Building Guerrilla Analytics Teams
Data scientists. Unicorns. Rock stars. Organisations want to hire analytical minds. Organisations want to embrace Data Science. Organisations want their people to have the skills to get value from their data. But so many organisations struggle to get started. They hire bright minds but cannot bring the business on a data science journey. They land teams of data scientists but fail to anticipate the technology requirements to enable those scientists. They put some data scientists in ‘a lab’ and expect magic to come out.
Wouldn’t it be great to hire the right people to hire in the right order so you could get your Data Science team off the ground? Is there a priority technology stack you should focus on instead of waiting years before your team can do useful work?
The answer is Guerrilla Analytics. Light weight teams, with light weight processes and simple technologies that follow the 7 Guerrilla Analytics Principles.
In this webinar on ‘Building Guerrilla Analytics Teams’, I explain how the 7 Guerrilla Analytics Principles help you build an agile Data Science team and get them productive.
You can learn more about building a Guerrilla Analytics capability in my book Guerrilla Analytics: A Practical Approach to Working with Data which has chapters devoted to getting the right people in place, giving them the right technology and controlling everything with a minimal lightweight process.
You can access the full recorded webinar here and the slides are embedded below.
Questions from the ‘Building Guerrilla Analytics Teams’ Webinar
Some really interesting questions came up at the end of the webinar. I’ve listed them below and will pick them up in subsequent blog posts.
- How do you build a business case to resource and set up a data science team?
- What is the number one tip for someone putting together a completely new data science team?
- What role is most important when setting up a data science team?
- What are the typical challenges faced when setting up a Guerrilla Analytics team?