- A look at how typical data teams are structured with examples from Snowplow customers, and discuss the pros and cons of each.
- Using the specific case of Hudl to explore the stages of evolution within the data team and what factors contributed to that evolution.
- Reflect on the lessons learned at Hudl and what they would have done differently during their evolution.
- Highlight the opportunities for data teams to exert their value and create long-term business impact.
The evolution of the data team
Lessons learned from
growing a data team from 3 to 20
There is no denying that growing a data team has it’s challenges. What you plan your data team structure to look like initially may not turn out to be the most effective long term. Building a well balanced skill set within your data team and evolving the function alongside the business to ensure continuous growth is no easy feat.
In this webinar, we will unpack how data team structures have evolved, drawing on examples from our customers and specifically from the data team at Hudl. Mindy Chen, Director of Decision Science at Hudl will take us on a journey through the challenges and opportunities she has seen when building a data team from scratch.
Growing from 3 data engineers to a robust team of 20, Hudl has been on a journey to establish their data capability. What started out as an experiment working on data ingestion has evolved into one of Hudl’s key competitive advantages. Over the past 4 and a half years, the data team at Hudl has moved through several stages of evolution, leading to great successes as well as some important lessons which Mindy will share with us.
In this webinar, Rebecca and Mindy will cover:
Director of Decision Science, Hudl
Head of Customer Success, Snowplow
London / GMT+1