Importance of Storytelling Skill for Data Scientists
In the vast world of data science, technical skills and proficiency in programming languages are undeniably crucial.
However, there is another skill that often goes unnoticed but holds significant value for data scientists – storytelling. While data scientists excel at crunching numbers and extracting insights from complex datasets, being able to effectively communicate these findings through compelling narratives is equally important.
In this blog post, we will explore the importance of storytelling skills for data scientists and how it can enhance their impact in the field.
Storytelling helps data scientists share their insights and understandings quickly, efficiently, and effectively. Data stories can be visual or narrative, but they all have one common goal: to help people understand what you’re talking about. Storytelling is taking data and turning it into a narrative that helps people understand it. A data scientist can make complex data more accessible and easier to understand by telling a story.
Data stories can be as simple as a chart or as complex as a storyboard. They are not limited to the data science industry but are just as valuable for other fields of study.
In earlier days, due to scant knowledge, the companies didn’t know how to extract meaningful and relevant information from this stored data. But the advent of data analytics has successfully bridged the gap between the company and this unpolished data. So, it can be concluded that data analytics have completely changed the company’s vision. By using comprehensive business analytics, companies can make the right decisions that will help them surpass their competitors.
The storytelling skill for data scientists include, but are not limited to:
1. Data interpretation skills
Data interpretation is about identifying the right variables, creating a correct data model, and choosing the right visualisation for your data. Data interpretation skills are different from data analysis skills. An example of a reasonable data interpretation includes: What data can the audience use to determine what kind of variable is essential? What is the meaning of a value over a certain threshold for some audiences?
2. Data manipulation skills.
Data manipulation includes, but is not limited to, the following skills: What is the correct way to organise the variables? What is the right time to visualise the data? How can the visualisation tell a story that is both informative and useful? What kind of data should be visualised with what type of chart? Is it possible to add an important variable to the visualisation to create a better story? What is the correct type of visualisation that can show different aspects of the data? What is the best way to combine data from different sources? How can you use real data instead of a simulation to validate the model?
3. Storytelling skills
How can the data tell the story to the audience? How can you tell a story with charts while still explaining the information of the data? What should be an interactive model, a static model, or even a static visualisation? What would be the best approach to demonstrating the results? How do you put the results in a personal context?
Role of Data Scientists in Storytelling
Since the importance of data analytics is burgeoning day by day, companies are appointing savvy professionals who will provide them with more comprehensive insights into structured data. A data scientist will be responsible for designing and implementing various processes and layouts for the complex and large-scale datasets used for modelling, data mining, and different research purposes.
What are the core responsibilities of a data scientist? Why have they become an integral part of every business?
Need to take care of those data which affect the organisation most
As a data scientist, your primary responsibility is to uncover the most relevant data that will assist your organisation make the proper decisions to expand your business and boost your revenue. In most cases, a data scientist delves headfirst into the data. He swiftly discovers all the crucial information and disregards the rest using his skill and knowledge. For example, if a corporation sells mobile phones, it will behove them to find out who is currently using them. Do they know how to get more people like them? These questions can only be answered by a meticulous data scientist, which is why businesses are increasing the number of data scientists on their payrolls.
Need to present data in such a way that anyone can understand it
- Though a data scientist should be well-equipped with all the technical and machine languages like R, Python, etc., he should present facile and more superficial data. Even a non-specialist can understand the insight from the data.
- A data scientist should never show a regression analysis or a plot from R because only a few people have adequate knowledge. Instead, he should present the data in a storytelling way that consists of simple slides and visuals instead of numbers.
- Visualising and communicating data are equally important, especially for budding companies making data-driven decisions for the first time or companies where these professionals are viewed as people who help others make data-oriented decisions. In this way, everyone in a company should understand which portions or departments need further improvement.
Help with promotions and other marketing strategies
A data scientist will also work coherently with the marketing team and help the company conduct fruitful campaigns and promotions, enhancing sales and profit.
If a mobile company has an idea of who its most engaged customers are, then a data scientist will help the company see what campaigns those members liked the most or what made them get involved so closely with the brand. By evaluating all these questions, a company can design its promotional campaigns and other marketing strategies to help enhance its customer base and visibility.
Nowadays, along with the marketing and production or service teams, these professionals are also the company’s pillars for growth. Hence, the companies commission more data scientists into their crew to go beyond their competitors.
Final Words
Storytelling is something that everyone has to go through to understand that what you have is a gift and a treasure to give to the world. Data is a treasure, and I love and enjoy the fact that I am trying to add some value to others’ lives with the information I gather. I don’t know how old you are, but I am sure you have already heard of stories like the one about the frog and the well because it is the kind of story passed down to everyone.
Storytelling is something that everyone should strive to achieve. Data science is storytelling with numbers. If you don’t understand the information you are gathering and not telling a story with it, your data is of no value. The best story that I know is how to get more business.