Why a data scientist needs to also be a storyteller
By: Angelica Lo Duca
Tags: Communication, Storytelling, Data Journalism, Storytelling With Data, Data Science

Data Science Discussions

Image by Lukas Bieri from Pixabay

Working as a data scientist is a fantastic job: you can collect data, through wonderful scraping mechanisms or Web RESTful APIs. Then you can build fantastic datasets or exploit existing ones. You can manipulate data before analysing them through preprocessing strategies, such as replacing missing values, standardizing, normalizing, binning and formatting data. You can also build perfect estimators to predict the trend of your data for the next ten years. You can even build wonderful time series predictors through ARIMA and SARIMA models. Finally you can build extraordinary visualizations and graphs. But ultimately your work may be totally useless.

You may have wasted time.

You could counter my position, justifying yourself and saying that your work is correct, that the accuracy, precision and recall of your predictors is almost 100%. You are sure you have not made any mistakes and that your work is novel. Nobody has reached your conclusions yet.

You are right. Your work is novel and fantastic. You are a very competent data scientist. But a data scientist alone is useless.

Why?

Imagine that you work as a data scientist for a powerful company that produces thousands of copies of a certain product every day. Imagine that your boss asks you to analyze the sales trend of this product, in order to increase sales.

As a good data scientist, you sit down at your computer and start analyzing your beautiful dataset containing the data of the last 10 years of product sales trends.

You start with your analysis and get some surprising results: your ARIMA model shows that the product sales will decrease in the next two months. You are sure of your result because the model accuracy is around 99%.

You go to your boss and you show him the graph of the accuracy of the results, built with the most powerful visualization tools (d3.js, seaborn etc.). You are very enthusiastic thus you also show him the precision and recall charts, made with the same tools.

Your boss listens to you attentively. Everything seems to be fine. But after a week, you get the dismissal letter. Yes, your boss fired you.

What went wrong?

The problem is that your boss is not a data scientist, thus he does not know anything regarding precision, recall and accuracy. In addition, he does not know anything about d3.js and seaborn and other geek stuff. He wants to know only how to make money in the next months.

Communication abilities is what is missing in your work. Yes, you need to communicate your results in a proper way. This is why a data scientist cannot live alone. He needs someone who translates his work in to a more understandable language. Three aspects must be considered for a good communication:

it must be clear what you want to communicate

well speaking abilities

work in a team

interdisciplinary work.

What can you do if you work alone?

If you don’t have the possibility to work in a team, you must develop at least some basic communication skills:

understand the context

decluttering

focus attention

build a story

Understand the context

Firstly, you must think about your interlocutor. Who is your work aimed at? Who will read your results? The presentation of the results depends a lot on this. You need to speak the language of the interlocutor. It is necessary to get out of one’s self-referentiality. The audience is smarter than you think.

At least, three basic interlocutors could be identified:

public audience — In this case, it is sufficient to build a video for Instagram or Youtube. You can exploit some basic tools such as Adobe Spark, which provides you with some basic features to present your data;

— In this case, it is sufficient to build a video for Instagram or Youtube. You can exploit some basic tools such as Adobe Spark, which provides you with some basic features to present your data; your boss — For your boss, you can build a presentation, such as a power point presentation or a google scholar presentation;

— For your boss, you can build a presentation, such as a power point presentation or a google scholar presentation; your personal portfolio — You could enrich your personal profile, for example on LinkedIn, by building a custom web site, where you show the results of your research. In this case you could exploit a CMS like Wordpress or other Javascript libraries, such as Bootstrap.

Decluttering

To convey a message through a graph, it is necessary to reduce the amount of information it carries as much as possible. In other words, you should declutter your graph. Your motto should be less is more: the less information a graph provides, the easier it is to understand it.

Focus attention

It is very important that the interlocutor does not get distracted while you present your results. For this reason, you should pay attention to some preattentive attributes, such as size, color, and position of graphs on page. Furthermore, your speaking skills should guide the interlocutor to process the information exactly as you want him to process, that is, in the order you have established.

Build a story

Although you have developed some skills build wonderful graphs, you must enhance also textual elements. You don’t need to write papers and papers of words, but text is always mandatory to understand data. Graphs without text are useless, thus you need to balance the textual and visual parts.

Summary

In this brief article I have discussed on the importance of communication. A data scientist is able to process data but he does not necessarily have the skills communicate them to others.

Communication requires many skills, of which the basic are: understand the interlocutor, decluttering graphs, focus the interlocutor attention on what you want and, finally, build a story.

If you want to be a good data scientist, you must be also a good storyteller or a data journalist. Definitely, what people want is not data, but stories.

If you are interested in this topic, you can also read my previous article on how to design a data journalism story.

If you wanted to be updated on my research and other activities, you can follow me on Twitter, Youtube and and Github.