It’s not uncommon for data science teams to find themselves in conflict with their product counterparts. This can be detrimental to both parties, as it prevents them from reaching their full potential. But what is the root cause of this friction?

One reason may be the differing nature of their goals. Product teams are often focused on driving profit, while data science teams prioritize cost-effectiveness. This can lead to tension when it comes to customization, delivery timelines, and more.

Another reason for this conflict is the uncertainty that is inherent in data science projects. Product teams tend to prefer a degree of certainty in their planning, while data science teams are more comfortable embracing uncertainty.

Finally, the product team may lack the knowledge and understanding of what is feasible and uncertain when designing a data science project. This can lead to the data science team taking on more product management responsibilities, but without the necessary skillset.

It’s important to understand these underlying causes of conflict in order to find ways to resolve them and promote collaboration between data science and product teams.

Ideally, you would want a product manager who fully understands the complexities of data science, or a data scientist with a strong background in product management. While you may come across these individuals, they are not always readily available. So, what can be done to bridge the gap between data science and product teams?

One solution is to align the vision, strategy, and objectives of both teams. This can often be achieved through discussions and negotiations, but it requires a significant amount of effort and management. A more effective approach is to establish principles that both teams can agree upon.

Principles provide a framework for decision-making and prioritize resources, time, and cost. They allow teams to make trade-offs and align on priorities. When resources are limited, principles can guide decisions and empower autonomous work. This way teams can make decisions together, without the need for constant management effort.

In conclusion, conflicts between data science and product teams are often a result of mismatched priorities. By establishing principles, teams can align on priorities and make trade-offs that would benefit both teams. This can empower autonomous work and lead to a better collaboration between the two teams.