Data Communication Concepts
Course: Data-Driven Decision-Making for Business

Oftentimes, Kaggle competitions are looked at as an excellent way for data scientists to sharpen their machine learning skills and become technically excellent. This begs the question, what are the hallmarks of high-performing Kaggle competitors? What makes a Kaggle Grand Master?
Today’s guest, Jean-Francois Puget PhD, distinguished engineer at NVIDIA, has achieved this impressive feat three times. 
Throughout the episode, Richie and Jean-Francois discuss his background and how he became a Kaggle Grandmaster. He shares his scientific approach to machine learning and how he uses this to consistently achieve high results in Kaggle competitions.
Jean-Francois also discusses how NVIDIA employs nine Kaggle Grandmasters and how they use Kaggle experiments to breed innovation in solving their machine learning challenges. He expands on the toolkit he employs in solving Kaggle competitions, and how he has achieved 50X improvements in efficiencies using tools like RAPIDS. 
Richie and Jean-Francois also delve into the difference between competitive data science on Kaggle and machine learning work in a real-world setting. They deep dive into the challenges of real-world machine learning, and how to resolve the ambiguities of using machine learning in production that data scientists don’t encounter in Kaggle competitions.

Studies have shown that companies lacking in racial diversity also have a corresponding lack in their ability to innovate as a whole, which makes it important for any organization to prioritize an inclusive workplace culture and welcome more women and underrepresented groups in data.
This is why Nikiska Alcindor's work is so vital to the future of the data science industry. Nikisha is the President and Founder of the STEM Educational Institute (SEI), a nonprofit corporation that equips underrepresented high school students with the technological skills needed to build generational wealth and be effective in the workforce. 
Nikisha is a strategic management leader with expertise in organizational change, investing, and fundraising. She is a  recipient of the 2021 Dean Huss Teaching Award, a board member of the Upper Manhatten Empowerment Zone, and has taught a master class at Columbia Business School as well as several guest lectures at Columbia University.
Throughout the episode, we discuss SEI’s three-pillar approach to education,  the rising importance of STEM-based careers, why financial literacy is crucial to a student’s success, SEI’s partnership with DataCamp, contextualizing educational and upskilling programs to your organization’s specific population, how data leaders can positively communicate upskilling initiatives, and much more.

In order for any data team to move from reactive to proactive and drive revenue for the business, they must make sure the basics are in place and that the team and data culture is mature enough to allow for scalable return on investment. 
Without these elements, data teams find themselves unable to make meaningful progress because they are stuck reacting to problems and responding to rudimentary questions from stakeholders across the organization. This quickly takes up bandwidth and keeps them from achieving meaningful ROI.
In today’s episode, we have invited Shane Murray to break down how to effectively structure a data team, how data leaders can lead efficient decentralization, and how teams can scale their ROI in 2023.
Shane is the Field CTO at Monte Carlo, a data reliability company that created the industry's first end-to-end Data Observability platform. Shane’s career has taken him through a successful 9-year tenure at The New York Times, where he grew the data analytics team from 12 to 150 people and managed all core data products. Shane is an expert when it comes to data observability, enabling effective ROI for data initiatives, scaling high-impact data teams, and more.
Throughout the episode we discuss how to structure a data team for maximum efficiency, how data leaders can balance long-term and short-term data initiatives, how data maturity correlates to a team’s forward-thinking ability, data democratization with data insights and reporting ROI, best practices for change management, and much more.

The most common application for data science is to solve problems within your own organization, and as professionals become more data literate, they rely less and less on others to solve their problems and unlock professional growth and career advancement.
But in the world of consulting, data science is used to solve other people’s problems, which adds an additional layer of complexity since consultants aren’t always given all of the tools they need to do the job right.
Enter Pratik Agrawal, a Partner at Kearney Analytics leading the automotive and industrial transportation sector. In this episode, we are taking a look at how data science is applied in the consulting industry and what skills are critical to be a successful data science consultant. 
As a software engineer and data scientist with over a decade of experience in the consulting world at companies like Boston Consulting Group and IRI, Pratik has a deep understanding of how to navigate the industry and how data science can be leveraged in it, as well as expertise in digital transformation projects and strategy.
Throughout the episode, we discuss common problems that consultants encounter, the skills needed to be successful as a consultant, the different approaches to analytics in consulting versus in an organization, how to handle context switching when juggling multiple projects, what makes consulting feel exciting and challenging, and much more.