Data Storytelling: How to Master the Core Skill set of Data Science
Data storytelling is the process of using data to create a narrative that effectively communicates insights and findings to an audience. It is a critical aspect of data science as it helps to make sense of complex data and enables people to make better decisions based on that data.
Let’s take a practical example to understand data storytelling in data science. Suppose you are working as a data scientist for an e-commerce company, and you have been asked to analyze customer data to identify trends and insights that can help increase sales. You are given a dataset containing information on customer demographics, purchase history, and website interactions.
First, you will need to explore the data to identify interesting patterns and relationships. For example, you might discover that customers who spend more time on the website tend to make larger purchases, or those customers who purchase items in a certain category are more likely to return to the website to buy more products.
Next, you will need to organize your findings into a compelling narrative that tells a story. This might involve creating visualizations such as charts, graphs, or maps to highlight important data points or trends. For example, you might create a chart that shows the correlation between time spent on the website and the average purchase amount.
Finally, you will need to communicate your insights and findings to your audience clearly and engagingly. This might involve creating a presentation that combines your visualizations with a written narrative that explains your findings in plain language. For example, you might explain that customers who spend more time on the website tend to make larger purchases because they are more likely to discover new products and take the time to explore the website.
Data storytelling: Why Is It Important?
Data scientists analyze and interpret data for a living. In general, raw data is difficult to understand, of little use, and likely to raise more problems than it does solutions. Data scientists may give context and close the knowledge gap between what the analytics shows them and what the audience must understand by telling a data story. Data storytelling can ultimately motivate teams, inspire leaders, alter companies, foster innovation, and fend off objections to transformation because the story that surrounds the data is more potent than the data itself.
What are the steps in Data Storytelling?
Here are the typical steps of data storytelling:
1. Identify your audience
2. Collect and analyze data
3. Develop a narrative
4. Select visualizations
5. Create a presentation
6. Deliver the presentation
Necessary skills to become a successful Data Storyteller
The skills required to be a great data storyteller go beyond just technical expertise in data analysis. Here are five essential skills that data storytellers should possess:
1. Audience awareness:
Great data storytellers must understand their audience and tailor their stories to meet their needs. They must consider the audience’s level of knowledge, interests, and objectives to present data insights in a way that resonates with them.
2. Business acumen:
Data storytellers must understand the business problem they are trying to solve and the context in which the data insights will be used. They should be able to identify the impact of their insights on the business and use this knowledge to deliver actionable recommendations.
3. Critical thinking:
Great data storytellers must be able to analyze data from multiple angles, identify patterns, and draw meaningful insights. They should be able to think critically about data and recognize when data trends are driven by anomalies or outliers.
4. Data management:
Data storytellers must have the skills to access, organize, and manipulate data. They should be able to identify relevant data sources, clean and transform data, and choose the right visualization tools to communicate their insights.
Data storytelling requires excellent presentation skills. Great data storytellers must be able to articulate their insights and findings clearly, using visual aids and language that is easy to understand. They should also be able to field questions from the audience and engage in productive discussions.
It is more crucial to introduce simplicity as data becomes deeper and more complex. This makes effective storytelling a highly important part of business intelligence (BI) solutions. Join our Data Storytelling Course using Python and learn how to transform complex data into compelling narratives. Discover how to use Python libraries such as Matplotlib, Seaborn, and Plotly to create data visualizations and develop engaging presentations. Start your journey towards becoming a skilled data storyteller today!