What Are the Top 5 Data Storytelling Techniques?

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Without these techniques, just tools/ platforms won’t ever deliver any value

Just the availability and access to mature data storytelling tools with advanced features do NOT guarantee their leverage.

What are the techniques that efficient and effective data storytellers must hone and embrace, to be able to justify the investments in the data visualization tools and platforms?

Here are the top 5 techniques to build effective data stories that will resonate with senior business leaders and will add demonstrable value to their decisions and actions:

Using a story-building questionnaire/ template
The 5 ‘So-what’ (deductive logic) or ‘What then’ (inductive logic) Questions
Build the Big Idea technique
Storyboarding
3-minute stories with the business/user teams

Exploratory vs. explanatory data storytelling techniques:

Techniques 1 & 2 are exploratory, QnA-based data storytelling techniques. As the category name suggests, they are applicable in scenarios where the data storytellers don’t already have a clear picture of the business problems and users’ targeted outcomes and expectations.

These scenarios and hence the techniques are similar to the DevOps/ agile methodologies in software/ solutions development and delivery.
Here, the users/ customers’ feedback is sought at every stage starting from requirements elicitation to design to code-build to test cases and UAT.
In these methods, the customers and users should be able to relate to the questions as relevant and useful in their specific problems, decisions, and actions context.

Techniques 3 to 5 are more explanatory and applicable to scenarios where the data storytellers already have a pretty good idea and confidence about what stories and answers the business stakeholders and customers/ user groups want to hear.

1- How to use a Data-story development questionnaire/ template

A good data story ALWAYS starts from the right questions, that the story will provide answers for. So, the first and foremost technique for effective data story-telling lies in the story developers’ ability to ask the right questions. Here is an example data-story building questionnaire:

Who is the targeted audience/ persona for this data story? What are their expectations from the data science programs/ projects?
What decisions are these targeted personas going to need support for, from the data stories?
What metrics for the target persona/ role will be most impacted by the accuracy and quality of insights presented in the data stories?
What are the risks and constraints that the target persona/ roles experience / envisage, that must be covered as critical dimensions of the data stories built for them?
What could be the potential sources of decision biases that the target persona/ roles may have, which may raise their concerns?
What are the data sources that data story builders need, to construct the data stories in a 360 manner?
What’s the priority, in terms of data criticality and ranking of insights visualization?
What will success look like, in a data story?

2- The ‘So-What’ / ‘What Then’ questions technique

Many of us are familiar with the ‘5 Why’s framework. Along similar lines, for data story-telling, the questions can span across multiple levels, a reasonable no. of steps being at least 5.

The key decision here is:

Whether the questions will be ‘So what’ i.e. deductive/ backward chaining based, e.g. the story aimed to explain an insight post-facto and deduce the learnings from it, or
the questions will be ‘What then’, i.e. the story extrapolating/ predicting a forward chaining-based future view/ simulation, based on the patterns and insights analyzed and presented in the visualization and analytics tools and the ML platforms (specifically for predictive models)

3- Build the Big Idea

As explained by Nancy Duarte in ‘Resonate’, the Big Idea is summarized into a key sentence, describing:

A unique viewpoint that’s the key highlight of the data story
Focusing on the key outcomes/ targets at stake
Explained in a syntactically valid narrative, in complete sentences and not just bulleted statistical observations and statements

For the previous example on COVID – planning for the third wave, the Big Idea covering the 4 key dimensions as stated above, can be expressed as:

Based on the analysis of the first and second waves of COVID in India, it has been seen that the mass vaccination drives are the most effective ways to prevent community spread of the contagion across all age groups, hence the vaccine production, distribution of cold chains, and execution infrastructure should be funded on an emergency basis, to prevent the third wave.

4- Storyboarding

Storyboarding focuses on first building a structural Strawman of the story. It’s like the flowchart we design, before developing the program/ algorithm and writing the code. It shows how the data story will flow, cascading the narrative from scenario to scenario, in a visual form or the targeted textual content structure.

5- The 3-minute story

This is a time-boxed technique so that the priority filters and key messages are articulated first, and also to reduce unnecessary noises and story ‘clutters’, e.g. without too many dashboards and data elements eventually proving similar insights and viewpoints.

Among these example data story-telling technique options (explained further in follow-through practice notes), the choice of the most apt story-telling technique will depend on:

the time and priorities of the targeted persona/ audience for the data story
complexity and availability accessibility and quality of the underlying data
what questions does the data story need to address as a top priority?