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Data Science

Radar—Hiring and Building High Impact Data Teams

December 2022

Tu(s) presentador(es)

Curren Katz, PhD

Senior Director of Data Science Portfolio Management @ Johnson & Johnson

Curren Katz is the Sr. Director for Data Science Portfolio Management at Johnson & Johnson, where she leads a variety of R&D projects in healthcare data science.

Prior to joining J&J R&D, Curren was the Director of Data Science R&D at Highmark Health where she led data science product development, research services, and other data & analytic innovation, and more.

Curren was named one of IBMs top 40 women leaders in AI for 2021 and has been a keynote speaker and featured panelist at multiple Think and Data & AI Forum events, where she has spoken about women in data science leadership, as well as AI, data & healthcare.

Additionally, Curren has been a member of the Analytic Leader’s Council for the International Institute of Analytics (IIA) and represented Highmark at the Future of Privacy Forum on ethical AI, data sharing and partnership models.

Dan Kellett

Chief Data Officer @ Capital One

Dan has over 20 years of experience building, maintaining and governing model-based solutions for consumer credit (including a range of complex Machine Learning techniques). As a Data Scientist he has built Machine Learning applications encompassing the whole customer cycle from marketing to application risk assessment to customer management, covering a range of geographies.

In his current role Dan is responsible for all Data Science, Data Analytics and Data Stewardship for the Capital One UK business, leading a team of over 80 highly-skilled data professionals. Dan is a regular conference speaker on the topics of pragmatic data science and building successful analytic teams.

Summary

Building high-impact data teams involves more than hiring skilled data professionals. Organizations must carefully consider the composition and integration of their data teams to drive meaningful business outcomes. Expert speakers Curran Katz from Johnson & Johnson and Dan Kellett from Capital One UK stress the importance of defining the roles within data teams, from data engineers to MLOps specialists, and ensuring they align with business goals. They highlight the significance of data quality and the strategic prioritization of tasks to achieve quick wins and sustained success. The discussion further explores the importance of upskilling existing employees versus hiring externally, the role of management in supporting data initiatives, and the need for aligning data strategy with overall business objectives. By sharing real-world examples, such as optimizing chemotherapy scheduling and enhancing mobile app features through data insights, the speakers emphasize the transformative potential of well-structured data teams.

Key Takeaways:

A high-impact data team requires diverse roles and collaboration to deliver business value.
Data quality is vital, and initial hires should focus on ensuring data accessibility and usability.
Upskilling existing employees can be beneficial but depends on available resources for training.
Management support and alignment with business strategy are essential for the success of data initiatives.
Quick wins through data insights can build momentum and demonstrate the value of data teams.

Deep Dives

Building High-Impact Data Teams

High-impact data teams are defined by their ability to deliver significant business value through data-driven insights. Curran Katz of Johnson & Johnson and Dan Kellett of Capital One UK stress that such teams require a combination of diverse roles, including data engineers, data scientists, and MLOps specialists. Katz points out, "It's important to bring in people who can connect with the business and tell the story," stressing the need for effective communication and storytelling within data teams. The inclusion of roles like data storyteller or visualization expert ensures that insights are not only generated but also effectively communicated to drive business impact.

Data Quality and Achieving Quick Wins

Data quality is a fundamental element of successful data teams. As Dan Kellett notes, "If you don't have any data, or if that data is of poor quality, you're not going to get any real results." Early hires should focus on ensuring data accessibility and usability, allowing teams to achieve quick wins that demonstrate value and build momentum. Kellett shares an example of how natural language processing and clustering techniques helped prioritize mobile app features, leading to improved customer experience. These early successes not only prove the worth of data teams but also help secure further investment and support from the organization.

Data Science Hiring and Upskilling

The decision to upskill existing employees or hire new talent is a common challenge for organizations building data teams. Katz and Kellett discuss the benefits of both strategies, stressing that upskilling can be advantageous if there are resources available for training. Katz remarks, "Skills are teachable," suggesting that with the right attitude and resources, existing employees can be trained in technical skills. However, hiring externally may be necessary when specific expertise is required that cannot be developed internally. The choice between upskilling and hiring should be guided by the organization's current capabilities and future needs.

Integrating Data Strategy with Business Goals

Management plays a vital role in the success of data teams by providing support and ensuring alignment with the organization's strategic goals. As Katz explains, engagement from management is necessary to identify business problems that data teams can address. This involvement helps prioritize data projects that align with what keeps executives awake at night, as Kellett points out. Additionally, aligning data strategy with overall business objectives prevents conflicts and ensures that data initiatives contribute to the broader organizational goals. Effective communication and education about the potential of data are essential for gaining management buy-in and driving successful data-driven transformations.