On simplicity in data scientist communications Everything should be made as simple as possible, but no simpler - paraphrase of A. Einstein I spent this past week at the 2024 INFORMS Analytics conference. One of the major themes across both the speakers, the panels (of which I was a panelist), and conversations in the halls was soft skills for data analysts/Data Scientist. Data scientists are subject to the same stereotype that is used with all those who are technical specialists, that they lack soft skills and this prevents our end stakeholders from understanding and being able to take advantage of the knowledge and capability we bring to our organizations. And that the most commonly provided solution is to ask the specialists to simplify the delivery. But I don't know if this is the right solution. A better direction would be, as John-Eric Bonilla described it, the data scientist acts as a translator, the person who takes the aggregated insight of the data and of the subject matter experts throughout the organization and translate that into the framework of the decision maker. This is a tall order, but this is the reason that Drew Conway in his Data Science Venn Diagram gives subject matter expertise equal weight with the math/stats and the computer skills that get so much prominence in these discussions. When I was deployed in Afghanistan, a brief that I prepared was being pushed up to the Commanding General. Members of the General's staff was present at the last brief, and their comment was that it was a good brief, but I needed to redo it in their format. And they gave me a highly specified template. Now, I could be judgmental and say that it met no conceivable definition of "simple", but I won't. Because the purpose of the template was to present the information inside the framework that that particular General uses to process information for the purpose of making a decision. And that specificity of presentation, that the recipient can process the information using the framework they have as an expert in their area and make a decision, is the goal of technical communications, such as data science. . . . Is being a translator easy? No. But I have found on my projects is that the data scientist is often the first person who realizes all of the people who are actually involved in an activity, because the data scientist is tracing all of the data elements. So the data scientist needs to learn everyone's language to get a good picture on what is actually happening, and communicate to the decision maker in a format the decision maker can understand. Yes, this is hard (80% of data projects fail, and while vendors use this to market products, those who investigate that number say it is mostly communications). But to be useful, we must. Full version
Thanks for sharing. Well-said!
Business Intelligence Analytic Developer
2moThank you for sharing your thoughts and for the mention! It’s fascinating how data science, much like a complex tapestry, requires us not only to weave technical skills but also to embroider with soft skills that bring out the full picture for everyone to appreciate. As a panelist at the INFORMS Analytics conference, I was reminded of the classic quote from Einstein about simplicity—indeed, everything should be made as simple as possible, but not simpler. This balance is what we, as data scientists, strive for; not to simplify, but to clarify. Being a translator isn't just about converting data into decisions; it’s about tailoring the story so intricately that it fits the cognitive wardrobe of every decision-maker perfectly. Remember, the template might not be simple, but if it's the general’s battle gear, then it's our job to ensure it fits them perfectly. Here's to all the data scientists out there, tailoring complexities into actionable insights—stitch by stitch, byte by byte. Keep translating, keep transforming, until we transform the World!