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Why Your B2B Thought Leadership Needs Data Storytelling 

Data is a core part of B2B thought leadership. But how can you tell a compelling data story when most people struggle with numbers?  

When we think of B2B thought leadership content, data is often at the heart of it. According to our research, primary research and generating statistical data to inform organizational or sector strategies are among the most trusted forms of thought leadership.  

However, it is no exaggeration to say that most people in the UK are not numerate. In research commissioned by National Numeracy, only 20% of the population answered maths questions to GCSE Grade 4 pass.  

Yet numbers are essential to sectors and organizations, shaping their understanding of success stories, progress, and where more action is needed. Most companies have data analytics. They use data dashboards, do research to leverage organizational insights, or create impactful, data-led thought leadership.  

However, unless data is communicated effectively, those insights will be lost or siloed to analytics teams and specialists.  

Data storytelling is vital to communicate data insights in a way that a broad and diverse audience can understand.  

What is data storytelling? 

Data storytelling is a way of drawing out the meaning of your data, offering context, explaining its significance, making it interesting to read, and increasing engagement. The same rules apply to data storytelling as copywriting – how can you tell a story that is simple, compelling, impactful, and  drives conversions

Like copywriting, your goals and audience shape the data story. An expert audience may not need a data story because they already understand the significance of numbers. Here, data visualization – the actual creation of visual charts – will be enough.  

For a non-expert audience, however, data storytelling is essential.  

There are various methods to convey your data stories, such as whitepapers and reports, slide decks, infographics, ‘scrollytelling’ and video. For each asset, an awareness of the limited time you have to grab the audience’s attention should shape how the data narrative is crafted.  

Dramatic data narratives 

Describing the data is not enough. Data also needs a dramatic storyline.  

One way of thinking about stories is environmental filmmaker Randy Olson’s ABT technique, which stands for ‘And-But-Therefore’ (ABT). ‘And’ connects a series of facts, but the ‘but’ creates narrative tension by creating a conflict or contradiction – it raises issues we need to be concerned about or what we don’t yet know. A challenge to be overcome. It steers us towards the ‘therefore’ – the consequence or resolution of that conflict. 

How does this translate into data storytelling?  

The financial software company Intuit conducted research into generational attitudes to finance. One blog focusing on GenZ looked at a series of insights gleaned from their research, such as: 

  • 66% of GenZ are only interested in their finances to support other interests. 
  • Two-thirds are unsure they’ll have enough money to retire.  

Those are the ‘ands’.  

And then come the ‘buts’: 

  • 63% have financial knowledge but don’t know how to use it.  
  • Two-thirds know it’s important to invest, but they don’t know how.  

The ‘therefore’ is the software products that Intuit offers: personalized financial services such as Credit Karma and consumer financial education.  

Keeping it simple 

A key mantra among data storytellers is avoiding ‘chart vomit’. Storytelling data allows content to focus on the most crucial data insights – what the audience needs to know to make meaning – rather than in-depth granular insights.  

As this example from our report, A Fairer Future: Equity and Inclusion in Professional Services, shows, one or two compelling statistics, portrayed through copy and visuals, are more meaningful to a lay audience than complex charts.  

Similarly, we need to understand that even if you keep numbers simple, many people find them worrying. Explaining numbers in words can be easier to access – one-fifth rather than 20%, for example, or words like majority/minority.  

The misinformation minefield 

Data tells a story, but how do you know that story is true? Statistics, shorn of their complexity and woven into a story, can tell wildly opposing truths.  

It is vital, therefore, that regardless of the need to select data to tell a story, it remains faithful to a more complex, worked-through data analysis and an understanding of context generated from secondary research.  

Think of an iceberg. The tip you see is the data storytelling, while the complex data analysis and secondary analysis lie beneath the surface.  

Integrating data storytelling 

We often assume that data storytelling takes place after data analysis, and to some degree that is right.  

But integrating data storytelling into your research from the beginning can make it more focused and efficient. You can spend more time determining why you are conducting the research, what questions will create a dramatic ABT narrative, and how to avoid casting too wide a net.  


Communication is key 

Data storytelling is a relatively new field. But it is vital for organizations and companies that want to make an impact with their insights.  

Without data storytelling, you risk wasting the time and effort you put into conducting the research.  

At a time when your data-driven thought leadership needs proven return on investment (ROI), data storytelling is the superpower that delivers the best results.  

Learn more about thought leadership storytelling in our ThinkWise Guide: The power of storytelling in thought leadership.  

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