Many businesses have set being strategically data-driven and getting to grips with their data as key organisational goals. The ability to effectively communicate any insights derived from this data is central to accomplishing these goals and facilitating change within the business.
This is where data storytelling can play a crucial role. In data storytelling, data, visuals and narrative combine to communicate complex information and insights in an engaging and actionable way.
As well as being a powerful skill to use internally, data storytelling has the power to bring your brand and what you do to life for your customers and create compelling narratives which can help to attract the attention of your audiences, build awareness and drive action. This piece will identify what makes data storytelling resonate, explore techniques to employ, and examine instances of impactful data storytelling in action.
The concept of data storytelling is fascinating. It relates to the ability to add creativity and flavour to a data scenario that connects qualitative analysis with a compelling narrative, making it come alive to the audience in the same way stories have been used to pass on ideas and folklore for many years.
Research shows the human brain is receptive to stories, with one Stanford Marketing professor suggesting that people remember stories up to 22 times more effectively than they remember facts alone. Research has further shown that when data and a story are used together, audiences are moved both intellectually and emotionally. The reason for this is that stories are known to increase cortical activity within the human brain and when you listen to a story, you contextualise it within your own experiences and ideas, which then creates a neural connection with the storyteller. This leads to individuals feeling more engaged with the story.
Companies have access to huge amounts of data to make sense of and turn into actionable insights. In Econsultancy's Future of Marketing Report, Paul Duxbury, Global Lead for the Repeatable Growth Model Programme at Haleon, described how he sees communication as the key to solving this:
"The biggest challenge is how you will present data in a meaningful way. Having got this sea of data and sea of measurement information, how do you then create compelling and useful insights and stories to communicate those insights that will actually motivate teams to do things differently?"
Data storytelling therefore becomes an essential skill to have, whether you are working in a technical, data, marketing or sales team to transform data and insights into persuasive narratives which will help to support data-driven decision making. To use data storytelling effectively also requires having a solid underlying data foundation where there is confidence in what is being presented.
Data, visualisation and narrative can combine to create engaging and, more importantly, actionable insights. Source: Econsultancy's Guide to Data Visualisation and Narrative.
There are a number of different storytelling techniques that can be considered. One I particularly like is based around the concept of a 'story mountain', since it is one that is very relatable, being crafted around the idea that every good story has a beginning, middle and end.
A key benefit of adopting the story mountain, as described by Cole Nussbaumer Knaflic of 'Storytelling With Data', is that it helps the presenter avoid jumping directly into the insight. Instead, this approach ensures there is build-up and context that takes an audience from the beginning of the data story to the middle. It is then that the insight can be presented, which forms the middle of the data story, along with a clear action or a resolution to take an audience to the end of the data story.
The story mountain structure works well since the human brain is also familiar with this approach, so by using it in data storytelling, it will resonate well with an audience.
To create the beginning, there should be context. Provide the framing around your story by starting with the "why." It is important to explain to your audience why the data matters to the company's specific business objectives or goals.
For example, if presenting sales data trends, align those trends with any targets or the impact on revenue, or changes in the market and whether it is growing, or compare performance against competitors. The focus should be on relevance and context, helping stakeholders understand why the data being shown is important.
Use visualisation to convert a dataset into something that allows meaning or insight to be easily derived from it and increases your audience's engagement with your data story. Effective visualisations help to distil complex data into easily digestible insights.
Consider who the audience is and what they need to know and use that to decide what format best suits the use case. Ensure charts and other visualisations only include what is necessary. The best way of displaying data to a CFO would not necessarily be the best way of displaying it to a salesperson, for example.
Aim for simplicity and consider what formats can help transform complex datasets into intuitive narratives that will guide your audience through the story and key insights. Having spent a number of years of my career with responsibility for web analytics, one of the biggest challenges I was often faced with was the need to help teams understand what the data and reports were showing so that they could consider what action to take as a result.
At a simplistic level, one approach I have used is an image of a well-known stadium such as Wembley to illustrate the scale of website visitors and how many times they would have filled the stadium.
A coherent narrative should be structured, and a compelling structure is vital to support the understanding of data and insights. In data storytelling, the data visualisations serve as key players in the narrative but the story weaves these visual elements into a compelling narrative to provide a fuller picture.
Following the story mountain structure of beginning, middle and end can help craft a story that follows a logical flow but also makes an emotional connection with stakeholders.
Another common structure that can also be applied to data storytelling and other situations such as writing and presenting ideas is the Minto Pyramid Principle. This is a communication framework and methodology developed by Barbara Minto, who is a former McKinsey & Company consultant and author of The Pyramid Principle: Logic in Writing and Thinking.
Which approach and structure for data storytelling is chosen can also be informed by the audience being presented to and the level of detail they will need.
Data storytelling can be a powerful external communication tool as well as an internal one. A consumer-facing business might use data storytelling to demonstrate its impact or brand purpose, as seen in the way that Airbnb weaves narratives into its press releases to illustrate its impact across the globe. Its 2024 article, 'How we're using data to make travel more open for all', for instance, presents data from Airbnb's 'Project Lighthouse' to demonstrate the company's progress in improving the equality of accommodation bookings in the US.
For B2B companies, using data storytelling and creating compelling narratives is often described as the best type of B2B marketing content, acting as a powerful way to communicate complex information to audiences and present solutions to their challenges and opportunities.
Agena is a leading business-to-business provider of parking and software solutions. The company's Chief Sales Officer, Deep Sen, shared in an article that Agena wanted to be a data-led organisation and use the power of data storytelling to drive business decisions and create client value.
Some key examples include the way in which the team has collated data around all vehicles entering a car park over a given period of time and built a compelling narrative using the data and other data points. Agena's team spent time understanding their clients' objectives such as improving motorist experience and maximising income, and developed a compelling narrative around occupancy to provide recommendations such as additional onsite presence to help motorists, changing shift patterns, and training schedules for staffing parking and incentivising parking during low occupancy periods.
Another example involves the emissions data they collate for all vehicles visiting their clients' car parks. Through effective storytelling, they describe how they have been able to analyse this data to help clients meet their sustainability objectives and identify actions around helping them reduce the carbon footprint of their car parks and support how they drive their EV charging strategy.
Whether you are a brand looking to engage a B2C or B2B audience, data storytelling can be a powerful way to build connections and demonstrate the expertise you have and how you can help potential customers and build loyalty with existing customers.
To use data storytelling effectively, individuals need to feel confident in their ability to analyse data and construct a compelling narrative supported with the appropriate visuals.
Building data storytelling skills through further training and development, as well as exploring how various data tools and platforms are used, can all help in supporting this process.
AI and machine learning tools also have the capacity to analyse millions of data points about customer behaviour, identify patterns, uncover trends, reveal hidden relationships, provide automated summaries of data, and create visuals. These can all support storytelling, but human intervention is still required to understand which patterns and trends are important and situate them in the context of the wider business.
Mastering your data storytelling skills will help you inspire and drive action from your insights to support data-driven decision-making as well as acting as an effective tactic to deliver persuasive messages to your customers. Next time you want to deliver that impactful message to drive action, you can follow these simple steps to help frame your findings using data storytelling:
