In today’s data-driven world, data storytelling is becoming increasingly important for decision-making and business growth. Data analytics roles such as market research analyst, financial analyst, and operations research analyst are becoming more prevalent as companies realize the importance of data-driven insights.
According to the US BLS Occupational Outlook Handbook 2021-2031, these job roles are experiencing significant growth:
These analysts use a variety of data storytelling techniques to perform effective analytics operations. Let’s discuss what data storytelling is, its key components and benefits, and if you’re an analyst, how you can become better at data storytelling.
What is Data Storytelling?
Data storytelling involves analyzing data using visuals and compelling narratives to communicate data insights to stakeholders. A data storyteller explains the “why” in data using visualization. The goal is to clearly explain the characteristics of the data and provide meaningful context for what that data represents. Uncovering underlying data insights and trends is essential for effective decision making.
For example, a financial analyst may present a candlestick chart to investors to show the price movement of a stocks or wealth. A candlestick chart shows historical stock patterns using four trading indicators (“open price,” “close price,” “high price,” and “low price”) to predict upcoming market trend.
For a better understanding, see the bitcoin price candlestick chart below. The graph depicts bitcoin prices for the first two months of 2023. The green bars represent an increasing price trend, while the red bars show a decreasing bitcoin price trend.
An important aspect of data storytelling is that data storytellers need to understand the business context and stakeholder requirements. Research shows that 60% the investment made in data analytics is wasted because the insights gained do not align with decision making and business goals. As a result, only used by decision makers 22% of the data insights they receive.
3 Key Components of Data Storytelling
Data, visuals, and narrative are the three main components of data storytelling. Let’s explore them below.
- Data: Data storytellers gather and preprocess the data they need to tell a story. They perform statistical analysis and identify key trends and patterns for thorough data analysis.
- Narrative: Creating an engaging story and providing context to key findings gleaned from the data is called narrative. A good narrative inspires the audience to take action.
Thomas. H. Davenporta thought leader in business management, says:
“Narrative is the way to simplify and make sense of a complex world. It provides context, insight, interpretation – all the things that make data and analytics meaningful and interesting.”
- Visuals: A picture is worth 1000 words. Visualization adds weight to the narrative and creates an impactful data story. Visuals can be in the form of graphs, pictures, or videos.
A data analyst can use a data storytelling framework such as characters, setting, conflict, and resolution to tell a compelling story. For example, in the e-commerce domain, the characters might be customers, the setting might be a company struggling with customer retention, the conflict might be an increasing churn rate, and the resolution might be the set of steps suggested. of data storyteller to reduce the churn rate.
How Can a Data Analyst Become Better at Data Storytelling?
Understand Your Audience
Understanding the audience is the key to compelling data storytelling. If you’re talking to business executives, it makes sense to provide them with high-level analysis and actionable insights for business strategy. But when talking to the team, you should explain the methods used to reach a conclusion in detail.
Select Applicable Visualizations
Data visualization highlights different aspects of data, such as;
- Comparison (Bar chart, line chart)
- Relationship (Scatter plot, bubble chart)
- Distribution (Histogram, scatterplots)
- Composition (Waterfall chart, stacked area chart)
Understand what you are trying to achieve with the data and how many variables you must consider. Choose the best visualization to convey your idea.
Declutter the visualization by combining or removing unnecessary information. For example, in the charts below, WGM, WIM, WCM, and WFM are the top women’s chess titles; the rest of the data can be grouped together as “other”.
Use Vibrant Colors
Use color palettes that are accessible to everyone, including the visually impaired or color blind. Maintain contrast in colors and avoid using the same colors next to each other. For example, in the bar charts below, the color combination in the first chart may be difficult to distinguish compared to the second chart.
What Are the Benefits of Data Storytelling for Organizations?
Promoting Data Literacy among Employees
Data storytelling can improve the data literacy of employees in the organization. According to the survey by Accenture and Qlik, only 21% of employees feel confident reading, analyzing, and discussing data. Hence, compelling data storytelling encourages them to explore and discuss data within the organization.
Create Engaging and Valuable Experiences for All Stakeholders
Understanding and capturing the audience’s attention is critical to effective communication. The human brain processes visuals 60,000 times faster than text, and people remember stories 22 times more than the truth. Therefore, telling data stories to your product users or shareholders using compelling narratives and visualizations can be highly engaging and valuable.
Influence on Decision Making
Compelling data storytelling provides new insight or uncovers hidden aspects. It tells what needs to be done. This enables stakeholders to make informed decisions and take action regarding their business strategy.
Data Storytelling – Way Forward for Data Analysts
Data storytelling is the art and science of communicating insights about data. As data continues to grow and become more complex, data-driven storytelling becomes an essential skill.
In an organization, the role of data storytellers is played by data analysts or data engineers. Tools like Tableau and PowerBI allow data analysts to build compelling visualizations and dashboards without much effort. In fact, Gartner it is estimated that by 2025 most data stories will be generated automatically.
Data analysts must stay in touch with the latest trends and tools in data analysis industry to share impactful data. For more AI related content, visit unite.ai.