Introduction
Communication in the workplace is extremely important in any profession, especially in data science. It is so important in data science, in fact, that some people are arguing that it is more important than technical skills like coding and machine learning. One of these such people is Karen Church and she wrote an article undermining these concepts based on a keynote speech she gave at Women in Data Science(the blog post is linked here).
Summary Of Article
The article details 5 reasons why the ability to communicate effectively is just as important, if not more, compared to the technical aspect of data science and lists tips to improve in each of those 5 ways. Firstly, communication helps you understand and translate business requirements. The article says that impactful data science projects often are a part of bigger business requirements and the ability to communicate can help the data scientist better understand these goals and be better aligned with them. To improve this the article suggests to ask lots of questions and invest time into understanding what they need out of this project. Next, the article focuses on how communication helps you frame or reframe problems. This builds on the last point because framing problems comes from the outcomes that the shareholders need from your project. A well-framed problem can set the team up for success but this requires good communication with shareholders. To improve always try to understand the problem at hand and use the 5 why’s. Then the article gets into the 3rd reason – communication helps you collaborate better. With more effective communication team members can better share ideas and give feedback. To improve this, you and your team can get together to establish guidelines on how to facilitate effective communication. Next, the article gets into how communication can help you present results and insights effectively. The use of effective data storytelling can help stakeholders gain insight into your insights or any other deliverable associated with the project much better and clearer. Effective communication is at the heart of this and some ways to improve are taking time to understand your audience, including relatable and humanistic anecdotes to make it easier to understand, and choosing the right visualizations to support your narrative. Finally, the author gets into the last use case of communication – to help influence and persuade people. When you present your insights, without the following action required(invest more into this marketing campaign, make this new product, or offer your product in this new market) is essentially useless. To get people to do actions you need to have influence which is where effective communication comes in. To help improve this you can build credibility and trust, and be clear and concise. The author wraps up the article by saying that although the technical aspects are important in data science so are communication skills and they can help you become much more valuable in the workplace.
My Take
Overall, I really liked this article. I liked the consistent way it was written to help the reader follow along. I also liked the actionable tips to get better at the concepts discussed in the article they really make the article worthwhile to read. One more thing I liked about this article is how the insights go in order of the project. For example, first, it starts out by translating business requirements then it gets into problems which is the next step of the data science process, then collaboration(actually working on the project), then presenting insights, and finally acting on what you have found. I found this level of organization really helpful in my understanding.
Conclusion
All in all, this article was a good one. Due to its actionable insights and chronological formatting, it made for a really easy and enjoyable read. I highly recommend you read it(article linked here)