Planning for success

1. Planning for success

Welcome back! Now that we've reviewed some of the basic concepts related to data storytelling, it's time to review a sample workflow that you could use to develop your own storytelling products. As we mentioned in the previous lesson, here we will focus on visual storytelling aided by data visualizations, such as charts.

2. A sample workflow

The workflow for data storytelling can vary depending on your specific use case. Here, we present a sample workflow consisting of five main steps. These are: planning and sketching an endpoint, simulating and considering the simulated data, acquiring and preparing the actual data, exploring and understanding the data and finally, sharing your results. In the following slides I will briefly describe each step and its importance. If you want to learn more about this sample workflow and see some examples using the R programming language, I recommend reading the book Telling Stories with Data by Rohan Alexander.

3. 1. Plan and sketch an endpoint

First, there's the planning and sketching step. If you have ever worked on a deliverable in any area, you might already realize how trying out new ideas can help you come up with a novel solution. While experimenting can be very useful, we cannot walk aimlessly for long. Planning and sketching an endpoint forces you to think about the ultimate goal and consider what you want to achieve from your data storytelling project. This step can help you focus your efforts and ensure that your project stays on track. So, take the time to plan and sketch your endpoint before moving on to the next step.

4. 2. Simulate and consider the simulated data

Welcome to step two of the data storytelling process. This step involves simulating data to help you understand the details of your data storytelling product. By simulating data, you can identify errors in your data visualization and statistical models before investing time and resources into the actual data. Additionally, it is more cost-effective to realize mistakes when working with simulated data rather than actual data. So, make sure to look into the details of your data storytelling product and use simulated data to identify any potential issues before moving on to the next step.

5. 3. Acquiring and preparing the data

Now it's time to move on to step three of the data storytelling process - acquiring and preparing the data. This step can be one of the most important and often difficult stages of the entire process. You may find that there is not enough data or, conversely, too much of it. This can be a challenging task that often requires the expertise of a data scientist. Remember, acquiring and preparing the data is a crucial step that sets the foundation for the rest of your data storytelling project. So take the time to gather and prepare your data carefully to ensure that your end product is accurate and informative. As you work on more data visualizations, you will become more familiar with your specific domain and develop a kind of sixth sense of what kind of information will be available to you. So do not despair if, at first, it seems overwhelming. This will get easier over time!

6. 4. Explore and understand

Now that you actually have some data to work with, it's time to explore and understand your data. In this step, you will explore and understand your dataset, in order to extract the relevant insights that will be useful to enhance decision making. This involves getting to know your data, identifying relationships, calculating descriptive statistics, and performing statistical models to answer questions. As you become more familiar with your dataset, you will begin to uncover patterns and trends that can inform the story you want to tell with your data visualization. This step is critical to ensuring that your visualizations accurately represent the insights that you want to communicate.

7. 5. Share what was done and what you found

Finally, here comes the story part in your storytelling. While all of the previous steps are very important to deduce insights from your data, they really become useful once they are available for others to discuss and make decisions. This is where all the pieces fit together - the statistics, the data visualizations, and the narrative - to create a cohesive story. To effectively communicate your findings, make sure to take into account the following questions: What decisions did you make when analyzing the data? Why did you make them? And what did you learn from the data?

8. Let's practice!

Now, it's time to put this knowledge into action! In the following exercises, you will review the steps of the data storytelling workflow. Good luck!

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