As someone who works in data science and understands smart mobility, I’ve noticed that sometimes there can be disagreements between data scientists and experts in smart mobility who don’t specialize in data. I’ve been part of various projects and have seen many succeed around the world. Often, disagreements are due to different ways of thinking and sharing ideas. Don’t misunderstand; most of my colleagues are well-intentioned and committed. However, I’ve observed certain behaviors that we could learn from and improve. Both data scientists and business professionals have their own ways of discussing what they need and their thoughts.
One common issue is that experts often struggle to explain their technical needs to a data scientist. This leads to challenging situations where,after spending a lot of time on calculations and running large models, data scientists present their work to the manager. The manager might then respond with surprise, asking if all that time was spent just to run the model when the real question was how to boost sales in different areas.
To meet managers’ expectations, data scientists and analysts need clear details. It’s important to ask the right questions to get the necessary information. As a data scientist, we must first understand the manager’s goals and what outcomes will make them happy with our work. Sometimes, creating a presentation can help our leaders see our results more clearly. If this approach is more effective, then it’s worth the extra effort to ensure they are pleased with our model.
But let’s remember the habits data scientists have. Data science is a complex field that takes time to master, and we should keep in mind that not everyone understands our technical language, like ‘hyperparameter’, ‘silhouette score of K-means’, or ‘variance analysis’. Using too much jargon in meetings is something many technical folks do. As data scientists, we need to use language that everyone in our industry can understand. If we only use technical terms known in our field, it can cause misunderstandings.
We need to remember that every job requires its own set of skills and training. Everyone on a team brings different experiences and has taken different paths in their careers. What we as data scientists see as basic might be new to specialists in other fields. It’s clear that this is where our misunderstandings come from, but it’s important to realize that it happens on both sides. Data scientists might think that other experts get the metrics we use to evaluate our work, while those experts might use terms and acronyms that we’re not familiar with.
As a result, having an analytics translator on a smart mobility project team is crucial. This person helps create an environment where asking questions and sharing information is encouraged, ensuring that everyone understands each other correctly. This role is key in preventing misunderstandings and confusion by bridging the gap between data science jargon and the practical needs of the project. It ensures that both data scientists and smart mobility experts can work together effectively, making the most of each other’s expertise.
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