- Terminology (A to D)
- AI Capability Control
- Bayes Theorem
- Big Data
- Chatbot: A Beginner’s Guide
- Computational Thinking
- Computer Vision
- Confusion Matrix
- Convolutional Neural Networks
- Data Fabric
- Data Storytelling
- Data Science
- Decision Tree
- Deep Learning
- Deep Reinforcement Learning
- Diffusion Models
- Digital Twin
- Dimensionality Reduction
- Terminology (E to K)
- Edge AI
- Emotion AI
- Ensemble Learning
- Ethical Hacking
- Explainable AI
- Federated Learning
- Generative AI
- Generative Adversarial Network
- Generative vs. Discriminative
- Gradient Boosting
- Gradient Descent
- Few-Shot Learning
- Image Classification
- IT Operations (ITOps)
- Incident Automation
- Influence Engineering
- K-Means Clustering
- K-Nearest Neighbors
- Terminology (L to Q)
- Terminology (R to Z)
Table Of Contents
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 getting prevalent as companies realize the importance of data-driven insights.
According to U.S. BLS Occupational Outlook Handbook 2021-2031, these job roles are experiencing considerable growth:
|Job Role||Job Growth||Median Salary|
|Market Research Analyst||19%||$63,920|
|Operations Research Analyst||23%||$82,360|
These analysts employ various data storytelling techniques to carry out effective analytics operations. Let’s discuss what data storytelling is, its major components and benefits, and if you are an analyst, how can you become better at data storytelling.
What Is Data Storytelling?
Data storytelling involves analyzing data using visual and compelling narratives to communicate data insights to stakeholders. A data storyteller explains the “why” in the data using visualization. The aim is to explain the data attributes clearly and provide a meaningful context for what that data represents. Presenting the underlying insights in data and trends is necessary for effective decision-making.
For example, a financial analyst can show a candlestick chart to investors to demonstrate the price movement of a stock or asset. A candlestick chart visualizes the historical stock patterns using four trading indicators (“open price,” “close price,” “high price,” and “low price”) to predict the upcoming market trend.
For a better understanding, look at the bitcoin price candlestick chart below. The graph visualizes bitcoin prices for the first two months of 2023. The green bars represent an increasing price trend, while the red bars show decreasing bitcoin price trend.
A crucial data storytelling aspect is that data storytellers need to understand the business context and stakeholder requirements. Research shows that 60% of the investment done in data analytics goes to waste because the insights obtained do not align with decision-making and business goals. As a result, decision-makers only use 22% of the data insights they receive.
3 Major Components of Data Storytelling
Data, visuals, and narrative are the three main components of data storytelling. Let’s explore them further below.
- Data: Data storytellers gather and preprocess the data they need to tell a story. They perform statistical analysis and visualize key trends and patterns for thorough data analysis.
- Narrative: Creating an engaging story and providing context to the key findings obtained from data is called narrative. A good narrative inspires the audience to take action.
Thomas. H. Davenport, a thought leader in business management, says:
“Narrative is the way we simplify and make sense of a complex world. It supplies context, insight, interpretation – all the things that make data meaningful and analytics more relevant 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, images, or videos.
A data analyst can use a data storytelling framework like characters, setting, conflict, and resolution to tell a compelling story. For example, in the e-commerce domain, characters can be customers, the setting is a company struggling with customer retention, conflict can be an increasing churn rate, and resolution is the set of steps the data storyteller suggests to reduce the churn rate.
How Can a Data Analyst Get Better at Data Storytelling?
Understand Your Audience
Understanding the audience is the key to compelling data storytelling. If you are talking to business executives, it would be significant to provide them with high-level analysis and actionable insights for business strategy. But when talking to the team, you must explain the methods used to reach a conclusion in detail.
Choose Appropriate 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 data and how many variables you have to consider. Select the best visualization to convey your idea.
Declutter the visualization by aggregating or removing information that is not required. For example, in the charts below, WGM, WIM, WCM, and WFM are the leading women titles in chess; the remaining data can be aggregated as “others”.
Use Vibrant Colors
Use color palettes accessible to everyone, including those who are visually impaired or color blind. Keep contrast in colors and avoid using the same colors next to each other. For instance, in the bar charts below, the color combination in the first chart can be hard to distinguish compared to the second chart.
What Are the Benefits of Data Storytelling for Organizations?
Promotes Data Literacy Among Employees
Data storytelling can enhance the data literacy of the employees in the organization. According to a survey by Accenture and Qlik, only 21% of employees feel confident in reading, analyzing, and discussing data. Hence, compelling data storytelling encourages them to explore and discuss data within the organization.
Create Engaging & Valuable Experiences for All Stakeholders
Understanding and grabbing 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 facts. Hence, telling data stories to your product users or shareholders using compelling narratives and visualization can be highly engaging and valuable.
Compelling data storytelling provides a new perspective or uncovers hidden aspects. It communicates what needs to be done. It allows 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 keeps increasing exponentially and getting more complex, data-driven storytelling is becoming an essential skill.
In an organization, the role of data storytellers is performed by data analysts or data engineers. Tools such as Tableau and PowerBI enable data analysts to build compelling visualizations and dashboards without much effort. In fact, Gartner estimates that by 2025 most of the data stories will be automatically generated.
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