Successful data projects are built on solid foundations. What happens when we’re misled or unaware of what a solid foundation for data teams means? When a data team is missing or understaffed, the entire project is at risk of failure. This talk will cover the importance of a solid foundation and what management should do to fix it. To do this I’ll be sharing a real-life analogy to show how we can be misled and what that means for our success rates. We will talk about the teams in data teams: data science, data engineering, and operations. This will include detailing what each is, does, and the unique skills for the team. It will cover what happens when a team is missing and the effect on the other teams. The analogy will come from my own experience with a house that had major cracks in the foundation. We were going to simply remodel the kitchen. We weren’t ever told about the cracks and the house needs a completely new foundation. In a similar way, most managers think adding in advanced analytics such as machine learning is a simple addition (remodel the kitchen). However, management isn’t ever told that you need all three data teams to do it right. Instead, management has to go all the way back to the foundation and fix it. If they don’t, the house (team) will crumble underneath the strain.