Effective Communication skill is a must-have skill for data scientists, but it’s something that is easily overlooked. Lack of this skill can lead to inefficiencies in projects and misinterpretation of actionable insights, which overall can be costly for any company. Our job as data scientists/analysts isn’t just looking at the numbers. We are the people who provide actionable insights and come up with data-driven recommendations. After analyzing the data, we’ll need to interpret the results to the business stakeholders and help them take timely business decisions. As a result, being able to interpret your work to stakeholders and actually put your analysis and recommendation to use is extremely important.
Communication and public speaking skills are the areas I’m still working on improving. Of course, there is always something about ourselves we can improve on. The more we know, the more we realize we don’t know. In this blog, I want to share some tips I’ve learned from the Effective Training Solutions (ETS) training, and how this training has helped me better communicate with stakeholders and colleagues, and hopefully, you will find them helpful too!
I. Be there Comfortably
Believe it or not, simply by sitting or standing comfortably, you can instantly boost your confidence. When we smile, we tend to feel happier. The same may be true for confident body language. When we sit and speak comfortably as if we are talking to our friends, we may indeed feel more relax and confident. Ever since I started to apply this tip, I notice that my voice sounds more affirmative and confident. Being a soft-spoken person, sometimes I struggle with making my voice heard, and I usually have to make a conscious effort to speak louder. I thought if I speak louder, my opinion would be heard. However, it’s not about the volume, it’s whether the voice sounds indecisive or not. In fact, during the training, the trainer and the CEO of Effective Training Solutions, Ingrid Gudenas, helped us practice this, and it was an eye-opening experience to observe how this little adjustment can change how others see us dramatically. Try it and let me know if you feel the difference as well!
II. Be a Good Listener + Full Attention
What does it mean to be a good listener? A good active listener would engage with the speaker and ask questions. In school, we were not trained to ask stakeholders questions, because the questions were usually provided by the teachers. However, in solving real-world problems, we first need to understand the business problem the stakeholder is trying to solve, and then look for the data. Even if you have many years of experience working as a data scientist, you’ll still need to listen to the business context carefully, because you don’t want to waste hours working on an analysis and the stakeholder tell you that you misunderstood the request. I find it helpful to turn off any notifications when I’m in a meeting. This way, I can stay focused, avoid side conversations, and ask clarifying questions.
III. Make Eye Contact in Virtual Meeting
Being someone who onboarded virtually during the pandemic, I haven’t met any of my colleagues in person. At first, I found it hard to establish a strong relationship with colleagues. Virtual increases distance. Ingrid recommended us to look at the camera when we speak. This mimics making eye contact virtually. I find it extremely helpful, especially when giving a presentation in front of 30+ colleagues. Looking at the camera helped me feel more powerful and confident. And most importantly, I didn’t get distracted when I heard some background noise from the audience.
IV. Have Empathy
Have empathy for your audience and present them with the information they want in a format and language they can understand. First of all, we need to know what the audience cares about and what do they already know. We definitely don’t want to waste time repeating the same thing over and over again, especially when they already knew. Meanwhile, we also shouldn’t assume they already knew. It’s part of our job to know the audience.
Business stakeholders and executives don’t care which tools we used for the analysis, they care more about the findings, the direction of the business, and what they can do next; other data scientists/analysts/ML Engineers care more about your approach and if there is any trade-off. Once we understand the audience, we can then tailor the language and provide a targeted presentation. With non-technical stakeholders, we should avoid highly technical terms. Don’t assume everyone knows what you are talking about. If we can’t explain complicated results in laymen’s terms, we fail as good data scientists. Being a good data scientist is not about building cool models, it’s about using data to tell a story that helps come up with a strategic business plan.
V. Follow Up if There’s a Disagreement
Not gonna lie, it can be really hard to get people to change their values. In this case, we need to be even more patience. Disagreement happens all the time! Instead of thinking of it as an issue, consider it as an opportunity for you to better explain yourself. Misunderstandings can lead to disagreement. When there’s a conflict, it’s better to have a meeting instead of messaging. Don’t take it personally! Everyone has his/her own concerns. Be a good listener and have empathy. Try to understand why they disagree and explain your point of view calmly. At the end of the day, we are all trying to resolve the conflict.
During the meeting, both parties can discuss the issues and come up with a potential solution. This means that both parties make a verbal agreement and will work on the said solution. Isn’t this a happy ending? However, you waited for few more weeks and still haven’t heard anything from the other person. You might be wondering… I thought we had an agreement?🤔 Ingrid told us that we can actually tell whether the agreement is valid through body language and facial expressions. For example, avoiding eye contact, looking confused, lowering both eyebrows are the red flags that they still disagree with you. When this happens, acknowledge them until they are open to what you have to say. It’s very important to schedule a follow-up meeting to see how things are going with them.
VI. It’s Okay to Say, "I Don’t Know"
First of all, there is no shame in asking for help. Sometimes, we don’t know the answer because it would take time for us to investigate further before giving out a solution. You don’t need to feel like you know everything all the time. In fact, it’s encouraged for data scientists to ask questions. By asking questions, you are providing a different perspective, maybe you are not the only one who got lost. It’s better to say "I don’t know" than assuming we understand the question when we don’t, and providing a wrong answer. There are no stupid questions, but if someone already told you the answers multiple times, you should probably think before you ask the same questions again.
Final Thoughts
Effective communication is one of the most important skills to be a good data scientist. It takes time, effort and practice to develop this skill. The above tips I learned through the Effective Training Solutions (ETS) training have helped me improve my communication skill, and I really hope you can start seeing results too 🙂
