Secrets of Data Analytics Leaders

It's not easy being the head of data & analytics at a large organization. You must align a large team across multiple disciplines; you must deal with oodles of legacy systems and tools that hamper innovation; and you must deliver business value fast to keep executives at bay and your job intact. You also need to recruit dynamic managers who can push the envelope while meeting operational objectives. And when you falter--which you inevitably will-you have to rebound fast.

No one knows these lessons better than Tiffany Perkins-Munn. She currently runs a 275-person data & analytics team at JP Morgan Chase that consists of data engineers, data scientists, behavioral economists, and business intelligence experts. She thrives on versatility, having earned a Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Building on this foundation, she has accumulated vast experience in the art of managing data & analytics teams during her 23 years in technical and managerial roles in the financial services industry.

In this interview, you’ll learn:

1. Tiffany’s secret for aligning a large data & analytics team and keep them from splitting into silos of specialization

2. Her favorite techniques for recruiting the right people to her team.

3. How to wade through the thicket of legacy systems and deliver innovative solutions quickly.

4. The impact of GenAI on her operations and the financial services industry.

5. How to advance your careers in data & analytics.

Adopting community of practice principles, along with coaching and mentoring, is a practical approach to fostering and cultivating data literacy.

Published at:

https://www.eckerson.com/articles/a-people-first-approach-to-developing-data-literacy

This blog examines the upcoming trend of domain-specific LLMs and evaluates three different methods of implementation.

Published at:

https://www.eckerson.com/articles/the-next-wave-of-generative-ai-domain-specific-llms

Many machine learning (ML) use cases center on real-time calculations. This article defines streaming ML and its architectural components.

Published at:

https://www.eckerson.com/articles/machine-learning-and-streaming-data-pipelines-part-i-definitions-and-architecture

Companies need to invest heavily in teams and people, both at corporate and in the field, if they want to become a data-driven organization.

Published at:

https://www.eckerson.com/articles/organizing-for-success-part-iii-how-to-organize-and-staff-data-analytics-teams

Data management practices have changed substantially since the early 1990s and the dawn of data warehousing.

Published at:

https://www.eckerson.com/articles/the-continuing-evolution-of-data-management

Conventional data governance conflicts with today’s world of self-service analytics and agile projects.

Published at:

https://www.eckerson.com/articles/modern-data-governance-problems

Let's reflect on the events of the past year and prognosticate on what may transpire in the months ahead.

Published at:

https://www.eckerson.com/articles/trends-for-2024-our-team-gazes-into-the-crystal-ball

Data leaders must prepare their teams to deliver the timely, accurate, and trustworthy data that GenAI initiatives need to ensure they deliver results. They can do so by modernizing their environments, extending data governance programs, and fostering collaboration with data science teams.

Published at:

https://www.eckerson.com/articles/the-data-leader-s-guide-to-generative-ai-part-i-models-applications-and-pipelines

Data modeling is a core skill of data engineering, but it is missing or inadequate in many data engineering teams. These teams focus on moving data with little attention to shaping the data. They engineer processes, not products. Full data engineering is both process and product engineering, and that calls for data modeling.

Published at:

https://www.eckerson.com/articles/a-fresh-look-at-data-modeling-part-2-rediscovering-the-lost-art-of-data-modeling