Digital Storytelling: The Key to a Data-driven Business

In the era of digital finance, data storytelling has evolved beyond traditional narrative structures. As companies increasingly rely on data to drive decisions, the ability to create compelling stories from complex datasets is essential.

Yet, many finance organisations fall short by trying to fit old storytelling structures to new self-service tools. And this will only get harder. As teams upskill on AI and the explanation for data insights becomes more technically complex, good storytelling will be even more important to put the right information in front of leaders and drive retention.

So how do you effectively tell a story through a self-service dashboard when you’re not there to actually tell it? We know that when leaders don’t retain critical information, they act on impulse, leading to unsound decisions.

What’s wrong with current storytelling frameworks?

While traditional storytelling is linear, the inherent problem is that finance is trying to tell stories in a non-linear platform.

Dashboards don’t have a linear data structure; they have an entry-point or landing page that summarises the situation, which leads to a sub-level, where readers investigate the factors that contributed to the summary. From there, we delve into facts that intrigue or intersect with other points of understanding, and we arrive at our conclusion after plucking different, non-linear data points.

In other words, with digital storytelling, the story is more open-ended, with characteristics that more resemble a “choose-your-own-adventure” storyline than a traditional novel.

Elements of a good digital story

To implement the “choose your-own-adventure” approach, you first need to structure narrative clusters, which form the basics on which the story will centre. Each narrative cluster remains coherent as the reader explores it. Next, design narrative chains to guides the reader towards an intuitive conclusion.

Telling good stories with modern finance tools means structuring data relationships and designing data story plotlines that make sense to readers. Two new capabilities – graph analytics and decision intelligence – hold the key to what good management reporting or analytic storytelling looks like in a modern finance function.

Graph analytics, for those unfamiliar, is the study of relationships between objects (the people, places and things we measure with data). It is usually deployed to forecast behaviour and prescribe action for dynamic groups. Decision intelligence, on the other hand, is the multidisciplinary study of how humans make decisions, and what we can do to improve decision making.

They’re important because graph analytics reveals the relevant context – the narrative clusters of data points and the relationships between them. And decision intelligence is how we start to better understand the intuition of our readers – answering questions like: when they see a data point, what do they expect to see next?

If a finance team has a self-service tool implemented and is trying to become better versed in predictive analytics, focus on graph analytics, as it is the gateway to predictive analytics. If the team is a little more advanced, and has AI/ML tools, focus on decision intelligence.

Tell good data-driven stories by avoiding common pitfalls

Data volume and variety is on a new scale, creating data overload and informational clutter that can be hard to manage. A simple remedy is to use small watermarks that denote data quality in your dashboards.  A few red flags or call outs can go a long way in educating your business leaders on which data sets they should focus.

Use the self-service tool’s semantic reporting capabilities to help finance business partners avoid making unguided decisions leading to false conclusions. Nearly all modern BI tools have an admin report that identifies which users are accessing which datasets, as well as the calculations and definitions they are using.

In addition, “trap” insights are another common pitfall, which occur when finance delivers a purely financial explanation of a business problem without doing any real analysis. Regression analyses can mitigate this potentially slippery relationship between financial and nonfinancial data.

The last two pitfalls centre on old reporting structures and dashboards that are used incorrectly. In the first case, if engagement and adoption of dashboards tend to fall short of expectations, try rethinking or redesigning the orientation of the reporting suite to better match the audience. Consider the volume of dashboards and information on the dashboards and try not to overwhelm. Also be cognisant of outdated metrics crowding out the relevant ones over time.

Finally, for optimal dashboard use, consider storyboarding them and doing some A/B testing to see if it’s possible to use features to make the dashboard useful on its own. It could be in the form of an analysis that connects two plot points or a missing data set that needs to be considered and possibly included on decision maps.

The ways of working in the digital world are quickly changing thanks to self-service tools, remote work and AI. To keep pace with this evolution, financial analysts must identify and use the tools at their disposal to ensure that digital storytelling evolves as well.

About the Author:

Sid Sahoo is a Senior Director Analyst in the Gartner Finance practice, and will be presenting on digital storytelling change at the Gartner CFO & Finance Executive Conference in Sydney on 24-25 March 2025.