Data is everywhere, but it is what we do with that data, what insights we tap into that make it so valuable. That is why data storytelling is becoming so important. Whether we realize it or not, we are bombarded with an onslaught of visual information every day. From print advertisements and television commercials to stop signs and green lights, the world around us relays a constant stream of data. These are often accompanied by some kind of visual representation to help us absorb it quickly.
With the rise of digital business and data-driven decision making, data storytelling has become a much-talked-about skill often associated with the data science and analytics community. Data storytelling is the process of translating data analyses into layman’s terms in order to influence a business decision or action.
Some of the steps required to follow for effective data storytelling
• Understanding the importance of context and audience
• Determining the appropriate type of graph for your situation
• Recognizing and eliminate the clutter clouding your information
• Directing audience’s attention to the most important parts of your data
• Thinking like a designer and utilize concepts of design in data visualization
How To Identify Stories Using Data
By identifying trends: Trends are indicators that there is a general direction in which something is changing or developing, and it’s something you should look out for in data. For example, are people using their PCs less as compared to tablets and smartphones? Or is there a growth in online shopping in a certain region?
Using rankings: Rankings tell a story using data about the relationship between items on a list. For example, is Vienna being ranked number one in the list of most livable cities? While Vancouver is in second place?
Draw comparisons: Comparisons tell a side-by-side story between either polar opposites, or very similar things. For example, how are two companies performing on the stock market. How much more dedicated to work-life balance is India, compared to Netherlands?
Look for surprising or counter-intuitive data: Data that challenges previously confirmed knowledge tells a great story. For example, the sale of pop tarts apparently increased seven-fold before a hurricane. Seems surprising, but if we dig deeper – it seems that in anticipation for a natural disaster, people seek out for comfort foods.
Point out the relationship between data points: Relationships between data points tell a story by showing a connection or correlation between a number of variables – like the popularity of bitcoin and expensive utility bills. Example: the influx of bitcoin mining companies shifting to Canada is leading to rising costs in energy prices for local residents.
Visuals required to Support Data Storytelling
Once the structure and theme of story is done, it’s time to curate the right assets. This is done by incorporating icons and images, paired with the data and message. There are many visualization tools available nowadays. One of the most common tools among them that help storytelling with data is tableau. It can incorporate varieties of pictures, matrices, graphs, points, charts, layouts, highlights and so on. It also has a mapping functionality and is able to plot latitude and longitude coordinates and connect to spatial files like Esri Shapefiles, KML and GeoJSON to display custom geography.
Thus, with proper interpretation of the content, along with appropriate diagrammatic representation, storytelling of data can be done effortlessly.
