Data alone is not enough, storytelling matters - part 2
This article comprises the second half of a 2 part piece. Be sure to read part 1 before reading this article.
Three common mistakes in data storytelling
Of course, there are both opportunities and risks when using narratives and emotions to guide decision-making. Using a narrative to communicate important data and its context means listeners are one-step removed from the insights analytics provide.
These risks became realities in the public discourse surrounding the 2020 global COVID-19 pandemic. Even as scientists recommended isolation and social distancing to ´flatten the curve´ - low the spread of infection - fears of an economic recession grew rampant. Public figures often overlooked inconvenient medical data in favor of narratives that might reactivate economic activity, putting lives at risk.
Fortunately, some simple insights into human behavior can help prevent large-scale mistakes. Here are three common ways storytellers make mistakes when they employ a narrative, along with a simple use case to illustrate each example:
- 'Objective' thinking: In this case, the storyteller focuses on an organizational objective instead of the real story behind the data. This might also be called cognitive bias. It’s characterized by the storyteller approaching data with an existing assumption rather than a question. The analyst therefore runs the risk of picking data that appears to validate that assumption and overlooking data that does not.
Imagine a retailer who wants to beat its competitor’s customer service record. Business leaders task their customer experience professionals with proving this is the case. Resolute on meeting expectations, those analysts might omit certain data that doesn’t tip the results in favor of the desired outcome.
- 'Presentative' thinking: In this case, the storyteller focuses on the means by which he or she presents the findings - such as a data visualization method - at risk of misleading, omitting, or watering down the data. The storyteller may favor a visualization that is appealing to his or her audience at the expense of communicating real value and insights.
Consider an example from manufacturing. Imagine a storyteller preparing a narrative about productivity for an audience that prefers quantitative data visualization. That storyteller might show, accurately, that production and sales have increased but omit qualitative data analysis featuring important customer feedback.
- 'Narrative' thinking: In this case, the storyteller creates a narrative for the narrative’s sake, even when it does not align well with the data. This often occurs when internal attitudes have codified a specific narrative about, say, customer satisfaction or performance.
During the early days of testing for COVID-19, the ratio of critical cases to mild ones appeared high because not everyone infected had been tested. Despite the lack of data, this quickly solidified a specific media narrative about the lethality of the disease.
Business leaders must therefore focus on maximizing their 'insight-to-value conversion rate', as Forbes describes it, where data storytelling is both compelling enough to generate action and valuable enough for that action to yield positive business results. Much of this depends on business leaders providing storytellers with the right tools, but it also requires encouragement that sharing genuine and actionable insights is their top priority.
Ensuring storytelling success
“Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.”
- Stephen Few, Founder & Principal, Perceptual Edge®
So how can your practical data scientists succeed in their mission: driving positive decision-making with narratives that accurately reflect the story behind the data your analytics provide? Here are some key tips to relay to your experts:
- Involve stakeholders in the narrative’s creation. Storytellers must not operate in a vacuum. Ensure stakeholders understand and value the narrative before its official delivery.
- Ensure the narrative ties directly to analytics data. Remember, listeners are a step removed from the insights your storytellers access. Ensure all their observations and visualizations have their foundations in the data.
- Provide deep context with dynamic visualizations and content. Visualizations are building blocks for your narrative. With a firm foundation in your data, each visualization should contribute honestly and purposefully to the narrative itself.
- Deliver contextualized insights. 'Know your audience' is a key tenant in professional writing, and it’s equally valuable here. Ensure your storytellers understand how listeners will interpret certain insights and findings and be ready to clarify for those who might not understand.
- Guide team members to better decisions. Ensure your storytellers understand their core objective - to contribute honestly and purposefully to better decision-making among their audience members.
As citizen data science becomes more common, storytellers and their audience of decision-makers are often already on the same team. That’s why self-service capabilities, contextual dashboards, and access to optimized insights have never been so critical to empowering all levels of the organization.
Getting started: creating a culture of successful storytelling
Insights are only valuable when shared - and they’re only as good as your team’s ability to drive decisions with them in a positive way. It’s data storytellers who bridge the gap from pure analytics insights to the cognitive and emotional capacities that regularly guide decision-making among stakeholders. As you might have gleaned from our two COVID-19 scenarios, outcomes are better when real data, accurate storytelling, and our collective capacities are aligned.
But storytellers still need access to the right tools and contextual elements to bridge that gap successfully. Increasing business users’ access to powerful analytics tools is your first step towards data storytelling success. That means providing your teams with an analytics platform that adds meaning and value to business decisions, no matter their level in your organization.
If you haven´t read part 1 of this article yet, you can find it here.
Author: Omri Kohl
Source: Pyramid Analytics