Many leaders talk about the power of data, yet some still hesitate to fully integrate it across their organizations. According to analytic translator Wendy Lynch, that hesitation often stems from the fear of what the numbers might reveal about performance, culture, or leadership itself.
“I have seen this type of fear and reluctance too, and it feels similar to someone avoiding their preventive screenings,” Lynch says. “On the one hand, it protects you from bad news. On the other hand, when you delay, any existing problem gets more difficult to treat, and more lethal. Hiding from problems isn’t true leadership.”
Lynch says this fear reveals much about modern leadership culture. Some executives are focused on self-preservation, while others simply do not know how to use data to their advantage. Many organizations also suffer from a lack of trust and understanding between business leaders and data teams. “It may reflect protectionism, a lack of awareness about how to leverage data, or a disconnect between leaders and data scientists,” she says.
The shortage of skilled data professionals makes this gap even more concerning. A study revealed that in-demand data scientists switch employers on average every 1.7 years. Three major reasons were identified for driving this “great resignation” among data scientists: Lack of employee engagement, the gap between reality and expectations, and the lack of professional development opportunities.
According to Lynch, trust in data begins with small steps. One approach is to show leaders practical examples of how integrated data can produce clear benefits such as cost savings or innovative solutions. Once they see those advantages, they are more likely to support broader integration. Another method is partial integration. Combining HR, payroll, and training data, for example, can reveal valuable insights about workforce performance. When those insights lead to positive results, leaders often become more open to linking other areas such as safety, sales, or operations.
Building trust between business and data teams is also critical. “They don’t communicate in the same language,” Lynch says. “Companies need trained analytic translators (who leaders trust) who can speak to both teams and build a constructive alliance.”
When a company connects all of its systems, data can reveal the true character of its culture. Complete integration not only shows how each department performs but also how those parts influence one another. Lynch says that data can uncover whether a company values improvement, innovation, safety, or knowledge-sharing. “Do improvements last over time? Are high-performing teams studied and replicated? Are injury rates consistently low? Is innovation happening faster?” she asks. “These patterns tell the story of a company’s culture.”
The benefits of integration go beyond understanding culture. Lynch describes how one organization faced high turnover in specific departments but lacked the resources to investigate every case. After integrating HR and operations data, analysts identified the top predictors of turnover, such as excessive shift work, limited time off, and uneven experience levels among staff. HR then launched targeted programs like mentoring and temporary staffing, and turnover declined almost immediately.
Another example comes from a retail company that wanted to cut costs by hiring more part-time workers. Analysts used integrated data to compare sales, revenue, and customer retention across employee types. They discovered that long-term workers generated enough additional revenue to more than offset their higher benefit costs. With this insight, the company shifted its strategy to retain experienced staff rather than replace them.
After decades of investment in analytics, many assume the biggest barriers to effective data use are technical. Others blame a lack of imagination among leaders. Lynch believes the real issue is a communication gap between business and data functions. “Most business leaders do not have sufficient training to know what and how to ask the right questions in data language. Plus data scientists, for the most part, are underinformed about business priorities and what leaders want to accomplish. Data people want specific data requests, business people want support for their decisions,” she says. “Neither gets what they need. This results in a frustrating cycle of unsatisfactory analyses and inefficient rework.”
Her proposed solution is both practical and forward-thinking. Rather than blaming leaders for lacking analytical skill or data scientists for answering the wrong questions, she advocates for developing a hybrid professional who is fluent in both the language of business and the language of data, capable of bridging the gap and earning the trust of both teams.
