Telling stories with data and visualizations – Some key messages

The topic of telling stories from data is huge and probably needs many many hours and books to explain the ideal ways of doing it. But Dr. Roberto Martinez did a great job in giving us a quick introduction to the topic and its pragmatic application in an hour at his talk at the UTS LX lab. It very much aligned with the Connected Intelligence Centre‘s  vision of building staff capacity in data science particularly by keeping human in the center of the data. This post includes my notes from this talk where I summarize some of the key messages.

Humans are producing enormous amounts of data these days. According to recent statistics, 2.5 quintillion bytes of data are created every day and the pace keeps growing. But, there is a stark contrast between data and knowledge – Data by itself means very little, and knowledge is created only when the data is made sense of. We might be drowning in data, but not in knowledge. Roberto compares this abundance of data to oysters and an insight to a pearl. We need to open many oysters to maybe find one pearl.

The rest of the blog is divided into two main sections 1. Data Storytelling, 2. Data visualization, and a few overall key messages that I took away from the talk.

Data Storytelling:

The value of data is not the data itself, but how we present it. This is what makes storytelling really important to present insights from data. It is not about presenting ALL the data we have, but to highlight the main insights from the data that should be noted. It is about finding patterns from the data to make people engaged with the story just like finding hooks in a fictional story. It often operates in conjunction with data visualization to communicate results from data. Check out the list of resources given at the end of this post for detailed reading.

There are a few ways to make the insights clear and pop out when communicating the story from data:

  • The first step is to declutter the data by removing all the noise. This can be done by stripping down all the unwanted information and building up on the useful insights.
  • The next key thing to do is to foreground things that are important. We do not want too much ink/ data that makes the results too complicated to understand.
  • A data story approach can be used merging narrative and visuals together to engage audience and point to key messages from the data (see examples of line graphs annotated this way here). Also check out this interesting article and podcast on the good and bad of storytelling for further reading.

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