Data science: the art of asking the right questions

Often, data science is misunderstood as a specialized application of various tools and technology. But as Martin Ryan, vice president of pricing at XPO, emphasized in a recent SMC³ session, the real power of data science lies in knowing how to ask the right questions.

“The most valuable skill set is actually the ability to formulate problems,” Ryan explained.

While tools and techniques have their place, their effectiveness depends on a clear understanding of what needs to be solved. This philosophy underscores a shift in how organizations must approach data—not solely as a mechanism to solve their problems, but also as a lens to refine objectives and strategies.

Communication as the cornerstone

Effective data analysis depends on effective communication. Good communication allows data scientists to get the information they need from operations experts by asking the right questions. It also allows them to relay often complex insights to stakeholders after the analysis is done. Ryan argued that data scientists are essentially translators—transforming real-world conditions into data sets that can then be translated to insights. The True North for this often tortuous process is clarity and concision. Ryan’s advice for data scientists new to the field: Practice your elevator pitch.

“How can I condense this message into one or two minutes?” he encouraged data scientists to ask themselves. 

This skill becomes even more crucial when in the face of changing real-world circumstances. Ryan cited an example of how, when working for a company, a 30-minute presentation of his was unexpectedly cut to four minutes. Knowing how to get points across quickly can turn complex ideas into actionable insights for decision-makers.

Adopt a CPA mindset

To further help with communication skills, Ryan stressed the importance of not simply understanding data science, but also understanding a business at its core, which often begins with grasping its financial mechanics.

“Some of the very best data scientists I’ve met also have ventured to get a CPA,” Ryan noted.

While accounting training may seem like a needless diversion, Ryan stressed how it can help give data scientists an intimate acquaintance with how companies generate value for customers. Combining technical data skills with communication and business acumen creates a unique blend of expertise that helps data scientists stand out.

The emerging role of AI in data science

The conversation also touched on AI and its abilities to revolutionize LTL’s use of data. While some AI capabilities are further along down the road, Ryan pointed to forecasting as a primary and immediate area of impact.

“Having a good forecast of supply and demand increases the chances that we will efficiently use capacity to help deliver service to customers,” he said.

AI techniques, particularly those leveraging multivariate systems, can outperform traditional methods, offering more dynamic and accurate predictions.

Additionally, AI is transforming data accessibility. Document recognition and automation are helping carriers and shippers digitize paper-based processes, creating new data streams. This abundance of data improves operations across the board—from dynamic pricing strategies to better capacity planning.

Collaboration is a two-way street

As AI and data analytics evolve, collaboration between carriers and shippers becomes even more crucial, as each holds critical data that completes the other. Ryan described the “two-way street” necessary for carriers and shippers to share data effectively.

“If you can look behind the lens of one of your large shippers,” he explained, “you can better match equipment availability with demand forecasts.”

This mutual transparency builds efficiency as well as trust, enabling both parties to navigate challenges proactively rather than reactively.

This underlines a familiar point: As a shipper or carrier, picking the right partners is essential. Companies aligned in their vision for data and technology can achieve breakthroughs together. As Ryan put it, “insights create more opportunities for more insights,” creating a cycle of continuous improvement.

Building expertise: a mile deep, an inch wide

For early-career professionals in a complex business like LTL, Ryan has clear advice: Learn one area of the business deeply.

“If you really become an expert in some area of the transportation business,” he said, “it helps you naturally learn more about the rest of the business.”

Specialization offers a solid foundation upon which broader expertise can be built over time.

This principle applies not just to individuals but also to teams within organizations. By developing a deep understanding of specific problems, teams can identify cross-functional solutions that drive meaningful change. It’s a strategy that further reinforces the importance of both focus and collaboration in achieving long-term success.

The road ahead

As LTL evolves, leveraging data and AI effectively will require more than just technical know-how. It will demand a nuanced understanding of business priorities, a commitment to clear communication, and a willingness for shippers and carriers to work together.

Ryan’s insights are a blueprint to navigate this ongoing evolution. Start by framing the right problems, then build relationships through trust and transparency, and follow through by continually adapting to new tools and techniques. For those willing to embrace this multifaceted approach, the opportunities are limitless.

Interested in joining LTL Hybrid Sessions? Register here: https://smc3.info/LTLedu