upset data scientist not communicating with his team

15 November 2022


Where Data Science Went Wrong

Picture this: It’s the year 2075. Everyone has their own flying car, a robot to walk their dog and take out their trash, and a teleportation machine in their backyard. Sounds pretty cool, right? 

While that future might not seem so impossible, when you think about it, that’s what the general public of the ‘70s thought today might look like. Instead, there are no flying cars in sight (in fact, we’re still working out a way to bring autonomous cars fully into the fray), and data scientists… Well, they’re still spending far too much time staring at stale pie charts. 

So, where did we fail? When did we stop reaching the expectations of the future? 

The answer is simple: We forgot how to talk to one another. 

In this blog, we’ll explain to you just how important communication is, especially to data scientists who have the skills and technology at their fingertips to change the world. 

The Importance of Communication & Collaboration to Data Science 

All data scientists want to be remembered for making a real impact—on their organizations, and on society as a whole. But, to do that, you’ll need more than just algorithms and data and machine learning models. You’ll need to rely on each other—your fellow data scientists, teammates, and members of cross-functional teams. 

Part 1: Communication 

Communication at work might seem to be a given, but for us data scientists (who have a reputation of spending all day coding in our parents’ basement), including other people in our model creation process might not come as easily as it should. 

I believe that a breakdown in communication is one of the core reasons why data science projects aren’t advancing as far as they could be. 

Data scientists, I encourage you to take this opportunity to understand the unique needs of different teams within your organization. I know you want to use flashy algorithms and try out the latest technology, but maybe the problem you’re working on would be better served by a simple k-means cluster or linear regression algorithm. 

In the end, what matters most is that the models data scientists produce are actually in line with the end users’ needs. You don’t want to waste time and resources creating something that doesn’t fit the use case. So, I urge you to communicate—listen to others’ needs, tell them what you can do, and create a solution that works for everyone. 

Part 2: Collaboration 

The second part of this equation is, of course, collaboration. You can be attuned to teammates’ needs (and fill them in on your perspective) all you want, but that alone won’t create a true sense of shared ownership over the end product. 

At RapidMiner, we believe that data science is a team sport. That means saying goodbye to your dark office in your parents’ basement and inviting other stakeholders into your projects. True collaboration goes beyond visibility into what you’re working on—though that’s always a good start. In a bona fide collaborative environment, everyone contributes, regardless of their data science skill set. 

For data science to reach its max potential, we need to integrate it into every business department. So, where do we start? With upskilling all our employees to develop analytical thinking and embed data-driven decisions into their everyday roles. 

Remember, have fun with it! Using data science to solve problems isn’t supposed to be a chore—it’s a power we can use for good! We can realize these dreams of the future with the technology in our hands today. 

To Wrap Up 

It’s time to say goodbye to the profession of “data scientist” as we know it. Gone are the days when data scientists operated in silos and presented their work to a group of bleary-eyed, confused executives—at least, that’s the way we think things should be. 

By fostering an open, collaborative environment, organizations can connect their people, expertise, and data to make an impact. And hey, maybe it won’t be flying cars on day one, but hopefully it will get us one step closer. 

Communication and collaboration are only two pieces of the puzzle. If you want to learn more about how we think the role of “data scientists” needs to change, check out our latest webinar, Requiem for a Data Scientist. 

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