Data Storytelling — Basic Data Visualization in Excel
Design and visualization are an important part of data stories. They help users to identify insights quickly. There are multiple charts, and graphs available to make informative and meaningful data stories.
What is Data Storytelling?
Data storytelling is the practice of building a narrative around a set of data and its accompanying visualizations to help convey the meaning of that data in a powerful and compelling fashion.
Data storytelling is a methodology for communicating information, tailored to a specific audience, with a compelling narrative. It is the last ten feet of your data analysis and arguably the most important aspect.
Evolutionarily, as Humans, we are naturally hard-wired to share stories as a means of sharing information.
Theorists even suggest that storytelling was the primary launchpad for the transmission of knowledge across large groups of people, which formed cultures as we know them today and allowed evolutionary success across generations.
Now, with the so much data available to us, only data storytelling can put a human perspective on the increasingly complex and rapidly changing world of the digital era.
What is Data Visualization?
Data visualization is the pictorial or graphical representation of data. It communicates the hidden information in data with images and charts. They are used to make important insights visually obvious. Data as visual narratives bridges the gap between data consumption and decision making.
Visualizations are also a vital part of a data story. They make the non-obvious insights visible on the screen and help in decision-making. In our earlier data storytelling blogs, we talked about easy steps on how to create data stories and tips to structure data stories. In this episode, we’ll show different types of data visualization that you can use to design your data story.
Why is Data Visualization Important?
Every dataset is different, and certain visualizations suit certain types of data. For instance, a line chart is perfect to show variations in data with respect to time. A pie or bar chart is the most suited to show categorical data.
Data visualizations allow users to identify patterns and trends in a single chart or a series of charts rather than exploring thousands of rows and columns in an Excel sheet. Even though data scientists and analysts explore meaningful insights from spreadsheets, it gets difficult to communicate them to stakeholders. That’s where data visualizations pitch in.
The Different Types of Visualizations
When you think of data visualization, your first thought probably immediately goes to simple bar graphs or pie charts. While these may be an integral part of visualizing data and a common baseline for many data graphics, the right visualization must be paired with the right set of information. Simple graphs are only the tip of the iceberg. There’s a whole selection of visualization methods to present data in effective and interesting ways.
Common general types of data visualization
- Charts
- Tables
- Graphs
- Maps
- Infographics
- Dashboards
More specific examples of methods to visualize data
- Area Chart
- Bar Chart
- Box-and-whisker Plots
- Bubble Cloud
- Bullet Graph
- Cartogram
- Circle View
- Dot Distribution Map
- Gantt Chart
- Heat Map
- Highlight Table
- Histogram
- Matrix
- Network
- Polar Area
- Radial Tree
- Scatter Plot (2D or 3D)
- Streamgraph
- Text Tables
- Timeline
- Treemap
- Wedge Stack Graph
- Word Cloud
- And any mix-and-match combination in a dashboard
Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote that creative data exploration.