Each month the Scattered Clouds blog takes a look at the wonderful world of tourism through a data and evidence-led lens, all in pursuit of transforming tourism sector data into insight of course!

“I wanna tell you a story” - July 2025

Only readers of a certain age will know who it was that became synonymous with the saying I’ve adopted for the title of this month’s blog, but it’s a catchphrase that those communicating statistics and research ought always to have front of mind.

Conveying what it is that data is telling us requires a blend of both hard and soft skills. You definitely need to be at home beavering away in a spreadsheet where you marshal the data, make calculations, input formulae, format the numbers and undertake the analysis. These are the “hard skills” and can be thought of as science.

But just as important are the “soft skills”, or put another way, the “art” part of the process. This is where words and visuals matter so that the narrative borne out by the numbers is meaningful to those with whom you’re communicating.

Before getting carried away with the prospect of penning some flowery verbiage we mustn’t lose sight of the fact that it has to be built on solid data, and attentive analysis.

Data analysis can be descriptive in nature, answering the question “What happened?”, for example a trend chart showing monthly visits to an attraction. It can be diagnostic, answering the question “Why did it happen?”, for example finding a correlation between visitor admissions and ticket prices or the number of rainy days each month.

Or data analysis can be predictive, answering the question “What might happen in the future?” for example building a regression model using historic data to predict the impact of a proposed change in ticket prices.

And last but by no means least data analysis can be prescriptive, answering the question “What should we do next?”, for example rummaging around in survey findings to spot that visitor feedback suggests customer service in the café is below standard and thereby having evidence to underpin a recommendation for extra staff training.

A report or presentation that contains table after table of data with little or no commentary is unlikely to engage an audience. Hearing or reading a story is far better at evoking an emotional response and at creating a lasting memory than is a sheet of paper filled solely with numbers, decimal points and percentage signs.

Just as in any stereotypical story when we’re thinking about data and research it’s helpful to figure out the characters that will feature in the story, for example young and old visitors. We also need a context (or setting) for the story, which might be something like the variance in reported visitor satisfaction between our two “characters”. As with any good story there ought to be a spot of tension, and this can be thought of as the issue the data or research is shining a light on, maybe the analysis reveals that young visitors provide a far lower Net Promoter Score on departing the visitor attraction than do older visitors.

As we’ll recall from our schooldays all good stories should have a beginning, a middle and an end, and this is true when it comes to bringing data to life, even though there’s no guarantee that it will be a happy ending.

The ending may well focus on potential resolutions to the problem that the data is flagging up, for example unpicking whether younger visitors have different characteristics to older visitors, perhaps they are less likely to recommend the visitor attraction than older visitors because the latter group are more likely to be “members” who feel they’re getting better value for money than do the younger cohort.

A picture really can paint a thousand words, but thought is required regarding the type of picture. For charts we need to think about which variables are most impactful in telling our story and whether we can add layers (different variables in our dataset) to illustrate comparisons and trends.

Dangers abound when creating a chart, and it’s vital to avoid pitfalls such as using a scale that will exaggerate or disguise a trend and therefore mislead an audience. Consistent and intuitive use of colours for types of answer to survey questions can help, for example shading from red to green for “strongly disagree” to “strongly agree” makes “seeing” the story far easier than deploying a hotch-potch of colours.

Ordering the way that data is presented in a chart, for example answers to agree / disagree statements from a survey question, can help underpin a message, so ensuring that the statement agreed with most strongly is at the top and that which is agreed with least strongly at the bottom. It’s possible to be a tad clever in doing this by using a weighted score for each statement when ordering the results rather than just opting for the statement achieving the highest percentage of “strongly agree” at the top.

Some of the waymarkers we can deploy when creating our story include searching for correlation between the variables in our data, plotting the data over time to help reveal trends, benchmarking against alternative sources of data, looking for outliers that may signal an error in the data or that have a plausible explanation, in short, being really inquisitive when we’re digging around in the numbers.

Robust analysis and composing a compelling story go a long way to ensuring that decision-makers will be in as strong a position as possible to utilise statistics and research in the most effective way, but the storyteller matters as much as the story – it being exceptionally helpful if they’ve a good understanding of the underlying data, the messages that emerge from the analysis, and above all an enthusiasm for telling the story.

Oh, and if you’re too young to have known who had the catchphrase “I wanna tell you a story” and have not yet Googled it, the answer is Max Bygraves. .