Crafting meaningful narratives from data is a critical skill for all types of decision making—in business, and in our public discourse
As companies connect decision-makers with advanced analytics at all levels of their organizations, they need both professional and citizen data scientists who can extract value from that data and share. These experts help develop process-driven data workflows, ensuring employees can make predictive decisions and get the greatest possible value from their analytics technologies.
But understanding data and communicating its value to others are two different skill sets. Your team members’ ability to do the latter impacts the true value you get from your analytics investment. This can work for or against your long-term decision-making and will shape future business success.
There are well-established connections between stories and their ability to guide people’s decisions, even in professional settings. Sharing data in a way that adds value to decision-making processes still requires a human touch. This is true even when that data comes in the form of insights from advanced analytics.
That’s why data storytelling is such a necessary activity. Storytellers convert complex datasets into full and meaningful narratives, rich with visualizations that help guide all types of business decisions. This can happen at all levels of the organization with the right tools, skill sets, and workflows in place. This article highlights the importance of data storytelling in enterprise organizations and illustrates the value of the narrative in decision-making processes.
Data storytelling is an acquired skill. Employees who have mastered it can make sense out of a body of data and analytics insights, then convey their wisdom via narratives that make sense to other team members. This wisdom helps guide decision making in an honest, accurate, and valuable way.
As we described previously, reporting that provides deep, data-driven context beyond the static data views and visualizations is a structured part of a successful analytic lifecycle. There are three structural elements of data storytelling that contribute to its success:
In the best cases, storytellers can craft and automate engaging, dynamic narrative reports using the very same platform they use to prepare data models and conduct advanced analytics inquiries. Processes may be automated so that storytellers can prepare data models and conduct inquiries easily as they shape their narrative. But whether the storyteller has access to a legacy or modern business intelligence (BI) platform, it’s the storyteller and his or her capabilities that matter most.
“The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.”
— Hal R. Varian, Chief Economist, Google, 2009
The history of analytics has been shaped by technical experts, where companies prioritized data scientists who can identify and understand raw information and process insights themselves. But as business became more data-driven, the need for insights spread across the organization. Business success called for more nuanced approaches to analysis and required broader access to analytics capabilities.
Now, organizations more often lack the storytelling skill set—the ability to bridge the gap between analytics and business value. Successful storytellers embody this “bridge” as a result of their ability to close the gap between analytics and business decision-makers at all levels of the organization.
Today, a person doesn’t need to be a professional data scientist to master data storytelling. “Citizen data scientists” can master data storytelling in the context of their or their team’s decision-making roles. In fact, the best storytellers have functional roles that equip them with the right vocabulary to communicate with their peers. It’s this “last mile” skill that makes the difference between information and results.
Fortunately, leading BI platforms provide more self-service capabilities than ever, enabling even nontechnical users to access in-depth insights appropriate to their roles and skill levels. More than ever, employees across business functions can explore analytics data and hone their abilities in communicating its value to others. The question is whether or not you have an organization that can facilitate their development.
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”—slow 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:
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.
“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:
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.
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.
Pyramid Analytics differentiates itself from other enterprise BI platforms with its built-in data governance, allowing leaders to track the lineage of data sets and ensure their accuracy. Featuring robust security, collaborative dashboards, and controlled, multi-tenant access for all your business and technical users, Pyramid Analytics provides storytellers with everything they need to drive value-added decision-making. Contact us to learn more.