Building Trust with Your Data Team: Why Collaboration Leads to Better Insights
“Great things in business are never done by one person; they're done by a team of people.” – Steve Jobs
Successful business decisions are rarely the product of one perspective alone. In the same way, effective data analysis requires collaboration, where business expertise meets analytical rigor to uncover meaningful insights. Data teams often receive specific requests for models or analysis from stakeholders who have some understanding of analytics. A stakeholder with a working knowledge of analytics asks for a specific type of model or analysis, confident that they already know the right approach. While stakeholder involvement and understanding are valuable, sometimes this level of familiarity can lead to unintended missteps—especially when the suggested method doesn’t align with the nature of the data or the business problem.
When Familiarity Becomes a Challenge
Imagine a marketing director asking for a regression model to evaluate campaign performance. While it seems reasonable, the data team might know that customer behavior is influenced by multiple factors that regression alone may not capture, making a segmentation or clustering model more effective. In this case, focusing on the goal—understanding campaign effectiveness—rather than prescribing the method would lead to better insights. While it seems reasonable, the data team might know that the data lacks a consistent time component, making a classification or clustering model more effective. In this case, focusing on the business problem—forecasting demand—rather than prescribing the method would lead to better insights. But the data team knows that churn is a binary outcome—either a customer leaves or they don’t—making logistic regression or even a classification model more suitable. The sales leader’s understanding of regression is helpful, but without deferring to the data team’s expertise, the wrong model could lead to inaccurate predictions and misguided business decisions.
This kind of disconnect can arise when stakeholders have a solid understanding of analytics but may not be familiar with the nuances of specific methods. When data teams are put in a position to execute a flawed request rather than collaborate on the best approach, the result can be misleading insights and missed opportunities.
Why Collaboration Matters
Effective data analysis thrives when stakeholders and data teams work together, blending business expertise with technical know-how. Stakeholders have valuable insights into business operations, customer behavior, and strategic goals. The data team, meanwhile, understands how to structure data, select the right models, and validate findings. When both sides communicate openly and respect each other’s expertise, the outcome is a stronger, more accurate analysis.
In the churn example, the ideal approach would be for the sales leader to describe the business problem—“We need to understand why customers are leaving and how to prevent it”—and let the data team recommend the most effective model. By focusing on the desired outcome rather than prescribing a specific method, stakeholders can bring valuable ideas to the table while trusting the data team to identify the most effective analytical approach. This collaboration leads to more meaningful insights and better business decisions.
Key Takeaways
Stakeholder knowledge of analytics is valuable and should complement the expertise of the data team to refine the analysis process.
Clear communication about business problems, rather than specific methods, leads to better model selection and more accurate insights.
Mutual trust between stakeholders and data teams creates a stronger foundation for decision-making.
A collaborative approach ensures that analysis is aligned with both business goals and technical realities.
Fostering a Strong Partnership
The most successful data-driven companies create an environment where stakeholders and data teams operate as partners. Encourage open dialogue about business goals, data limitations, and analytical approaches. When stakeholders focus on the problem and trust the data team’s technical expertise, they’re more likely to get answers that drive real business impact.
Building trust and collaboration between business and data isn’t about knowing it all—it’s about knowing when to lean on each other’s strengths.