Data storytelling combines a variety of topics to develop an engaging and impactful narrative from data. You will likely get 10 different answers if you ask 10 different people. And just like ice cream, it can have multiple flavors. The chart below covers some of the key areas of the “vanilla” flavor that I created for the Storytelling with Data course I created at the University of Toronto .
Technical Skills
- Data analysis: This involves working with data in the ways below:
- Data Cleaning: More often than not, data is not ready and usable for the purposes needed and intended. This is a surprise for first-time data analysts and usually takes more time than most people think.
- Data Exploration: Once the data is cleaned and ready to be used, the analyst needs to explore to find interesting and new insights. A lot of the time, it is like exploring a new area for the first time and what we find interesting depends on the key question we are trying to answer.
- Data Explanation: Exploration can lead to insights that we can choose to investigate and explain further
- Data Science: Many data storytellers need in-depth programming and statistics knowledge to be able to analyze the data more scientifically and statistically. This are is one of the hottest fields of the 21st century.
- Machine Learning: The power of machine learning is its ability to digest large data sets, find patterns that it can apply to similar data sets, and provide predictions, explanations, or even new creations that simply wouldn’t exist before.
- Software Development: Software development is baked into most of the areas above in various forms.
- Data Visualization: A visual is worth a thousand words. Visualization is the powerful tool of summarizing findings for our audiences in a way that is interesting, engaging, and informative.
Non-Technical Skills
- Storytelling: Communicating the insights in a way that is emotionally engaging and resonates with the intended audience is what drives the results. Storytelling is where science meets art.
- Design Thinking: Getting the perfect ingredients and balance of art and science is extremely hard. Design thinking advocates for understanding the audience, ideating potential stories, experimenting with them, and refining until we get things right.
- Domain Knowledge: Knowing the domain that the data belongs to is essential in understanding what the data shows.
- Problem Solving: Asking the right questions is a fundamental skills for data storytellers. With the large data sets available to us these days, working with data can become an intimidating and fruitless effort.
Each one of the skills above can take years or decades to learn. Which ones do you have a head start in? Which ones can bring the highest return for you?
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